Relevant bibliographies by topics / Optimization and supply chain Management

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Optimization and supply chain Management'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 February 2022

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Journal articles on the topic "Optimization and supply chain Management"

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Prafulla K Swain et al.,, Prafulla K. Swain et al ,. "Optimization Methods of Supply Chain Management." International Journal of Mechanical and Production Engineering Research and Development 10, no. 3 (2020): 9411–20. http://dx.doi.org/10.24247/ijmperdjun2020894.

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Park, Ji Seok, and Sunguk N. Jang. "Endoscopic Supply Chain Management and Optimization." Gastrointestinal Endoscopy Clinics of North America 31, no. 4 (October 2021): 671–79. http://dx.doi.org/10.1016/j.giec.2021.05.004.

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Patmond Mbhele, Thokozani. "Engineering patterns of supply chain optimization to manage oscillation effect." Problems and Perspectives in Management 15, no. 2 (June 8, 2017): 124–39. http://dx.doi.org/10.21511/ppm.15(2).2017.12.

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The cascading order variability from downstream trumping up the upstream site of the supply chain network indicates the deleterious effect to the performance of the fast moving consumer goods industry. The fundamental likelihood to optimization in this industry requires dexterous flows of quasi-real-time information, as well as reliable product availability. In this context, this study analyzes the challenges of bullwhip effect on the perspective of ingenious optimization strategies, and further contemplates to establish the engineering patterns of interrelationships on the magnitude of pooling the resources to advance supply chain capabilities. The suppression of bullwhip effect on underlying optimization strategies is sought to elevate accelerated responsiveness, improve network demand visibility and reduce volatility in frequencies to inventory replenishment. A rigorous and disciplined quantitative approach afforded the tentatively development of pattern of interrelated supply chain dimensions. The factor analysis method was used on 448 responses and insightful findings were produced from the compelling purposive sampling technique. The findings indicate that the magnitude of better ameliorating bullwhip effect, the value of competitive economic information and strength of selected optimization strategies depend on the model of unified engineering patterns. This paper provides insights to FMCG industry on using innovative strategies and modern technology to enhance supply chain visibility through integrated systems networks.
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Ene, Seval, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (April 12, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.1391.

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In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: Green supply chain management, multi-objective modelling, network optimization;
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Ene, Seval, and Nursel Ozturk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (January 15, 2018): 15–24. http://dx.doi.org/10.18844/gjbem.v7i1.1875.

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In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: green supply chain management, multi-objective modelling, network optimization.
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Küçükoğlu, Ilker, and Nursel Öztürk. "Multi-objective green supply chain network optimization." Global Journal of Business, Economics and Management: Current Issues 7, no. 1 (October 20, 2017): 15. http://dx.doi.org/10.18844/gjbem.v7i1.2561.

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Abstract In supply chain management, economical objectives have traditionally guided decisions of the supply chains. However, with increased global environmental and social concerns, in recent years, green aspects have been incorporated in supply chain decisions. These expansions lead to new research areas about green or sustainable supply chain management that includes applying various green practices in order to reduce negative impact on the environment or providing sustainable development. The purpose of this study is to develop a multi-objective optimization model for determining network design of the green supply chains. In multi-objective frame of the proposed model, total profit maximization and environmental impact minimization objectives are considered in order to obtain best network configuration for economic and environmental performance of the green chain. The proposed model is validated with numerical experiments. Obtained results showed that the model can be used as a strategic decision tool in problems with multi and conflicting objectives. Keywords: green supply chain management, multi-objective modelling, network optimization.
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ASLAM, M. M. HARIS, KAMRAN RASHID, and TASHFEEN M. AZHAR. "ROLE OF OPTIMAL PRODUCTION PLAN AT THE FOCAL FIRM IN OPTIMIZATION OF THE SUPPLY CHAIN." Journal of Management and Research 1, no. 2 (November 29, 2019): 1–8. http://dx.doi.org/10.29145/jmr/12/0102001.

