Dissertations / Theses on the topic 'Operations management'

This paper examines the process of Operations Acceptance Management, whose main task is to control Operations Acceptance Tests (OAT). In the first part the author focuses on the theoretical ground for the problem in the context of ITSM best practices framework ITIL. Benefits, process pitfalls and possibilities for automation are discussed in this part. The second part contains a case study of DHL IT Services (Prague), where a solution optimizing the overall workflow was implemented using simple web applications. The author of this paper was personally involved in the described project.

Abstract The framing into demand driven operations is because of the operations research modelling approach. The modelling approach requires continuous regressors and an independent response factor. The demand as an operating factor is considered as independent response factor in relation to the continuous regressors. The method validation is made along several longitudinal case studies to cover local, global and international industrial operations. The examined operational scope is from continuous operations to one-off production. Concerning scheduling, these examined demand driven, industrial operations are considered as open and dynamic, flow shop or job-shop operations. The examined managerial scope is from local work management to global industrial operations management. The theoretical framework of this study is based on operations management, productivity and controllability engineering. The strategical target is to improve productivity. The operational target setting is based on linear goal programming, streamlined demand driven material flow and specified operating factors according to this study, Forrester effect diagnostics and replenishment models. The engineering of strategical target into exact operational schedule as a task target is hard to accomplish, because of the combinatorial dynamic job-shop problem. The purpose of this study is to simplify this managerial task. These study operating factors are the heart in constructing a Decision Support System for the examined operations, alongside the method’s product flow diagnostics. This operations management method consists of the operating factors, specified in this study and these specified factors’ use in constructing a Decision Support System, by engineering current operations management system. The construct consist two parts. Firstly, the exact operational target alignment along this method diagnostics and secondly, the control mechanism according to this operational linear target. The expected managerial benefit is in productivity improvement. The practical benefits are in savings in logistics costs and improvement in customer service, due to shorten lead time and exacting delivery.

Advancing the state of disaster operations planning has significant implications given the devastating impress of disasters. Operations management techniques have in the past been shown to advance disaster-planning efforts; in particular, much progress can be noted in its application in the advancement of short-term recovery operations such as humanitarian logistics. However, limited emphasis has been placed on the long-term development scope of disaster operations. This dissertation argues the need for a fundamental shift in the motivation of archetypal disaster planning models, from disaster planning modeled around the emergency of the disaster event, to that of the sustainability of the community. Consequently, the purpose of this study is to address three key issues in regard to sustainability in disaster operations and planning. The first study of this dissertation (Chapter 3) focuses on describing disaster operations management and planning in its current state, examining features unique to sustainability in this context, and finally developing a planning framework that advances community sustainability in the face of disasters. This framework is applied in the succeeding quantitative studies (Chapter 4 and Chapter 5). The second study in this dissertation (Chapter 4) extends the sustainable planning framework offered in Chapter 3, using mathematical models. In particular, the modeling contributions include the consideration of multiple possible disaster events of single disaster type expected in a longer-term decision horizon, under integrated disaster management planning that is geared towards sustainability. These models are assessed using a mono-hazard scenario generator. A pedagogical example based on Portsmouth, Virginia, is offered. The last study in this dissertation (Chapter 5) extends the application of quantitative models to account for the 'multi-hazards' paradigm. While Chapter 4 considered multi-event analysis, the study was limited to a mono-hazard nature (the consideration of only one type of hazard source). This study extends analytical models from mono-hazard to multi-hazard, the consideration of a range of likely hazards for a given community. This analysis is made more complex because of the dependencies inherent in multiple hazards, projects, and assets. A pedagogical example based on Mombasa, Kenya, is offered.
Ph. D.

This dissertation addresses two topics in the domain of operations management. First we study a single utility’s optimal policies under the Renewable Portfolio Standard, which requires it to supply a certain percentage of its energy from renewable resources. The utility demonstrates its compliance by holding a sufficient amount of Renewable Energy Certificates (RECs) at the end of each year. The utility’s problem is formulated as a stochastic dynamic program. The problem of determining the optimal purchasing policies under stochastic demand is examined when two energy options, renewable or regular, are available, with different prices. Meanwhile, the utility can buy or sell RECs in any period before the end of the horizon in an outside REC market. Both the electricity prices and REC prices are stochastic. We find that the optimal trading policy in the REC market is a target interval policy. Sufficient conditions are obtained to show when it is optimal to purchase only one kind of renewable energy and regular energy, and others to show when it is optimal to purchase both of them. Explicit formulas are derived for the optimal purchasing quantities in each case. In the second essay, we examine the interaction between a buyer (Original Equipment Manufacturer, OEM) and his supplier during new product development. A “white box” relationship is assumed: the OEM designs the specification of the product and outsources the production to his supplier. The supplier may suggest potential specification problems. Our research is motivated by the fact that the supplier may detect potential specification problems, and one cannot take for granted that the supplier would inform the OEM. We solve an optimization problem from the perspective of the OEM. We first prove that it is strictly better for the OEM to design the contract so that the supplier will inform the OEM should she detect any flaws. Then we characterize the optimal solutions for the OEM. We also perform some sensitivity analysis at the end.
Business, Sauder School of
Graduate

This thesis describes the development of a Knowledge Management (KM) Assessment tool for the Operational level of the organisation. Its main focus is to help organisations to identify the KM activities and mechanisms that they could improve in order to improve their operational efficiency. Current KM literature is lacking in guiding organisations in what they need to do in order to implement and formalise KM in their operations with a view to improving operational efficiency. Therefore the aim of this thesis is to fill this gap in the literature and also to influence the manner in which KM is practiced. The research project has three distinct stages: the model development, modification and testing stages. The model development stage synthesises KM literature and a pilot study in order to develop a conceptual model of the KM assessment tool. The second stage of the research project describes the application of the tool in three organisations and details the modifications that were made as a result. Finally, the third stage tests the final version of the KM Assessment tool using four case organisations. The KM Assessment tool presented in this thesis is not a prescriptive KM solution; it emphasises the need to approach KM from a process and task specific perspective. Put another way, KM improvements should be implemented to reflect the processes and task charactaristics of each individual organisation. However, the thesis presents a method of evaluation of such that is unform across organisational types

There are three topics in operations management presented in this dissertation. Each topic deals with a specific issue encountered by managers from various organizations. In the context of non-profit operations, we study a two-customer sequential resource allocation problem whose objective function has a max-min form. For finite discrete demand distribution, we give a sufficient and necessary condition under which the optimal solution has monotonicity property. However, this property never holds with unbounded discrete distributions. Then, we look at a service system with two servers serving arriving single class jobs. Servers care about fairness, and they can endogenously choose capacities in response to the routing policy. We focus on four commonly seen policies and examine the two-server game where the servers' objective functions have a term that reflects fairness. Theoretical results concerning the existence and uniqueness of the Nash equilibrium are proved for some policies. Numerical studies also provide insights on servers' off-equilibrium behaviours and the system efficiency under different policies. Finally, suppose that a firm has heterogeneous servers who provide service with different quality levels, and that there exists a learning curve of the servers so that the quality can be improved by accumulating experience in serving customers. As customers decide their service procurement based on the quality and system congestion, what pricing scheme should the firm adopt to achieve optimal revenue in the long run? We compare a traditional pricing scheme with a proposed one, and theoretically establish the superiority of the proposed pricing scheme. Based on both theoretical and numerical evidence, we characterize the sensitivity of some parameters with respect to the comparison.
Business, Sauder School of
Graduate

This thesis comprises three independent essays in operations management. The first essay explores a specific issue encountered by mobile gaming companies. The remaining two essays address the contracting problem in a supply chain setting. In the first essay, we study the phenomena of game companies offering to pay users in "virtual" benefits to take actions in-game that earn the game company revenue from third parties. Examples of such "incentivized actions" include paying users in "gold coins" to watch video advertising and speeding in-game progression in exchange for filling out a survey etc. We develop a dynamic optimization model that looks at the costs and benefits of offering incentivized actions to users as they progress in their engagement with the game. We find sufficient conditions for the optimality of a threshold strategy of offering incentivized actions to low-engagement users and then removing incentivized action to encourage real-money purchases once a player is sufficiently engaged. Our model also provides insights into what types of games can most benefit from offering incentivized actions. In the second essay, we propose what we call a generalized price-only contract, which is a dynamic generalization of the simple wholesale price-only contract. We derive some interesting properties of this contract and relate them to well-known issues such as double marginalization, relative power in a supply chain due to Stackelberg leadership, contract structure and commitment issues. In the third essay, we consider a supplier selling to a retailer with private inventory information over multiple periods. We focus on dynamic short-term contracts, where contracting takes place in every period. At the beginning of each period, with inventory or backlog kept privately by the retailer, the supplier offers a one-period contract and the retailer decides his order quantity in anticipation of uncertain customer demand. We cast the problem as a dynamic adverse-selection problem with Markovian dynamics. We show that the optimal short-term contract has a threshold structure, with possibly multiple thresholds. In certain cost regimes, the optimal contract entails a base-stock policy yet induces partial participation.
Business, Sauder School of
Operations and Logistics (OPLOG), Division of
Graduate

This dissertation addresses three topics in the domain of operations management. First we study the problem of profit allocation in a supply chain using a bargaining approach. We present a novel framework for the analysis of this problem. The application of our framework results in a prescription for the required profit allocations. We prove that in a setting where all supply chain agents can communicate, possibly coordinating their actions, the allocation prescribed by our bargaining framework coincides with the Shapley value of a cooperative game associated with the setting. Next, we study revenue management in the presence of strategic consumers, who face some uncertainty regarding the product valuation. We show, contradictory to the main stream of the literature regarding strategic consumers, that under certain circumstances, the retailer may prefer facing strategic consumers rather than myopic ones. Finally, we study the issue of cross-dock operations management at a shift-level. We target the main gap identified in the literature for this issue, and present a holistic framework for the allocation of cross-dock resources to processing of containers and freight. We show, using simulated data that our approach outperforms current practices.

