Dissertations / Theses on the topic 'Demand Fluctuation'

En raison de l'augmentation des coûts d'énergie et des préoccupations environnementales telles que les empreintes de carbone élevées, les systèmes de la production d'électricité centralisée se restructurent pour profiter des avantages de la production distribuée afin de répondre aux exigences énergétiques toujours croissantes. Les microgrids sont considérés comme une solution possible pour déployer une génération distribuée qui inclut des ressources énergétiques distribuées DERs (Distributed Energy Resources)(e.g, solaire, éolienne, batterie, etc). Dans cette thèse, nous traitons les défis de la gestion d'énergie dans un microgrid industriel où les charges énergétique sont constituées de processus industriels. Notre plan consiste à diviser la gestion de l'énergie du microgrid en deux parties: la production et la demande d’énergie.Du côté de la production d'énergie, les défis incluent la modélisation des générations de puissance et le lissage des fluctuations des DER. Pour modéliser les générations de puissance, nous proposons un modèle basé sur les concepts de service courbé de Network Calculus. En utilisant cet outil mathématique, nous déterminons une quantité minimale de puissance que les DERs peuvent générer; leur agrégation nous donnera une production d'énergie totale dans le microgrid. Après cela, s'il existe un déséquilibre entre la production et la demande d'énergie, nous proposons des stratégies différentes pour minimiser les coûts d'approvisionnement énergétique. Sur la base des données réelles de la consommation d'énergie d'un site industriel situé en France, des économies significatives peuvent être réalisées en adoptant ces stratégies. Dans cette thèse, nous étudions également comment atténuer les effets des fluctuations de puissance des DERs en conjonction avec des systèmes de stockage d'énergie. Pour cela, nous proposons un algorithme de lissage gaussien et nous le comparons avec des algorithmes de lissage trouvés dans l'état de l'art. Nous avons trouvé que l'algorithme proposé utilise de batterie de moins de taille à des fins de lissage par rapport à d'autres algorithmes. À cette fin, nous sommes également intéressés à étudier les effets de la gamme admissible des fluctuations sur les tailles de la batterie.Du côté de la demande, l'objectif est de réduire les coûts de l'énergie grâce aux approches de gestion de la demande DSM (Demand Side Management) telles que Demand Response (DR) et Energy Efficiency. Comme les processus industriels consomment énormément, une petite réduction de la consommation d'énergie en utilisant les approches DSM pourrait se traduire par des économies cruciales. Cette thèse se concentre sur l'approche DR qui peut profiter des prix variables de l'électricité dans le temps pour déplacer les demandes énergétiques des heures de pointe aux heures creuses. Pour atteindre cet objectif, nous comptons sur un modèle basé sur la théorie de file d'attente pour caractériser les comportements temporels (arrivée et départ des tâches) d'un système de fabrication. Après avoir défini les processus d'arrivée et de départ de tâches, une fonction d'utilisation efficace est utilisée pour prédire le comportement de la machine dans un domaine temporel et qui peut afficher son statut (allumé/éteint) à tout moment. En prenant le statut de chaque machine dans une ligne de production comme une entrée, nous proposons également un algorithme de planification DR qui adapte la consommation d'énergie d'une ligne de production aux deux contraintes de puissance disponibles et de taux de production. L'algorithme est codé à l'aide d’une machine d’état fini déterministe (Deterministic Finite State Machine) dans laquelle les transitions d'état se produisent en insérant une tâche à l'entrée du tapis roulant (on peut aussi avoir des transitions sans insertion de taches). Nous définissons des conditions pour l'existence d’un planificateur réalisable et aussi des conditions pour accepter positivement des demandes DRs
Due to increased energy costs and environmental concerns such as elevated carbon footprints, centralized power generation systems are restructuring themselves to reap benefits of distributed generation in order to meet the ever growing energy demands. Microgrids are considered as a possible solution to deploy distributed generation which includes Distributed Energy Resources (DERs) (e.g., solar, wind, battery, etc). In this thesis, we are interested in addressing energy management challenges in an industrial microgrid where energy loads consist of industrial processes. Our plan of attack is to divide the microgrid energy management into supply and demand sides.In supply side, the challenges include modeling of power generations and smoothing out fluctuations of the DERs. To model power generations, we propose amodel based on service curve concepts of Network Calculus (NC). Using this mathematical tool, we determine a minimum amount of power the DERs can generate and aggregating them will give us total power production in the microgrid. After that, if there is an imbalance between energy supply and demand, we put forward different strategies to minimize energy procurement costs. Based on real power consumption data of an industrial site located in France, significant cost savings can be made by adopting the strategies. In this thesis, we also study how to mitigate the effects of power fluctuations of DERs in conjunction with Energy Storage Systems (ESSs). For this purpose, we propose a Gaussian-based smoothing algorithm and compare it with state-of-the-art smoothing algorithms. We found out that the proposed algorithm uses less battery size for smoothing purposes when compared to other algorithms. To this end, we are also interested in investigating effects of allowable range of fluctuations on battery sizes.In demand side, the aim is to reduce energy costs through Demand Side Management (DSM) approaches such as Demand Response (DR) and Energy Efficiency (EE). As industrial processes are power-hungry consumers, a small power consumption reduction using the DSM approaches could translate into crucial savings. This thesis focuses on DR approach that can leverage time varying electricity prices to move energy demands from peak to off-peak hours. To attain this goal, we rely on a queuing theory-based model to characterize temporal behaviors (arrival and departure of jobs) of a manufacturing system. After defining job arrival and departure processes, an effective utilization function is used to predict workstation’s (or machine’s) behavior in temporal domain that can show its status (working or idle) at any time. Taking the status of every machine in a production line as an input, we also propose a DR scheduling algorithm that adapts power consumption of a production line to available power and production rate constraints. The algorithm is coded using Deterministic Finite State Machine (DFSM) in which state transitions happen by inserting a job (or not inserting) at conveyor input. We provide conditions for existence of feasible schedules and conditions to accept DR requests positively.To verify analytical computations on the queuing part, we have enhanced Objective Modular Network Testbed in C++ (OMNET++) discrete event simulator for fitting it to our needs. We modified various libraries in OMNET++ to add machine and conveyor modules. In this thesis, we also setup a testbed to experiment with a smart DR protocol called Open Automated Demand Response (OpenADR) that enables energy providers (e.g., utility grid) to ask consumers to reduce their power consumption for a given time. The objective is to explore how to implement our DR scheduling algorithm on top of OpenADR