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Supply chain management and optimization is a critical aspect of modern enterprises and an expanding area of research. Modeling and optimization are the traditional tools of supply chain management. The techniques have been used by many companies for planning, manufacturing, and other decision areas in supply chains. Current study is motivated by the fact that optimization studies in supply chain management have mostly considered network optimization. Supply chain management however, requires alignment between the supply chain partners at the tactical level. As a first step towards achieving this goal, current study presents a model that incorporates the activity level planning at the focal firm in a supply chain. This paper presents a new mixed integer programming model that incorporates optimization of production planning at the focal firm while optimizing the strategic alignment of the supply chain entities. The model represents a four step, multi-echelon supply chain including supplier, warehouse, manufacturer, and retailer. The manufacturer in this network represents the focal firm. This model is an attempt to integrate the production planning decisions in the network optimization decisions.
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Kuzmina, Yana I., and Nikolay A. Zenkevich. "Supply chain finance solutions in joint working capital management." Contributions to Game Theory and Management 13 (2020): 252–95. http://dx.doi.org/10.21638/11701/spbu31.2020.14.

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The research is devoted to joint working capital management in supply chains aiming to improve joint working capital management methods through minimization of financial supply chain costs on working capital using Supply Chain Finance (SCF) Solutions. Though SCF applicability in Financial Supply Chain management has recently been studied to relieve access to capital sources, managerial perspective of SCF solutions is still uninvestigated as well as few other areas. The research suggests a managerial algorithm that contains four developed models: the model of Collaborative cash conversion cycle two models of SCF solutions and the model of Joint Working Capital optimization. The models imply using such SCF solutions as Factoring, Reverse Factoring and Inventory Financing to improve Joint Working Capital in terms of costs on it and liquidity of both supply chain members and entire chain, providing the optimal conditions of SCF solutions. Quantitative optimization with SCF solutions demonstrates on the cases of supply chains the improvement of financial position and liquidity of all chain members. The research has a potential to de applied in businesses since the algorithm represents a comprehensive managerial tool for Joint Working capital management in supply chains. It might be used to achieve optimal cash conversion cycle values for minimal supply chain costs on working capital constrained by liquidity and profitability target levels.
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Pan, Feng Shan, Chun Ming Ye, and Jiao Yang. "Inventory Optimization in Supply Chains." Applied Mechanics and Materials 66-68 (July 2011): 1305–10. http://dx.doi.org/10.4028/www.scientific.net/amm.66-68.1305.

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This paper reviews joint inventory management models and discusses its contribution to improve the overall supply chain competitiveness, after discussing suppliers and agents’ plan model respectively. Further, based on the background of Company F, and combined the modern scientific management theory of supply chain inventory with practical case, it improves the inventory management method. Finally, the conclusion of calculation is presented in detail.
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Jin, Maozhu, Hua Wang, Qian Zhang, and Yucheng Zeng. "Supply chain optimization based on chain management and mass customization." Information Systems and e-Business Management 18, no. 4 (January 3, 2019): 647–64. http://dx.doi.org/10.1007/s10257-018-0389-8.

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Dissertations / Theses on the topic "Optimization and supply chain Management"

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Romero, Morales Dolores. "Optimization problems in supply chain management /." [S.l.] : [s.n.], 2000. http://aleph.unisg.ch/hsgscan/hm00021582.pdf.

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Wike, Carl E. 1948. "Supply chain optimization : formulations and algorithms." Thesis, Massachusetts Institute of Technology, 1999. http://hdl.handle.net/1721.1/9763.