In this dissertation, I discuss three problems within service operations management: identifying situational attributes that lead to positive customer outcomes under a Twitter-based customer service framework; the conditions for finite delay of first-in-first-out multiserver systems when confronted with integral loads; and the relative performance of different bargaining mechanisms for a seller of finite perishable inventory, with a further investigation of the consequences of modeling private information. First, we consider a large telecommunications company that provides customer support over Twitter. Using 10 months of service data, we apply model selection techniques to develop an ordinal logistic regression model assessing the probability that a given customer service interaction will result in a positive, neutral or negative resolution as determined by the customer’s sentiment expression. Our model incorporates customer, service and network explanatory attributes. We find that customers are less likely to experience a positive final sentiment as time passes, that is, those cases later in the 10 month period studied are less likely to experience positive resolution. This suggests that there is a drop-off in the likelihood of more positive resolution, but that this effect levels off. This finding may indicate a shift by the customer service team to harder to resolve cases as the program matures. Next, we consider conditions for finite expected delay in FIFO multiserver queues with integral loads. Scheller-Wolf and Vesilo (2006) find necessary and sufficient conditions for a finite rth moment of expected delay in a FIFO multiserver queue, assuming a non-integral load and a service time distribution belonging to class L1B . Removing the non-integral load assumption results in a gap between the identified necessary and sufficient conditions, as discussed by Foss (2009). We decrease the size of this gap through the application of domain of attraction results. Specifically, we find a stricter necessary condition for a GI/GI/K-server system with integral p that is more restrictive than those in the literature. Finally, we consider the problem of a seller with a finite supply of perishable inventory. We consider four price setting mechanisms: seller posted price, buyer posted price, split-the-difference, and the neutral bargaining solution. We rank the value of these different mechanisms analytically and numerically in the context of the symmetric uniform trading problem from the perspective of the seller. While the ordering of the mechanisms remains the same as compared to the infinite horizon case studied in the literature, we use a model analogous to the infinite horizon case to find numerically that the relative value of the split-the-difference mechanism increases when the seller ultimately faces a dead- line to complete the sales. The split-the-difference mechanism becomes more valuable as the ratio of available inventory to time remaining increases because it is more likely to result in a sale than the seller posted price mechanism. In general, modeling private information is more challenging for the split-the-difference and neutral bargaining solution mechanisms than for the two posted price mechanisms. To assess the importance of this added complication, we quantify the effect of modeling private information when computing the seller’s opportunity cost and find that while private information makes only a small difference in the neutral bargaining solution case, this modeling choice makes a large difference in the split-the-difference case when the seller is weak.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2014.
Cataloged from PDF version of thesis.
Includes bibliographical references.
The thesis applies optimization theory to three problems in operations management. In the first part of the thesis, we investigate the impact of inventory control on the availability of drugs to patients at public health facilities in Zambia. We present consistent empirical data and simulation results showing that, because of its failure to properly anticipate seasonal variations in demand and supply lead-times, this system leads to predictable patient-level stock-outs even when there is ample inventory available in the central warehouse. Secondly, we propose an alternative inventory control system relying on mobile devices and mathematical optimization, and present results from a validated simulation model suggesting that its implementation would lead to a substantial improvement of patient access to drugs relative to the current system. In the second part of the thesis, we investigate the impact of returning customers on pricing for fashion Internet retailers. Our analysis of clickstream data from an online fashion retailer shows that a significant proportion of sales is due to returning customers, i.e. customers who first visit an item at a particular price, but purchase the item in a later visit at a lower price. We propose a markdown pricing model that explicitly incorporates returning customers. We propose a model for quantifying the value of the returning pricing model relative to a pricing model that does not distinguish between first-time and returning customers, and determine the value of returning pricing both exactly and through developing bounds. Based on real data from a fashion Internet retailer, we estimate the parameters of the returning demand model and determine the value of the returning pricing model. Lastly, we study the promotion optimization problem faced by grocery retailers, i.e. deciding which items to promote and at what price. Our formulation includes several business rules that arise in practice. We build demand models from data in order to capture the stockpiling behavior through dependence on past prices. This gives rise to a hard problem. For general additive and multiplicative demand structures, we propose efficient LP based methods, show theoretical performance guarantees and validate our results using real data.
by Ngai-Hang Zachary Leung.
Ph. D.

Thesis: Ph. D. in Policy, Operations, and Management, Massachusetts Institute of Technology, Department of Urban Studies and Planning, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references.
This dissertation strengthens planning and policy analysis by using concepts from operations management to examine production and distribution of goods and services for disadvantaged groups. Building on the introduction, chapter two tells a cautionary tale, investigating how scholars and decision makers used operations management methods to consider operations in planning and policy analysis in the 1970s in ways that further marginalized already vulnerable residents. The tools and concepts of operations management, however, if sufficiently framed by concerns about equity and advocacy, are powerful instruments in solving production and distribution problems with social consequences. Chapter three explores how these concepts can be used to descriptively identify disparities in access to goods and services by socio-economic status, examining the distribution of irrigation equipment in Senegal. The core question is about the allocation of risk and inventory across levels of a supply chain that extends far into Senegal's farming regions. Chapter four identifies how these concepts can be used to causally explain disparities, tracing policies and plans that aggregative or ameliorate them. It focuses on the main program that subsidizes affordable housing construction in the United States, a durable necessity that is unevenly available and exposed to environment risks across space. The core question is about patterns over space and time in building affordable housing stocks, relative to where and when disasters occur. Chapter five shows how these concepts can be used to prescriptively remedy disparities. It investigates quality risks in the US international food assistance supply chain in Eastern Africa. The core question is about what levers can be pulled in supply chain design to improve food aid quality. Chapter six concludes.
by Mark Emmanuel Brennan.
Ph. D. in Policy, Operations, and Management
Ph.D.inPolicy,Operations,andManagement Massachusetts Institute of Technology, Department of Urban Studies and Planning

Molecular management targets the right molecules to be at the right place, at the right time and at the right price. It consists of molecular characterisation of refining streams, molecular modelling and optimisation of refining processes, as well as overall refinery optimisation integrating material processing system and utility system on the molecular level. The need to increase modelling details to a molecular level is not just a result of political regulations, which force refiners to managing the molecule properly, but also seems to be a very promising to increase the refining margin. In this work, four aspects of molecular management are investigated respectively. Molecular Type Homologous Series (MTHS) matrix framework is enhanced on both representation construction and transformation methodology. To improve the accuracy and adequacy of the representation model, different strategies are formulated separately to consider isomers for light and middle distillates. By introducing statistical distribution, which takes the composition distribution of molecules into account, the transformation approach is revolutionised to increase the usability, and tackle the challenge of possibly achieving significantly different compositions from the same bulk properties by the existing approaches. The methodology is also enhanced by applying extensive bulk properties. Case studies demonstrate the effectiveness and accuracy of the methodology. Based on the proposed characterisation method, refining processes are modelled on a molecular level, and then process level optimisation is preformed to have an insight view of economic performance. Three different processes, including gasoline blending, catalytic reforming, and diesel hydrotreating, are investigated respectively. Regarding gasoline blending, the property prediction of blending components, and the blending nonlinearity are discussed. To tightly control on the property giveaway, a molecular model of gasoline blending is developed, and then integrated into the recipe optimisation. As for the conversion processes, catalytic reforming and diesel hydrotreating, reactions and reactors are modelled separately, and then followed by the consideration of catalyst deactivation. A homogeneous rigorous molecular model of a semiregenerative catalytic reforming process, considering pressure drop, has been developed. In addition, a multi-period process optimisation model has been formulated. Regarding diesel hydrotreating, a molecular model of reactions with a three-phase trickle-bed reactor has been developed. The concept of reaction family is successfully applied. A structural contribution approach is used to obtain kinetics and adsorption parameters. A series of procedures are developed to solve the complex problem. Thereafter, a process optimisation model has been developed with the consideration of catalyst deactivation, with a new strategy on the division of catalyst life. Finally, a two-level decomposition optimisation method is extended to incorporate molecular modelling into the overall refinery optimisation, and then applied in two aspects. Firstly, with the integration of the process and the site-level models, a better perspective is obtained with regard to a material processing system. By molecular modelling of refining streams and processes, the integrated approach not only controls the molecules in products properly, but also increases the overall performance. In the second application, a framework integrating a hydrogen network with hydroprocesses is developed to target the maximum profit, rather than saving hydrogen. It allocates hydrogen on the hydrogen network level and utilise hydrogen efficiently on the process level by optimising operating conditions. Consequently, the extent of achieving the maximum profit could be fully exploited with optimal hydrogen utilisation.

SHAVER, KAY ALBRIGHT. Activity-based Evaluation of Operations Management within Service Operations Organization. (Under the direction of John Dutton.) The purpose of this study is to use historical cross-sectional data including order characteristics to predict the time requirements of the indirect activity of managing. The subject of the study is the Operations Manager, who manages the supervision of engineering and installation of orders. Predictions of time estimates for the Operations Manager will provide information for staffing and workforce planning of the indirect activities required to manage the forecasted order workload. The research includes a pilot survey of Operations Managers in three regions and a final empirical study, which includes the entire Service Organization?s Operations Manager population. Using regression analysis, the study evaluates the factors noted in the pilot survey as important to the Operations Managers. Consideration is given to order characteristics, such as size, customer relationships, schedule changes, interval, Operations Manager assigned. Consideration is also given to general characteristics, such as seasonal effects, concurrent orders, experienced installers available, and inventory levels. The analysis reveals that category of work, size of the order as measured by number of frames, seasonal impacts, the Operations Manager assigned, customer relationships, and the effort required to underspend the budget are key predictors of the time required to manage the supervision of the engineering and installation of orders. The results indicate interval, inventory, schedule changes and experienced installers available are not significant indicators of this indirect order activity.

This thesis contributes to the area of integrated management of outbound and inbound passenger flows at an international airport terminal for optimal utilisation of resources with maximum comfort to the passengers. The main goal of this research is to develop a holistic model based on the combination of simulation, airport resource management algorithms and analytical optimisation approaches. This model provides an important step forward in the development of a fully-fledged holistic decision support tool. The model can be used for strategic and operational requirements for multi-terminal International/Domestic.

Thesis (M.S. in Information Systems & Operations) Naval Postgraduate School, June 2001.
Thesis advisors, John Arquilla, Steven J. Iatrou. Includes bibliographical references (p. 97-100). Also available online.

In the past two decades, retailers have witnessed rapid changes in markets due to an increase in competition, the rise of e-commerce, and ever-changing consumer behavior. As a result, retailers have become increasingly aware of the need to better coordinate inventory control with pricing in order to maximize their profitability. This dissertation was motivated by two of such problems facing retailers at the interface between pricing and inventory control. One considers inventory control decisions for settings in which planned prices fluctuate over time, and the other considers pricing of multiple substitutable products for settings in which customers hold inventory as a consequence of stockpiling when promotional prices are offered.

In Chapter 1, we provide a brief motivation for each problem. In Chapter 2, we consider optimization of procurement and inventory allocation decisions by a retailer that sells a product with a long production lead time and a short selling season. The retailer orders most products months before the selling season, and places only one order for each product due to short product life cycles and long delivery lead times. Goods are initially stored at the warehouse and then sent to stores over the course of the season. The stores are in high-rent locations, necessitating efficient use of space, so there is no backroom space and it is uneconomical to send goods back to the warehouse; thus, all inventory at each store is available for sale. Due to marketing and logistics considerations, the planned trajectory of prices is determined in advance and may be non-monotonic. Demand is stochastic and price-dependent, and independent across time periods. We begin our analysis with the case of a single store. We first formulate the inventory allocation problem given a fixed initial order quantity with the objective of maximizing expected profit as a dynamic program and explain both technical and computational challenges in identifying the optimal policy. We then present two variants of a heuristic based on the notion of equalizing the marginal value of inventory across the time periods. Results from a numerical study indicate that the more sophisticated variant of the heuristic performs well when compared with both an upper bound and an industry benchmark, and even the simpler variant performs fairly well for realistic settings. We then generalize our approaches to the case of multiple stores, where we allow the stores to have different price trajectories. Our numerical results suggest that the performance of both heuristics is still robust in the multiple store setting, and does not suffer from the same performance deterioration observed for the industry benchmark as the number of stores increases or as price differences increase across stores and time periods. For the pre-season procurement problem, we develop a heuristic based on a generalization of the newsvendor problem that accounts for the two-tiered salvage values in our setting, specifically, a low price during end-of-season markdown periods and a very low or zero salvage value after the season has concluded. Results for numerical examples indicate that our modified newsvendor heuristic provides solutions that are as good as those obtained via grid search.

In Chapter 3, we address a retailer's problem of setting prices, including promotional prices, over a multi-period horizon for multiple substitutable products in the same product category. We consider the problem in a setting in which customers anticipate the retailer's pricing strategy and the retailer anticipates the customers' purchasing decisions. We formulate the problem as a two-stage game in which the profit maximizing retailer chooses prices and the utility maximizing customers respond by making explicit decisions regarding purchasing and consumption, and thus also implicit decisions regarding stockpiling. We incorporate a fairly general reference price formation process that allows for cross-product effects of prices on reference prices. We initially focus on a single customer segment. The representative customer's utility function accounts for the value of consumption of the products, psychological benefit (for deal-seekers) from purchasing at a price below his/her reference price but with diminishing marginal returns, costs of purchases, penalties for both shortages and holding inventory, and disutility for deviating from a consumption target in each period (where applicable). We are the first to develop a model that simultaneously accounts for this combination of realistic factors for the customer, and we also separate the customer's purchasing and consumption decisions. We develop a methodology for solving the customer's problem for arbitrary price trajectories based on a linear quadratic control formulation of an approximation of the customer's utility maximization problem. We derive analytical representations for the customer's optimal decisions as simple linear functions of prices, reference prices, inventory levels (as state variables), and the cumulative aggregate consumption level (as a state variable). (Abstract shortened by ProQuest.)

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2003.
Includes bibliographical references (p. 83-86).
During 2002, some 11,000 people throughout the world were killed in natural catastrophes and man-made disasters were responsible for 10,000 fatalities worldwide; flood claimed the most victims with more than a third of the fatalities caused by natural disasters. Indeed, people will always face natural disasters, but it seems that disasters nowadays are frequently generated by or aggravated by human activities. The poverty as well as the increase of the density of the population is making the world more and more vulnerable since more people are living in riskier situations. The number of people at risk is growing every year and most of this population is located in developing countries where resources are limited. The purpose of this study is to identify the different types of risk and risk management in order to increase the participation of the private sector in disaster relief operations. This could generate the incentive for a collaborative work in an effective and efficient manner despite the number of agencies involved in disaster relief and fund raising in the corporate world. After providing an overview of the risk management concepts, this thesis will focus on assessing risks and ways to mitigate them before presenting risk transfer. Finally, there will be an emphasis on the importance and the role of Information Technology in Disaster Risk Management activities.
by Nathalie Assens.
S.M.

Expert Services like consulting, legal advisory and software design play a significant and growing role in the developed economies. In operations management (OM), the term “Professional Services” is frequently used to refer to these offerings. The literature on Expert Services proposes that these services are different from other types of services and require a different OM approach. However, recent empirical research calls into question some of the OM assumptions about Expert Services and their delivery processes. Some empirical and theoretical studies also suggest that individuals’ expertise is fundamental to making these delivery processes more efficient and effective. For these reasons, operations management researchers are calling for more robust models to explain the nature of Expert Services. This thesis therefore focuses on understanding Expert Services delivery processes and explores in detail the role individuals’ expertise plays in them. To reach its goals, this PhD by publication uses evidence from three Expert Service providers to compose three papers that contribute towards a better understanding of these service delivery processes. The three papers deal, respectively, with the characteristics of Expert Services delivery processes, with the nature and implications of customer involvement in the delivery process and with the development of new expert services. Combined, the insights from the three papers draw attention to the managerial implications of having expertise as a key productive resource. The outcomes of the papers also create means to refine and revisit OM concepts in relation to how Expert Services are developed and delivered. This research therefore contributes to the OM knowledge of Expert Services, addressing some of the recent calls for research in the area. This thesis also sets out an agenda for future research that can further increase our understanding of these offerings and create means to improve their delivery processes.