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Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 1999.
Includes bibliographical references (leaves 103-106).
In this thesis, we develop practical solution methods for a supply chain optimization problem: a multi-echelon, un capacitated, time-expanded network of distribution cen­ters and stores, for which we seek the shipping schedule that minimizes total inventory, backlogging, and shipping costs, assuming deterministic, time-varying demand over a fixed time horizon for a single product. Because of fixed ordering and shipping costs, this concave cost network flow problem is in a class of NP-hard network design problems. We develop mathematical programming formulations, heuristic algorithms, and enhanced algorithms using approximate dynamic programming (ADP). We achieve a strong mixed integer programming (MIP) formulation, and fast, reliable algorithms, which can be extended to problems with multiple products. Beginning with a lot-size based formulation, we strengthen the formulation in steps to develop one which is a variation of a node-arc formulation for the network design problem. In addition, we present a path-flow formulation for the single product case and an enhanced network design formulation for the multiple product case. The basic algorithm we develop uses a dynamic lot-size model with backlogging together with a greedy procedure that emulates inventory pull systems. Four re­lated algorithms perform local searches of the basic algorithm's solution or explore alternative solutions using pricing schemes, including a Lagrangian-based heuristic. We show how approximate dynamic programming can be used to solve this sup­ply chain optimization problem as a dynamic control problem using any of the five algorithms. In addition to improving all the algorithms, the ADP enhancement turns the simplest algorithm into one comparable to the more complex ones. Our computational results illustrate that our enhanced network design formula­tion almost always produces integral solutions and can be used to solve problems of moderate size (3 distribution centers, 30 stores, 30 periods). Our heuristic methods, particularly those enhanced by ADP methods, produce near optimal solutions for truly large scale problems.
by Carl E. Wike.
S.M.
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Xu, Shubin, and Shubin Xu. "Essays in Operations and Supply Chain Management." Thesis, University of Oregon, 2012. http://hdl.handle.net/1794/12408.

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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
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Huang, Wei. "Optimization models for sourcing decisions in supply chain management." [Gainesville, Fla.] : University of Florida, 2004. http://purl.fcla.edu/fcla/etd/UFE0006605.

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Lo, Billy S. (Billy Si Yee). "Inventory optimization in an aerospace supply chain." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39678.

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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 Manufacturing Program at MIT, 2007.
Includes bibliographical references (p. 66-67).
Strategic inventory management has become a major focus for Honeywell Aerospace as the business unit challenged itself to meeting cost reduction goals while maintaining a high level of service to its customers. This challenge has become particularly important as customers have steered their purchase decisions from focusing only on capability and quality to including cost performance as well. To do so, Honeywell Aerospace's Planning and Asset Management group is undertaking a three-year effort to re-engineer its inventory planning systems with the goal of increasing planner productivity, improving supply chain responsiveness, and reducing overall inventory. This internship forms the building blocks of this strategy by leveraging existing software available in the industry and applying it to Honeywell's supply chain. Through two pilot programs with different supply chain designs, this internship analyzed the cost and benefit of transforming the company's inventory management strategy. In addition, this internship attempts to identify the challenges associated with such an enormous change, compare them with challenges with implementation in other industries in order to prepare management for full implementation across all product lines. These challenges range from leadership buy-in and information readiness to implementation feasibility both within Honeywell manufacturing and its suppliers.
by Billy S. Lo.
S.M.
M.B.A.
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Ng, Chong Keat. "Inbound supply chain optimization and process improvement." Thesis, Massachusetts Institute of Technology, 2012. http://hdl.handle.net/1721.1/81122.

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Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division; and, (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; in conjunction with the Leaders for Global Operations Program at MIT, 2012.
Cataloged from PDF version of thesis.
Includes bibliographical references (p. 58).
The primary goal of this project is to evaluate Amazon's inbound supply chain processes and identify improvement opportunities in transportation cost and lead time. Analysis will be focused on defining the current state and evaluating the strengths and weaknesses of the inbound processes. This paper will also include a literature review of the various freight consolidation and vendor coordination strategies in the industry and their impact on cost and lead time. Specific case studies based on actual cost savings programs will be discussed.
by Chong Keat Ng.
M.B.A.
S.M.
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Hsu, Stephanie K. (Stephanie Karen) 1976. "Supply chain optimization in a retail environment." Thesis, Massachusetts Institute of Technology, 2003. http://hdl.handle.net/1721.1/84354.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2003.
Includes bibliographical references (p. 53).
by Stephanie K. Hsu.
S.M.
M.B.A.
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Henkle, Aimee L. (Aimee Leigh) 1975. "Global supply chain design and optimization methodology." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/34762.

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Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2004.
Includes bibliographical references (p. 72).
The work for this thesis was performed at Honeywell in the Automation and Control Solutions (ACS) division. The project focuses on ACS's manufacturing strategy regarding its global supply chain design, primarily discussing the manufacturing growth opportunities available in emerging regions. Honeywell ACS's current methodology for the development of a long-term manufacturing strategy is based on growth and total cost reduction objectives. In order to comprehend the total cost of the manufacturing strategy, considerations such as inventory, logistics and duties, outsourcing and material sourcing are evaluated. The project also considers a factory's geographical location and ACS's year-by-year implementation plan. An outcome of this Honeywell project and the basis of this thesis is the development of a general supply chain design and optimization methodology that utilizes three analytical tools (Country Selection Framework, Total Cost Model and Implementation Plan Process) that are capable of validating the supply chain design of any company. The analytical tools can be used to verify key strategic supply chain decisions or to create a baseline manufacturing strategy. The following results can be determined using this supply chain design methodology: Determine an appropriate operating region for current or future business needs; Evaluate the feasibility of factory relocation projects by considering all relevant costs; Evaluate the cost implications of the supply chain structure by considering logistics, inventory and material sourcing costs; Understand the impact of outsourcing on the manufacturing strategy; Recommend a year-by-year implementation plan in the case of multiple projects and limited capital resources.
by Aimee L. Henkle.
S.M.
M.B.A.
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Gunnarsson, Helene. "Supply chain optimization in the forest industry /." Linköping : Department of Mathematics, Linköpings universitet, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8856.

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Karimipour, Hadadan Elham. "Inventory Optimization through Integration of Marketing and Supply Chain Management." Thesis, Högskolan i Gävle, Avdelningen för ekonomi, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-21969.

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Purpose: This study aims to find how the integration between marketing and operations can improve demand management in order to have efficient inventory level and avoid excess inventory. Method used: In order to have optimize inventory level and managing demand, integrating business process between marketing, supply planning and inventory management team was considered in this study. Qualitative data from nine interviews among three direct sales cosmetics companies was gathered. Findings: Empirical findings address excess inventory is caused by several issue in the company as poor management of demand forecasting, wide product portfolio, long lead time, the lack of sharing information between the company and its suppliers and ineffective strategy to avoid excess inventory within the company. Regarding to improve forecast accuracy and manage demand, findings indicate the role of marketing in obtaining knowledge about customer insight is not good enough. Practical Implication: It is critical that works and plans from each function be integrated to optimize inventory. In order to support optimize inventory strategy all pertinent departments must continue reviewing meeting with the aim to reach a consensus about the products planning for the both side of demand and operation align with overall strategic goals of the company. Contribution: Empirical data demonstrate the best way for effective implication of demand management occurs when marketing can provide demand information in time, as well as supply chain management can react flexibly to demand changes in time. Moreover, the lack of integration between marketing and supply chain management is a major barrier in optimizing inventory.
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Books on the topic "Optimization and supply chain Management"

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Pirim, Harun, Umar Al-Turki, and Bekir Sami Yilbas. Supply Chain Management and Optimization in Manufacturing. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08183-0.

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Watson, Michael. Supply chain network design: Applying optimization and analytics to the global supply chain. Upper Saddle River, N.J: FT Press, 2013.

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Supply chain optimization, management and integration: Emerging applications. Hershey, PA: Business Science Reference, 2011.

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Sabri, Ehap H. Optimization of supply chain management in contemporary organizations. Hershey, PA: Business Science Reference, an imprint of IGI Global, 2015.

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service), SpringerLink (Online, ed. Demand Flexibility in Supply Chain Planning. Boston, MA: Springer US, 2012.

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1952-, Reiter Stephen E., ed. Supply chain optimization: Building the strongest total business network. San Francisco: Berrett-Koehler Publishers, 1996.

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Supply chain optimization, design, and management: Advances and intelligent methods. Hershey, PA: Business Science Reference, 2011.