The aim of operations management in health care is to enhance the provision of services to patients and to decrease costs. Overall worldwide health care expenditures represent around 10.5% of the global GDP and are projected to increase at an annual rate of 5.3% from 2015 to 2017 [74]. In order to investigate how to curb health care costs, I study the English NHS, a health care system that provided universal care to around 54 million people in 2014 [243]. The NHS has launched many initiatives to improve the performance of hospital operations such as the "QIPP" program, which has the objective to save £20 billion of costs by 2015 [98]. Given this framework, this research aims to contribute to the theory that is guiding these operational changes, using data on all admissions to hospitals and focussing on the inguinal hernia, one of the most common surgical procedures [86]. In the next chapters, this research describes inguinal hernia care delivery in the English NHS, examines the impact of spillovers and complementarities on costs, and investigates the effects of length of stay reduction on risk of re-admission and risk of death. The findings of this thesis indicate that one of the possible problems in the delivery of inguinal hernia care in the NHS is the decrease in the number of elective operations performed and the increase in readmission rates. They also clarify how decisions on allocation of resources can affect hospital expenditures by showing that loss in focus can increase health care costs and by pointing out that there is little evidence to support the theory of spillovers and complementarities in the surgical context. Finally, the results of this research can be used to suggest the logic of a policy to decrease length of stay that can inform hospital decisions and can decrease hospital costs.

The environmental uncertainty of federal politics and acquisition outsourcing in competitive markets requires an adaptive decision-analysis structure. Practitioners oriented toward exclusively static methods face severe challenges in understanding qualitative aspects of organizational governance. The purpose of this grounded theory study was to examine and understand behavioral relationship attributes within intuitive, choice, judgment, or preference decision-making processes. The problem addressed in this study was the detrimental effects of organizational citizenship behavior (OCB), compulsory citizenship behavior (CCB), and social exchange theory (SET) on the acquisition management relationship The OCB, CCB, SET dictates that sound business development, relationship acumen, emotional intelligence and perceptiveness transcend pure numerical quantification. Exhibition of relationship-based attributes influence and drive long-term contractual relationships and the sustainability of business organizations. The data collected included historical data and survey responses. Approximately 34,000 acquisition professionals comprised the population-sampling frame. The study sample consisted of 378 survey responses that yielded 294 qualifying respondents with 94 disqualifications that produced a 78% response rate. The Carnegie-Mellon behavioral survey guidelines underpinned questionnaire construction and affirmation of themes. Strauss and Corbin grounded theory and theme generation addressed behavioral decision making under the additive model that inform the development of an organizational social operations and business framework that accounts for intuitive judgment. The study may contribute to positive social change by orienting managers toward behavioral decision making, ensuring responsiveness to the public and federal governance

Thesis (Ph. D.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2013.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 105-111).
In this thesis, we present a system to make personalized lifestyle and health decisions for diabetes management, as well as for general health and diet management. In particular, we address the following components of the system: (a) eciently learning preferences through a dynamic questionnaire that accounts for human behavior; (b) modeling blood glucose behavior and updating these models to match individual measurements; and (c) using the learned preferences and blood glucose models to generate an overall diet and exercise plan using mixed-integer robust optimization. In the first part, we propose a method to address (a) above, using integer and robust optimization. Despite the importance of personalization for successful lifestyle modification, current systems for diabetes and dieting do not attempt to use individual preferences to make suggestions. We present a general approach to learning preferences, that includes an efficient and dynamic questionnaire that accounts for response errors, and robust optimization models using risk measures to account for the commonly seen human behavior of loss aversion. We then address part (b) of our system, by first modeling blood glucose behavior as a function of food consumed and exercise performed. We rely on known attributes of dierent foods as well as individual data to build these models. We also show how we use optimization to dynamically update the parameters of the model using new data as it becomes available. In the third part of this thesis, we address (c) by using mixed-integer optimization to nd an optimal meal and exercise plan for the user that minimizes blood glucose levels while maximizing preferences. We then present a robust counterpart to the formulation, that minimizes blood glucose levels subject to uncertainty in the blood glucose models. We have implemented our system as an online application, and conclude by showing a demonstration of the overall program.
by Allison Kelly O'Hair.
Ph.D.

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2002.
Includes bibliographical references (p. 59-60).
We develop two classes of optimization models in order to maximize revenue in a restaurant, while controlling average waiting time as well as perceived fairness, that may violate the first-come-first-serve (FCFS) rule. In the first class of models, we use integer programming, stochastic programming and approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation of a restaurant that does not accept reservations. In a computational study with simulated data, we show that optimization based methods enhance revenle relative to the industry practice of FCFS by 0.11% to 2.22% for low load factors, by 0.16% to 2.96% for medium load factors, and by 7.65% to 13.13% for high load factors, without increasing and occasionally decreasing waiting times compared to FCFS. The second class of models addresses reservations. We propose a two step procedure: use a stochastic gradient algorithm to decide a priori how many reservations to accept for a future time and then use approximate dynamic programming methods to decide dynamically when, if at all, to seat an incoming party during the day of operation. In a computational study involving real data from an Atlanta restaurant, the reservation model improves revenue relative to FCFS by 3.5% for low load factors and 7.3% for high load factors.
by Romy Shioda.
S.M.

This thesis examines the application of operations management (OM) theory to the management of NHS hospital inpatient bed resources (also known as bed management). The study was initiated by management's desire for improvement in bed management at North Management General Hospital (NMGH), a medium-sized acute hospital which is now part of the Pennine Acute Hospitals NBS Trust (the Trust) in the North West of England. The study made use of published research into the practical application of OM theory in various organisations - particularly the still small number of published studies conducted in the health sector - in order to analyse data collected relating to bed management at NMGH over two periods: April 2002 through to April 2004, and also for the month of May 2005. For the data collection, the study deployed various qualitative and quantitative techniques including, process mapping, interviews, observation and statistical analysis. The purpose of the second, shorter, field-study in May 2005 was to measure the outcomes arising from recommendations made to the Trust following the analysis of data collected between April 2002 and April 2004. This allowed for the re-appraisal of the original recommendations to be incorporated into the results and conclusions of this study. This thesis argues that the benefits of applying OM theory in an English NHS hospital are demonstrated by measurable improvements in the management of throughput and queues when OM theory is used in designing layouts and in augmenting capacity coping systems. However, currently, certain cultural and political conditions within the NHS effectively constrain the usefulness of OM approaches. The thesis argues that cultural and political contingencies in OM theory require further consideration if the benefits of OM theory are to be fully exploited in the English NHS hospital environment. 9

The main content of this master´s thesis is Airport Operations Management and it´s application on regional airport Brno – Tuřany. The study analyses common airport operations at the airports and compares it with actual situation at Brno airport. Focusing on comparsion, new upgrading or suggestions for increasing the operations are made. The part of thesis encloses also an approach to contemporary issues in greening the airports and air traffic.