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Song, Dong-Ping. Optimal Control and Optimization of Stochastic Supply Chain Systems. London: Springer London, 2013.

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Davis, Robert A. Demand-driven inventory optimization and replenishment: Creating a more efficient supply chain. Hoboken, New Jersey: Wiley, 2013.

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Shanthikumar, J. George. Stochastic Modeling and Optimization of Manufacturing Systems and Supply Chains. Boston, MA: Springer US, 2003.

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Book chapters on the topic "Optimization and supply chain Management"

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Pirim, Harun, Umar Al-Turki, and Bekir Sami Yilbas. "Supply Chain Management." In Supply Chain Management and Optimization in Manufacturing, 9–23. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08183-0_2.

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Pirim, Harun, Umar Al-Turki, and Bekir Sami Yilbas. "Optimization in Supply Chain." In Supply Chain Management and Optimization in Manufacturing, 43–60. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08183-0_4.

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Carvalho, Marcius F., and Pythagoras G. Furtado. "Optimization Structures for Supply Chain Management." In IFIP Advances in Information and Communication Technology, 227–36. Boston, MA: Springer US, 2002. http://dx.doi.org/10.1007/978-0-387-35585-6_25.

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Braglia, Marcello, Marco Frosolini, Roberto Gabbrielli, and Leonardo Marrazzini. "Process Engineering and Optimization." In Operations, Logistics and Supply Chain Management, 399–415. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92447-2_18.

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Pirim, Harun, Umar Al-Turki, and Bekir Sami Yilbas. "Scheduling Models in Supply Chain." In Supply Chain Management and Optimization in Manufacturing, 25–42. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08183-0_3.

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Christou, Ioannis T. "A Review of Optimization Methods." In Quantitative Methods in Supply Chain Management, 1–138. London: Springer London, 2011. http://dx.doi.org/10.1007/978-0-85729-766-2_1.

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Tempelmeier, Horst, and Gabriele Reith-Ahlemeier. "Advanced Purchasing and Order Optimization." In Supply Chain Management and Reverse Logistics, 349–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24815-6_16.

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Mula, Josefa, David Peidro, and Raúl Poler. "Optimization Models for Supply Chain Production Planning Under Fuzziness." In Supply Chain Management Under Fuzziness, 397–422. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-53939-8_17.

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Kyu, Jae Lee, and Yong Chang Sik. "A Framework of Optimization Agent for Supply Chain Management." In Multiagent based Supply Chain Management, 155–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-33876-5_6.

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Palma, Fanny, Jania A. Saucedo, and José A. Marmolejo. "Recovery Method of Supply Chain Under Ripple Effect: Supply Chain Event Management (SCEM) Application." In Intelligent Computing & Optimization, 455–65. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00979-3_48.

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Conference papers on the topic "Optimization and supply chain Management"

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Tajri, Hayat, and Anas Chafi. "Change management in supply chain: Supply chain urbanization method." In 2018 4th International Conference on Optimization and Applications (ICOA). IEEE, 2018. http://dx.doi.org/10.1109/icoa.2018.8370561.

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Tajri, Hayat, and Anas Chafi. "Change management in supply chain." In 2018 4th International Conference on Optimization and Applications (ICOA). IEEE, 2018. http://dx.doi.org/10.1109/icoa.2018.8370560.

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Munasinghe, Inoka, and Thashika Rupasinghe. "SIMULATION AND OPTIMIZATION-BASED APPROACHES TO ASSESS THE BEHAVIOUR OF THE RETAIL SUPPLY CHAIN NETWORK DESIGNS." In World Conference on Supply Chain Management. TIIKM, 2017. http://dx.doi.org/10.17501/wcosm.2017.2102.

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Saravanan, Sriramulu, Jaishankar K.S., Sajeesh Kumar Krishnan Nair, and Venkatachalam B. "Supply Chain Management and Optimization Models." In International Conference on Computer Applications — Management. Singapore: Research Publishing Services, 2010. http://dx.doi.org/10.3850/978-981-08-7303-5_1663.