This dissertation studies three topics in transport economics and operations management. The first topic is on the economic regulation of congested airports. The second one is revenue sharing between airlines and airports. In the third topic, we investigate the impact of strategic customer behavior on the channel profits. Chapter 2 studies the effects of concession revenue sharing between an airport and its airlines. It is found that the degree of revenue sharing will be affected by how airlines' services are related to each other (complements, independent, or substitutes). It is further found that airport competition results in a higher degree of revenue sharing than in the case of single airports. The airport-airline chains may nevertheless derive lower profits through the revenue-sharing rivalry, and the situation is similar to a Prisoners' Dilemma. Chapter 3 considers price-cap regulation of an airport where the airport facility (e.g., its runway) is congested and air carriers have market power. In the case of airports, there are two versions of price-cap regulation: the single-till approach and the dual-till approach. We show that when airport congestion is not a major problem, single-till price-cap regulation dominates dual-till price-cap regulation with respect to social welfare. Furthermore, we identify situations where dual-till regulation performs better than single-till regulation when there is significant airport congestion. Chapter 4 investigates the impact of customer and firm discounting as well as downstream retailer competition on the benefit of decentralization when customers are strategic. We consider a dynamic two-period model consisting of one manufacturer who sells a product through multiple retailers under linear wholesale price contracts. No firm can credibly commit to future prices or quantities. With strategic customers, we find that a decentralized channel may have higher profit than that of a centralized channel. We show that in addition to the double marginalization effect, both customer and firm discounting and retailer competition are also driving factors of the higher decentralized channel profit.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2011.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 257-263).
With the recent explosion of choices available to us in every walk of our life, capturing the choice behavior exhibited by individuals has become increasingly important to many businesses. At the core, capturing choice behavior boils down to being able to predict the probability of choosing a particular alternative from an offer set, given historical choice data about an individual or a group of "similar" individuals. For such predictions, one uses what is called a choice model, which models each choice occasion as follows: given an offer set, a preference list over alternatives is sampled according to a certain distribution, and the individual chooses the most preferred alternative according to the sampled preference list. Most existing literature, which dates back to at least the 1920s, considers parametric approaches to choice modeling. The goal of this thesis is to deviate from the existing approaches to propose a nonparametric approach to modeling choice. Apart from the usual advantages, the primary strength of a nonparametric model is its ability to scale with the data - certainly crucial to applications of our interest where choice behavior is highly dynamic. Given this, the main contribution of the thesis is to operationalize the nonparametric approach and demonstrate its success in several important applications. Specifically, we consider two broad setups: (1) solving decision problems using choice models, and (2) learning the choice models. In both setups, data available corresponds to marginal information about the underlying distribution over rankings. So the problems essentially boil down to designing the 'right' criterion to pick a model from one of the (several) distributions that are consistent with the available marginal information. First, we consider a central decision problem in operations management (OM): find an assortment of products that maximizes the revenues subject to a capacity constraint on the size of the assortment. Solving this problem requires two components: (a) predicting revenues for assortments and (b) searching over all subsets of a certain size for the optimal assortment. In order to predict revenues for an assortment, of all models consistent with the data, we use the choice model that results in the 'worst-case' revenue. We derive theoretical guarantees for the predictions, and show that the accuracy of predictions is good for the cases when the choice data comes from several different parametric models. Finally, by applying our approach to real-world sales transaction data from a major US automaker, we demonstrate an improvement in accuracy of around 20% over state-of-the-art parametric approaches. Once we have revenue predictions, we consider the problem of finding the optimal assortment. It has been shown that this problem is provably hard for most of the important families of parametric of choice models, except the multinomial logit (MNL) model. In addition, most of the approximation schemes proposed in the literature are tailored to a specific parametric structure. We deviate from this and propose a general algorithm to find the optimal assortment assuming access to only a subroutine that gives revenue predictions; this means that the algorithm can be applied with any choice model. We prove that when the underlying choice model is the MNL model, our algorithm can find the optimal assortment efficiently. Next, we consider the problem of learning the underlying distribution from the given marginal information. For that, of all the models consistent with the data, we propose to select the sparsest or simplest model, where we measure sparsity as the support size of the distribution. Finding the sparsest distribution is hard in general, so we restrict our search to what we call the 'signature family' to obtain an algorithm that is computationally efficient compared to the brute-force approach. We show that the price one pays for restricting the search to the signature family is minimal by establishing that for a large class of models, there exists a "sparse enough" model in the signature family that fits the given marginal information well. We demonstrate the efficacy of learning sparse models on the well-known American Psychological Association (APA) dataset by showing that our sparse approximation manages to capture useful structural properties of the underlying model. Finally, our results suggest that signature condition can be considered an alternative to the recently popularized Restricted Null Space condition for efficient recovery of sparse models.
by Srikanth Jagabathula.
Ph.D.

Le sujet de cette thèse porte sur le développement et l'analyse de modèles stochastiques pour l'aide à la décision dans les centres d'appels. Dans la première partrie, nous considérons un centre d'appels où tous les agents sont groupés ensemble. Nous étudions les bénéfices de la migration depuis cette configuration à celle où les agents sont organisés en pool. Chaque pool est responsable d'un portefeuille de clients. Ensuite, nous considérons un centre d'appel avec des clients impatients. Nous développons des politiques dynamiques pour l'affectation des clients aux différentes files d'attente. L'objectif est lié aux qualités de service différenciées exprimées en terme du pourcentage de clients perdus. Enfin, nous étudions un centre d'appel qui annonce les délais d'attente aux clients. Nous montrons les avantages de l'annonce des délais sur les performances du système. Dans la deuxième partie, nous considérons un processus de naissance et de mort de forme générale. Nous calculons les moments de plusieurs variables aléatoires liées aux temps de premier passage d'un état à un autre. Ensuite, nous montrons un résultat de concavité dans une d'attente avec des clients impatients. Nous montrons que la probabilité d'entrer en service est strictement croissante et concave en fonction de la taille de la file d'attente
In this thesis, we focus on various operations management issues of call centers. We derive both qualitative and quantitative results for practical management. In the first part of the thesis, we investigate the impact of team-based organizations in call centers management. We develop queueing models that show the benefits of the team-based organization in providing better performances. Next, we consider a multicall call center with impatient customers. We develop dynamic scheduling policies that assign customers to the waiting lines. We focus on differentiated service levels criteria related to the fraction of abandoning customers. Finally, we propose a call center model in which we provide information about queueing delays to customers, and we quantify its effect upon performance. In the second part of the thesis, we tackled the quantitative analysis of stochastic processes ans queueing models. First, we derive several closed-form expressions of the moments of first passage times in general birth-death processes. Second, we investigate some monotonicity properties for the probability of being served in markovian queueing systems with impatient customers

Dans cette thèse, je me propose d’étudier le système d’échange de quotas d’émission (SEQE) et les politiques de reprise adoptés par l’Union européenne (UE) dans le cadre de ses efforts de lutte contre le réchauffement climatique et de gestion de certaines catégories de déchets. Mon étude porte sur trois principaux problèmes de recherche qui y sont associés, à savoir : (i) la façon dont les entreprises s’adapteront à une réforme du SEQE en vigueur, prévoyant la réduction du nombre de quotas en circulation et la mise en réserve de quotas excédentaires, (ii) la façon dont interagissent le SEQE et les politiques de reprise, et (iii) la question de savoir s’il est plus avantageux de gérer certaines catégories de déchets par le biais de politiques de reprise prévoyant des taux de récupération minimums ou d’une politique de plafonnement et d’échange. Chaque chapitre est consacré à un de ces trois problèmes de recherche. Dans le premier chapitre, j’examine la façon dont les entreprises s’adaptent à la réforme du SEQE prévoyant de permettre à l’organisme de réglementation de retirer un pourcentage des quotas excédentaires et de le mettre en réserve—la réserve de stabilité du marché. J’étudie les stratégies permettant aux entreprises de se mettre en conformité avec cette réforme et le problème du régulateur pour définir un paramètre de politique, à savoir le pourcentage de quotas excédentaires à retirer, susceptible de favoriser la réduction des émissions sans pour autant compromettre la prospérité économique. Pour ce faire, je me base sur un modèle couvrant deux périodes dans lequel l'organisme de réglementation définit d’abord le paramètre de politique, puis les entreprises réalisent leur production, réduisent leurs émissions et participent à une enchère de quotas. Dans le second chapitre, j’étudie comment le SEQE et les politiques de reprise des déchets, en vigueur dans l'Union européenne, interagissent et s’influencent mutuellement en matière d’efficacité économiques et environnementales. Pour cela, je modélise un système dans lequel des fournisseurs soumis à une politique de plafonnement et d’échange de droits d’émission de carbone approvisionnent en matières premières des fabricants soumis à une réglementation de reprise avec un taux de recyclage minimum. Dans le troisième chapitre, j’étudie s’il est plus avantageux de gérer certaines catégories de déchets par le biais de politiques de reprise prévoyant des taux de récupération minimums ou d’une politique de plafonnement et d’échange. Pour ce faire, je prends comme exemple le contexte de la gestion des déchets électroniques et j’utilise deux modèles schématiques pour comparer l’efficacité économique et environnementale de deux politiques, à savoir : une politique de reprise prévoyant un pourcentage imposant aux fabricants de produits électroniques de recycler ou de remanufacturer un pourcentage des nouveaux produits qu’ils introduisent sur le marché, et une politique prévoyant des quotas imposant aux fabricants de produits électroniques de recycler ou de remanufacturer tous les nouveaux produits qu’ils introduisent sur le marché, à l’exception d’une quantité limitée de produits qui peuvent finir dans des décharges sans être traités. Cette quantité limitée sera déterminée par le nombre de quotas que les fabricants acquerront lors de ventes aux enchères
In this thesis, I examine the European Union Emissions Trading System (EU ETS) and the take-back policies used in the European Union (EU) to fight global warming and manage some waste categories. I study three key research problems associated with them: (i) how firms will adapt to a reform to the EU ETS that reduces the number of allowances in circulation and places excess allowances in a reserve, (ii) how the EU ETS and the take-back policies interact, and (iii) is it better to manage some waste categories through take-back policies with minimum recovery rates or a cap-and-trade policy. Each chapter covers one problem. In the first chapter, I examine how firms will adapt to a reform to the EU ETS that allows the regulator to remove a percentage of surplus allowances and put them in a reserve—the market stability reserve. I study the firms’ strategies for compliance with this reform and the regulator’s problem of setting a policy parameter—the percentage of surplus allowances to withdraw–– that encourages emissions abatement without sacrificing economic prosperity. To do that, I model a game spanning two periods, in which the regulator first sets the policy parameter and then firms produce their output, reduce emissions, and participate in the auction for allowances. In the second chapter, I study how the EU ETS and waste take-back policies interact and impact each other’s economic and environmental effectiveness. To that end, I model a game where suppliers subject to a carbon cap-and-trade policy supply raw material to manufacturers subject to take-back policy with a minimum recycling target. In the third chapter, I study if it is better to manage some waste categories through take-back policies with minimum recovery rates or a cap-and-trade policy. To that end, I consider the electronic waste context, and I use two stylized models to compare the economic and environmental effectiveness of two policies: a percentage-based take-back policy, which requires electronic manufacturers to recycle or remanufacture a percentage of the new products they introduce into the market, and an allowance- based policy, which requires electronic manufacturers to recycle or remanufacture all the new products they introduce into the market except a limited quantity of products that can end up in landfills untreated. This limited quantity is determined by the number of allowances manufacturers gain in an auction

This dissertation is based on three essays in operations and supply chain management. In essay 1, we study an operations scheduling problem in a complex manufacturing system, most notably, semiconductor manufacturing. In particular, we study the scheduling problem of minimizing total weighted tardiness on parallel non-identical batch processing machines. We formulate the (primal) problem as a nonlinear integer programming model. Moreover, we prove that the primal problem can be solved exactly by solving a corresponding dual problem with nonlinear relaxation. Since both the primal and the dual problems are NP-hard, we propose to use genetic algorithms, based on random keys and multiple choice encodings, to heuristically solve them. We found that the genetic algorithms consistently outperform a standard mathematical programming package in terms of solutions and computation times. We also found that for small scale problem instances, the multiple choice genetic algorithm outperforms the random keys genetic algorithm, while for medium and large scale problem instances, the random keys genetic algorithm outperforms the multiple choice genetic algorithm. In essay 2, we study a monopolist firm offering successive versions of a durable good (e.g., software) that improves over time. The firm decides the time between successive introductions as well as price. In turn, consumers strategically decide whether to purchase or wait for a later version. We model and analyze three alternative strategies for offering successive product versions: the partial-, continuous-, and no-updates policies. We first consider the firm's profit maximizing policy assuming a homogeneous market and subsequently address consumers with heterogeneous product valuations. Our analytic model's simple structure and results highlight the important tradeoff between price and release timing for products with successive versions. In essay 3, we study the effect of time series structure of customer demand models on the value of information sharing within a supply chain. We contribute to the literature by incorporating a nonlinear demand model based on exponential disturbances, coupled with temporal heteroscedasticity, which captures more complex patterns in the demand process. We examine the conditions under which information sharing is valuable.
10000-01-01

In the last decades, there has been a huge evolution in the development and application of Derivative Free Optimization (DFO) techniques. One of the major emerging DFO application fields are the optimal design of industrial products and machines and the development of efficient computer software. This Ph.D. thesis focuses on the exploration of the emerging DFO techniques, oriented especially in the optimization of real-world industrial problem. In this thesis is firstly presented a brief overview of the main DFO techniques and application. Then, are reported two works describing the development of DFO approaches aimed to tackle the optimization of Computer Vision Algorithms (CVA), employed in the automatic defect detection of pieces produced by a real-world industries.

In the last few decades, the natural environment has increasingly received attention among policy makers, media and the international community. In such a context, firms have approached the environmental issue by developing different strategies and adopting practices that contribute to pollution reduction, resources savings and, more generally, to green efficiency. The present Ph.D. dissertation blends together the natural environment and operations management. As plants are the major responsible of pollution in the industry, this thesis aims at investigating their environmental choices and actions within the framework of manufacturing strategy. The thesis consists of three papers, each corresponding to a chapter. The first one addresses the environmental issue from a general perspective and defines the role of environmental strategy as long as the drivers underlying higher proactiveness; the second one deals with the environment as a competitive priority to understand which firm-related characteristics affect environmentally oriented manufacturing strategies; and the final one analyses the implementation of green and lean practices and the effects on plant performance. More precisely, the first chapter investigates the concept of proactive environmental strategy (PES). The literature has defined this strategy as a set of voluntary practices that go beyond mere compliance with regulations. In order to examine this concept and provide a narrower definition of PES, I first propose that PES is composed of two dimensions: the technical/organizational dimension and the embeddedness dimension. Second, I propose a theoretical framework that allows identification of different typologies in a firm’s environmental strategy. I finally outline the dynamics that drive shifts among PES typologies. In such a way, my final intention is to show that proactive environmental strategy is multidimensional and that there is not a unique definition of it, thus recommending an in-depth investigation of its position within the firm’s strategy. The second chapter allows understanding the role of the natural environment within the manufacturing strategy. Particularly, I analyse the effects of the organizational context on the emphasis that plant managers place on the environmental priority compared to the competitive priorities of cost, quality, delivery, flexibility and innovation. A multinomial logit model is used to explore whether and to what extent the organizational context, proxied by plant- and supply chain-related characteristics, influences three clusters of different manufacturing strategic patterns: environmental-oriented group, balanced set group, and cost-oriented group. Data is collected from a survey of manufacturing plants from Canada in the fabricated metal products, machinery, electronics, and electrical appliances industries. The results highlight that the organizational context affects the emphasis managers put on the environmental issue. In particular, they show that an international supply chain leads the plant to adopt an environmental oriented strategy. This might be explained by arguing that plants are more exposed to environmental risks caused by their international stakeholders, thus threatening their image or raising the chances to monetary fines. The final chapter analyses environmental management from a more operational perspective. Specifically, it contributes to the exploration of how lean and green practices might fit together to improve competitiveness. The relationship of lean manufacturing and environmental performance on the one side, and environmental management and operational performance on the other side, has widely been investigated but final benefits are still uncertain. Anyway, several researches have shown that some lean and green practices actually synergistically interact to improve plant performance. Using a case study methodology, the present paper aims to understand how the two sets of practices interact together and how they affect operational and environmental performance. Based on the analysis of three successful projects of two Italian plants, I find that the timing of implementation, i.e. sequential vs simultaneous, defines modes of managing lean and green practices, i.e. planning vs mutual adjustment, that ultimately affect plant performance. Finally, the findings identify what drivers foster lean practices and green practices to be synergic and jointly produce a positive impact on both environmental and operational performance
Negli ultimi decenni, l’ambiente è stato al centro di un crescente interesse tra regolamentatori, media e comunità internazionali. In tale contesto, le aziende si sono ritrovate ad affrontare la questione ambientale attraverso lo sviluppo di strategie e l’implementazione di pratiche atte a ridurre l’impatto inquinante, l’uso di risorse e, in generale, ad accrescere la propria efficienza in ottica green. La presente tesi di dottorato ha lo scopo di analizzare congiuntamente la questione ambientale e le operations. Questo tema è particolarmente interessante perché prende in considerazione le fabbriche, ovvero le principali responsabili di inquinamento, allo scopo di investigarne le scelte e le azioni ambientali nell’ambito della strategia manifatturiera. La tesi è formata da tre articoli, ognuno dei quali corrisponde ad un capitolo. Il primo tratta della questione ambientale partendo da una prospettiva generale e definendo qual è il ruolo della strategia ambientale e quali sono i fattori determinanti per accrescerne la proattività; il secondo considera l’aspetto ambientale come una possibile priorità competitiva e cerca di individuare i fattori legati alle caratteristiche dell’azienda che incidono su una strategia manifatturiera orientata alla protezione dell’ambiente; infine, l’ultimo capitolo esplora il processo di implementazione di pratiche lean e green nelle fabbriche e l’effetto che questo produce sulle performance. In particolare, il primo capitolo analizza il concetto di strategia ambientale proattiva (SAP). La letteratura ha definito questa strategia come un insieme di pratiche volontarie che non si limitano ad applicare le norme in materia ambientale. Allo scopo di formulare una puntuale definizione, dapprima identifico le dimensioni di SAP: una dimensione tecnica e organizzativa e una dimensione di intensità relazionale. Secondo, propongo una struttura teorica che permetta di individuare differenti tipologie di strategia ambientale. Infine, provo a delineare le spinte dinamiche che sono alla base dei cambiamenti tra una tipologia di SAP e l’altra. In questo modo, metto in evidenza che la strategia ambientale proattiva è multidimensionale e che non può essere definita univocamente, rendendo pertanto opportuno capirne il ruolo all’interno della strategia aziendale. Il secondo capitolo affronta il tema dell'ambiente naturale nell'ambito della strategia manifatturiera delle aziende. In particolare, lo scopo è analizzare gli effetti del contesto organizzativo sull'enfasi che i managers pongono sulla priorità ambientale con rispetto alle tradizionali priorità competitive di costo, qualità, consegna, flessibilità e innovazione. Grazie a una regressione logistica multinomiale, indago la relazione tra il contesto organizzativo, espresso in termini di caratteristiche dell'azienda e della catena di fornitura, e tre diverse possibili strategie manifatturiere: strategia orientata al sostegno ambientale; strategia orientata alla riduzione dei costi; e strategia con simile enfasi su tutte le priorità competitive. L'analisi empirica si basa su un campione di aziende manifatturiere canadesi operanti nei settori dei metalli, dei macchinari, dell’elettronica e delle apparecchiature elettroniche. I risultati dell'analisi evidenziano che il contesto organizzativo influisce sull'enfasi che i managers decidono di dare alla sostenibilità ambientale. Infatti, essi mostrano che quanto più la catena di fornitura si estende internazionalmente, ovvero i clienti e i fornitori sono dispersi in diverse aree geografiche, tanto più l'azienda adotta una strategia manifatturiera che pone enfasi sulla protezione dell'ambiente naturale. Questo è probabilmente motivato dal fatto che un'azienda ha maggiore difficoltà a controllare i potenziali rischi ambientali causati dai propri stakeholders operanti in diverse aree geografiche, dai quali possono derivare sanzioni pecunarie su pezzi importati non conformi alle norme e/o un danno alla propria visibilità a livello globale. L’ultimo capitolo affronta il tema della gestione ambientale da una prospettiva esclusivamente operativa. Nello specifico, questo articolo contribuisce a esplorare come le pratiche di lean management e le pratiche di green management possono essere implementate insieme per migliorare la competitività. La relazione tra lean manufacturing e performance ambientale da un lato, e tra gestione ambientale e performance operativa dall’altro, è stata ampiamente studiata ma senza arrivare a una visione univoca dei benefici che ne derivano. Tuttavia, un certo numero di ricerche hanno mostrato che alcune pratiche di lean e green sono complementari. Basandosi sulla metodologia dello studio di caso, il presente articolo ha lo scopo di capire come i due insiemi di pratiche interagiscono tra di loro e producono effetti sulle performance operative e ambientali. Attraverso l’analisi di tre progetti di successo di due aziende italiane, identifico che il timing di implementazione, sequenziale vs simultaneo, definisce le modalità di gestione delle pratiche da adottare – pianificazione vs mutuo aggiustamento. Questo aspetto ha un effetto significativo sulle performance dell'azienda in quanto l'approccio simultaneo porta a risultati maggiori rispetto all'approccio sequenziale. Infine, l'analisi dei casi mi permette di individuare i drivers che influenzano la relazione tra pratiche green e lean nell'implementazione di progetti ambientali