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Bekrar, Abdelghani, Sondes Chaabane, and Philippe Wieser. "Supply chain optimization in healthcare systems." In 2015 International Conference on Industrial Engineering and Systems Management (IESM). IEEE, 2015. http://dx.doi.org/10.1109/iesm.2015.7380219.

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LAKHMAS, Kamal, and Abdelfettah SEDQUI. "Port Logistics Optimization Model Study." In 2018 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). IEEE, 2018. http://dx.doi.org/10.1109/logistiqua.2018.8428265.

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Li, Guang, and Daozhi Zhao. "Optimization of Supply Chain under Cooperation Constraints." In International Conference of Logistics Engineering and Management (ICLEM) 2010. Reston, VA: American Society of Civil Engineers, 2010. http://dx.doi.org/10.1061/41139(387)600.

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Hu, Song, and Lei Xiao. "Research on supply chain management and optimization models." In 2010 2nd International Conference on Information Science and Engineering (ICISE). IEEE, 2010. http://dx.doi.org/10.1109/icise.2010.5691152.

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Zhang, Liwei, Yan Chen, and Hongyu Jia. "Supply Chain Structure Optimization Based on Risk Management." In First International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2007. http://dx.doi.org/10.1061/40932(246)633.

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Wang, Zhiping, Yutang Zou, Haichao Wang, and Zhifang Feng. "The optimization of reverse supply chain supernetwork." In 2010 International Conference on Logistics Systems and Intelligent Management (ICLSIM). IEEE, 2010. http://dx.doi.org/10.1109/iclsim.2010.5461338.

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Reports on the topic "Optimization and supply chain Management"

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Morales, Diane K. DoD Supply Chain Materiel Management Regulation. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada430976.

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Visker, Edward R. Improving the DoD Supply Chain Can Commercial Supply Chain Management Software Do the Job"". Fort Belvoir, VA: Defense Technical Information Center, April 2000. http://dx.doi.org/10.21236/ada378230.

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Lee, Y. T. Management data specification for supply chain integration. Gaithersburg, MD: National Institute of Standards and Technology, 2001. http://dx.doi.org/10.6028/nist.ir.6703.

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Carter, Charles L. Intelligence Support to Supply Chain Risk Management. Fort Belvoir, VA: Defense Technical Information Center, June 2012. http://dx.doi.org/10.21236/ada562495.

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Perrin, Richard A., Robert E. Bona, Dennis A. Brekhus, and Carol E. Fraser. ARN Supply Chain Management System for OCIE. Fort Belvoir, VA: Defense Technical Information Center, December 2006. http://dx.doi.org/10.21236/ada473021.

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Cobb, Barry R. Modeling Uncertainty in Military Supply Chain Management Decisions. Fort Belvoir, VA: Defense Technical Information Center, June 2014. http://dx.doi.org/10.21236/ada612034.

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White III, Chelsea C., and Bojan Petrovic. Improvements in SMR Modular Construction through Supply Chain Optimization and Lessons Learned. Office of Scientific and Technical Information (OSTI), March 2017. http://dx.doi.org/10.2172/1351978.

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Boyens, Jon, Celia Paulsen, Nadya Bartol, Stephany A. Shankles, and Rama Moorthy. Notional Supply Chain Risk Management Practices for Federal Information Systems. Gaithersburg, MD: National Institute of Standards and Technology, October 2012. http://dx.doi.org/10.6028/nist.ir.7622.

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Radde, Guillaume. A restful web service for virtual supply chain time management. Gaithersburg, MD: National Institute of Standards and Technology, 2010. http://dx.doi.org/10.6028/nist.ir.7680.

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Boyens, Jon M., Celia Paulsen, Nadya Bartol, Kris Winkler, and James Gimbi. Case Studies in Cyber Supply Chain Risk Management: Mayo Clinic. Gaithersburg, MD: National Institute of Standards and Technology, February 2020. http://dx.doi.org/10.6028/nist.cswp.02042020-5.

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