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2000.
Also available online at the DSpace at MIT website.
Includes bibliographical references (p. 119-122).
Organizations perform work essentially through operations and projects. The characteristics of projects makes them extremely difficult to manage: their non repetitive nature discards the trial and error learning, while their short life span is particularly unforgiving to misjudgments. Some authors have found that effective scheduling is an important contributor to the success of research and development (R&D), as well as construction projects. The widely used critical path method for scheduling projects and identifying important activities fails to capture two important dimensions of the problem: the availability of different technologies (or options) to perform the activities, and the inherent problem of limited availability of resources that most managers face. Nevertheless, when one tries to account for such additional constraints, the problems become very hard to solve. In this thesis we propose an approach to the scheduling problem using a genetic algorithm, and try to compare its performance to more traditional approaches, such as an extension to a very innovative Lagrangian relaxation approach recently proposed. The purpose of using genetic algorithms is twofold: first to obtain good approximations to very hard problems, and second to realize the limitations and virtues of this search technique. The purpose of this thesis is not only to develop the algorithms, but also to obtain insight about the implications of the additional constraints in the perspective of a project manager.
by Eduardo B. Messmacher.
S.M.

Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, 2014. In conjunction with the Leaders for Global Operations Program at MIT.
Cataloged from PDF version of thesis.
Includes bibliographical references (page 63).
Massachusetts General Hospital (MGH) is ranked as the top hospital in New England and second nation-wide. It is also the largest hospital in New England; it uses an average of 58 operating rooms, where approximately 150 surgical procedures are performed daily. Management of surgical supplies is a critical component of the processes supporting this infrastructure. Specifically, ensuring the right equipment and supplies are available at the right time is critical for the efficiency and quality outcomes of each of the procedures. The materials management group handles over 10,000 unique items, purchased from more than 400 vendors. The majority (60-70%) of disposable supplies are ordered through Owens & Minor, a medical and surgical supplies distributor. The supplies are stored in multiple locations throughout the hospital, including two central locations as well as carts and cabinets on the surgical floors and in the operating rooms. The work described in this thesis focuses on the inventory management of disposable surgical supplies, where the current system design has inefficiencies in the inventory levels and location of items. Using a data-driven approach, based on historical demand, we calculate base stock levels by item that maintain three days of inventory at a 99 percent service level. In addition, we suggest a methodology to support decisions on inventory locations of the different items. Implementation of the recommended changes is estimated to result in savings of 30-40% in inventory levels (and space), corresponding to a one time saving of $700,000-$900,000, depending on the implementation scenario. In addition, the reduction in inventory levels can be translated to future savings in inventory holding costs at an estimated 40% rate, leading to a saving of roughly $300,000 annually.
by Noa Ben-Zvi.
S.M.
M.B.A.

Thesis (S.M.)--Massachusetts Institute of Technology, System Design and Management Program, 2007.
Includes bibliographical references (leaves 128-134).
As the Army conducts transformation in the midst of an ongoing information driven Revolution in Military Affairs (RMA) and the War on Terror, it has realized the need to develop leaner, more agile, versatile and deployable forces. As part of its latest transformation to Brigade "Units of Action," the Army realized the need to improve the "tooth to tail" ratio of its forces and transferred from a Cold War "Divisional" force structure to one focused around more deployable and sustainable Brigade Units of Action. Ironically, this transformation to what is suppose to become a more lean and deployable force structure has produced larger and more heavily staffed battalion, brigade and division command posts. Despite introduction of the Army Battle Command System (ABCS), a system of digital systems that are intended to help speed up the Army's ability to transfer information, improve situational awareness, make decisions, and out "OODA" (Observe-Orient-Decide-Act) its opponents, in many aspects the Army has actually taken a step backwards. The end result is that these larger command posts are becoming more hierarchical and bureaucratic, and are reducing the Army's ability to get ahead of the enemy's decision cycle. Platoon Leaders and Company Commanders in Iraq and Afghanistan constantly lament that "if they only had the information they needed 48 hours earlier," they could have captured the target. This study examines one small aspect of this tremendous problem, the architecture of the Battalion Tactical Operations Center (TOC). It analyzes the current information revolution, the contemporary operating environment, network centric warfare, other emerging Revolution in Military Affairs (RMA) concepts, and the current Battalion TOC configuration and doctrine. It then applies System Dynamics techniques and develops a set of heuristics to address the problem. The ultimate goal of this study is to develop a practical concept for an improved organization, structure and function of the command post.
by Nathan A. Minami.
S.M.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2015.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 183-188).
This thesis revolves around applications of optimal portfolio theory. In the first essay, we study the optimal portfolio allocation among convergence trades and mean reversion trading strategies for a risk averse investor who faces Value-at-Risk and collateral constraints with and without fear of model misspecification. We investigate the properties of the optimal trading strategy, when the investor fully trusts his model dynamics. Subsequently, we investigate how the optimal trading strategy of the investor changes when he mistrusts the model. In particular, we assume that the investor believes that the data will come from an unknown member of a set of unspecified alternative models near his approximating model. The investor believes that his model is a pretty good approximation in the sense that the relative entropy of the alternative models with respect to his nominal model is small. Concern about model misspecification leads the investor to choose a robust optimal portfolio allocation that works well over that set of alternative models. In the second essay, we study how portfolio theory can be used as a framework for making biomedical funding allocation decisions focusing on the National Institutes of Health (NIH). Prioritizing research efforts is analogous to managing an investment portfolio. In both cases, there are competing opportunities to invest limited resources, and expected returns, risk, correlations, and the cost of lost opportunities are important factors in determining the return of those investments. Can we apply portfolio theory as a systematic framework of making biomedical funding allocation decisions? Does NIH manage its research risk in an efficient way? What are the challenges and limitations of portfolio theory as a way of making biomedical funding allocation decisions? Finally in the third essay, we investigate how risk constraints in portfolio optimization and fear of model misspecification affect the statistical properties of the market returns. Risk sensitive regulation has become the cornerstone of international financial regulations. How does this kind of regulation affect the statistical properties of the financial market? Does it affect the risk premium of the market? What about the volatility or the liquidity of the market?
by Dimitrios Bisias.
Ph. D.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2017.
Cataloged from PDF version of thesis.
Includes bibliographical references.
Revenue Management (RM) is an area with important advances in theory and practice in the last thirty years. This thesis presents three different new applications in RM with a focus on: the firms' perspective, the government's perspective as a policy maker, and the consumers' perspective (in terms of welfare). In this thesis, we first present a two-part tariff pricing problem faced by a satellite data provider. We estimate unobserved data with parametric density functions in order to generate instances of the problem. We propose a mixed integer programming formulation for pricing. As the problem is hard to solve, we propose heuristics that make use of the MIP formulation together with intrinsic properties of the problem. Furthermore, we contrast this approach with a dynamic programming approach. Both methodologies outperform the current pricing strategy of the satellite provider, even assuming misspecifications in the assumptions made. Subsequently, we study how the government can encourage green technology adoption through a rebate to consumers. We model this setting as a Stackleberg game where firms interact in a price-setting competing newsvendor problem where the government gives a rebate to consumers in the first stage. We show the trade-off between social welfare when the government decides an adoption target instead of a utilitarian objective. Then, we study the impact of competition and demand uncertainty on the three agents involved: firms, government, and consumers. This thesis recognizes the need to measure consumers' welfare for multiple items under demand uncertainty. As a result, this thesis builds on existing theory in order to incorporate demand uncertainty in Consumer Surplus. In many settings, produced quantities might not meet the realized demand at a given market price. This comes as an obstacle in the computation of consumer surplus. To address this, we define the concept of an allocation rule. In addition, we study the impact of uncertainty on consumers for different demand noise (additive and multiplicative) and for various allocation rules.
by Charles Mark Thraves Cortés-Monroy.
Ph. D.

Thesis (S.M.M.O.T.)--Massachusetts Institute of Technology, Sloan School of Management, Management of Technology Program, 2000.
Also available online at the DSpace at MIT website.
Includes bibliographical references (leaves 71-72).
The telecommunications industry is highly competitive. Many of the players in the Carrier, Commercial and Network Construction Service markets are looking to have financial, personnel, marketing, other resources and other competitive advantages such as B2B Internet services. Increased consolidation and· strategic alliances in the industry, resulting from the Telecommunications Act of 1996 is giving rise to significant new competition in the industry. In addition to this, the advent of the Internet has made the traditional circuit-switched telephony no longer efficient and economical, and to a certain extent obsolete. The less expensive and easily maintainable IP-switched networks are in greater demand. Information age has made Ecommerce the process of empowering the organizations for information exchange using digital technology. This study includes an analysis of the effect of the above factors on a network operator's business. It also consists of the case studies of two new-age network operators, Level 3 and Qwest.
by Tharunidhar Narravula.
S.M.M.O.T.

Scheduling of winter maintenance operations such as plowing or salting is a difficult and complex problem. Proper selection and timing of such operations is critical to their effectiveness, however scheduling decisions must often be made with strict time and resource limitations imposed upon them. A decision support system which analyses current road conditions and makes scheduling suggestions based on them would be a valuable step toward improving the quality of treatment, while simultaneously reducing the burden of scheduling on maintenance managers. This thesis proposes a real-time scheduling model based on an Operations Research framework that can be used by maintenance managers to develop and evaluate alternative resources allocation plans for winter road maintenance operations. The scheduling model is implemented as an Integer Linear Program and is solved using off-the-shelf software packages. The scheduling model takes into account a wide range of road and weather condition factors such as road network topology, road class, weather forecasts, and contractual service levels, and produces a vehicle dispatch schedule that is optimal with respect to operating costs and quality of service. A number of heuristics are also explored to aid in efficient approximations to this problem.

From 2003 to 2009, the EU launched five military conflict management operations within the framework of the European Security and Defence Policy. This thesis examines their success. To this end, the thesis develops a definition and a set of criteria for success. It applies this theoretical framework in an empirical case study of success in the five EU operations, which were undertaken in Macedonia, Bosnia and Herzegovina, the Democratic Republic of Congo, Chad and the Central African Republic. Having established the level and nature of their success, the thesis goes on to examine the conditions under which ESDP military conflict management operations can be successful. The key finding of the research is that for an operation of this nature to succeed, it is necessary that it secures sufficient support internally, within the EU, and externally, outside the EU, from domestic, regional and international actors involved in the conflict and its management.

This research furthers understanding of UK tour operator approaches to sustainable supply chain management (SSCM). Although the wider tour operating industry is only just beginning to demonstrate some responsibility and Interest in addressing business impacts, it is an increasingly important feature of the global business agenda. It is therefore important that an understanding is gained of how sustainability can be addressed in a way which meets both commercial imperatives and societal interests.

Thesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2016.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 153-157).
With the advances in information technology and the increased availability of data, new approaches that integrate learning and decision making have emerged in operations management. The learning-and-optimizing approaches can be used when the decision maker is faced with incomplete information in a dynamic environment. We first consider a network revenue management problem where a retailer aims to maximize revenue from multiple products with limited inventory constraints. The retailer does not know the exact demand distribution at each price and must learn the distribution from sales data. We propose a dynamic learning and pricing algorithm, which builds upon the Thompson sampling algorithm used for multi-armed bandit problems by incorporating inventory constraints. Our algorithm proves to have both strong theoretical performance guarantees as well as promising numerical performance results when compared to other algorithms developed for similar settings. We next consider a dynamic pricing problem for a single product where the demand curve is not known a priori. Motivated by business constraints that prevent sellers from conducting extensive price experimentation, we assume a model where the seller is allowed to make a bounded number of price changes during the selling period. We propose a pricing policy that incurs the smallest possible regret up to a constant factor. In addition to the theoretical results, we describe an implementation at Groupon, a large e-commerce marketplace for daily deals. The field study shows significant impact on revenue and bookings. Finally, we study a supply chain risk management problem. We propose a hybrid strategy that uses both process flexibility and inventory to mitigate risks. The interplay between process flexibility and inventory is modeled as a two-stage robust optimization problem: In the first stage, the firm allocates inventory, and in the second stage, after disruption strikes, the firm schedules its production using process flexibility to minimize demand shortage. By taking advantage of the structure of the second stage problem, we develop a delayed constraint generation algorithm that can efficiently solve the two-stage robust optimization problem. Our analysis of this model provides important insights regarding the impact of process flexibility on total inventory level and inventory allocation pattern.
by He Wang.
Ph. D.

Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; in conjunction with the Leaders for Global Operations Program at MIT, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 62-63).
The Building Construction Products (BCP) Division of Caterpillar makes 12 different loader, excavator, and tractor products with 10 manufacturing facilities worldwide. With relatively high volume machines, BCP saw that their supply base continued to have challenges in managing their inventory levels when machine volume and mix would change. Challenges included poor Supplier Shipping Performance (SSP), Point of Use (POU) availability, and inventory turns. These failures translated into poor Committed Ship Date (CSD) performance; which also directly impacted the overall cost of production and profitability of BCP. For example, coming out of the 2009 recession, suppliers were unable to keep up with BCP's increasing demand; which was attributed to supplier's lack of confidence in the BCP forecast, and only reviewing a 13-week capacity outlook. Therefore, BCP would like to have visibility into their supplier's planning processes, and through enhanced collaboration and communication, improve both BCP and their supplier's performance. To obtain the expected result, the scope of the project was to evaluate the Sales & Operations Planning (S&OP) processes of two identified suppliers. While the primary goal of the project was to develop a robust BCP Supplier S&OP process, the performance improvements were generated from Inventory and Operations Management tool creation and process improvement. The project followed the 6 Sigma approach of DMAIC to clearly evaluate the S&OP processes at both BCP Leicester and the two identified suppliers. The study concluded, through the development of a Supplier S&OP process that there were several important factors hindering the implementation of S&OP. These factors included capacity planning, planning parameters and inventory management policies. To enable implementation, the following tools were created: 1. Capacity Planning Tool enabled £30k annual cost avoidance on labor, logistics, and equipment through proactive management and scenario planning 2. Batch Size Tool enabled £20k+ reduction of inventory holding costs while also reducing near-term schedule variation to 2nd tier supplier 3. Safety Stock Tool provided inventory levels to align customer service with lead-times Through looking at the current BCP S&OP process at Caterpillar several key issues were identified with the quality of the output. These included lack of accountability for forecast accuracy and a lack of clear BCP Supply Chain strategy. To improve the identified issues the following actions were taken: 1. Created a Forecast Accuracy Tool that quickly identifies areas of concern 2. Submitted a future project proposal for Improving Piece-Part Forecast Accuracy 3. Recommended a future project for Cost Analysis on 8 week order-to-delivery SC model.
by Stephen Herington.
S.M.
M.B.A.