Part I deals with the estimation of money demand functions. Several non-structural interpretations of the conventionally estimated functions are surveyed and discussed (Chapter 1). An application to Italian data is then presented, focusing on two such interpretations. First (Chapter 2), the role of expectations in determining money demand behaviour is assessed. Since monetary policy regimes have a direct effect on the time-series properties of interest rates, the identification of clear regime changes may provide a powerful test of forward-looking models of money demand. An expectations model is constructed, which is stable in the face of the Italian monetary policy regime change in 1970, when traditional backward-looking money demand functions show remarkable instability. Second (Chapter 3), the existence of multiple long-run relations among the variables relevant to money demand is shown to create problems for the interpretation of single-equation estimates. To obtain a satisfactory specification of the long-run relations and the short-run dynamics of the system around equilibrium, a sequential procedure is devised and applied. In Part II, the controversy between "real" and "monetary" theories of fluctuations is examined (Chapter 4). A "monetary" equilibrium model of the cycle is constructed, extending the original Lucas "island" framework to allow for a powerful role for stabilization policy. The implications of alternative monetary policy regimes are derived and tested on U.S. data, comparing two periods (1922-1940 and 1952-1968) with a different policy stance. Chapter 5 investigates the relative importance of the "money" and "credit" channels of monetary transmission for Italy in the 1982-1994 period, using a structural VAR methodology. Monetary policy is effective, though not through a "credit channel", and independent disturbances to credit supply have sizeable real effects. In Chapter 6 the focus is shifted to anticipated fiscal policy actions and their effect on consumption. A long series of pre-announced income tax changes is examined for the U.K. Consumption reacts to such fiscally-induced disposable income changes only at the implementation dates.

Si la realite economique du phenomene des fluctuations saisonnieres de demande parait manifeste, la question des differentes modalites de reaction des firmes qui y sont confrontees, et notamment des circonstances qui les motivent reste entiere. Le but de la these est precisement de formaliser ces differents comportements, c'est a dire d'en definir les conditions et les limites d'optimalite. Le cadre d'analyse de cette etude theorique est base sur la consideration d'un marche a composante monopolistique sur lequel evolue une firme produisant un bien unique dont la demande fluctuante est representee par deux periodes d'activite distinctes. Ce contexte peut etre rendu plus ou moins contraint en supposant un produit de nature instockable et ou une rigidite absolue du prix pratique
The context fo this research is a firm in a monopolistic marcket. It products a single output for wich the demand is very fluctuating. We propose to define and analyse the different ways of adaptation of the firm, in terms of price and quantity adjustments, to these foreseeable and recurrent fluctuations of demand. We also examine the conditions in wich these equilibrium are keeping optimal when the situation is constrained by considering an unstockable output and or an absolute price rigidity

Retail location planning in the grocery sector is used to aid location-based decision-making; addressing issues such as, store performance, store planning or for estimating demand. Location planning teams employ a range of sophisticated modelling techniques; this thesis draws on Spatial Interaction Models (SIMs). Spatiotemporal components have received limited attention in relation to retail location planning and this is addressed within this thesis. It is argued that spatiotemporal components of consumer demand fluctuate considerably within catchment areas, which has a direct impact of store sales, and that models, which have accounted for specific aspects of spatiotemporal demand result in better and more representative store revenue estimations. This thesis demonstrates the importance of spatiotemporal demand through an analysis of store level diurnal sales patterns which are related to observed consumer shopping habits, providing novel insight and improving our understanding of supply and demand-side spatiotemporal components. The insight is used to generate a series of spatiotemporal demand layers, which are used in conjunction with a (SIM) to generate spatiotemporally informed store level revenue estimations. Rare access to temporal EPOS transaction records at a store level and substantial loyalty card data provided by a major player in the UK grocery market presents novel opportunities for analysis. The data enables this thesis to generate insight into temporal fluctuations of store sales and the demand side drivers behind them, as well as, the geographies of consumer demand. The findings demonstrate evidence of distinct clusters of diurnal sales profiles in stores, which appear to be directly related to specific spatiotemporal demand types, and is used in conjunction with a SIM. The analysis shows that through adding spatiotemporal demand layers, demand estimates within catchment areas are far more representative and can lead to improved revenue estimations.

Enligt Carlsson och Rönnqvist (2005) och Frayret et al. (2007) blir det allt viktigare att arbeta med supply chain management inom trävaruindustrin. En utmaning är att finna ett arbetssätt för att hantera variationen i kundens efterfrågan. Idag hanteras variationen oftast genom onödigt stor lagerhållning (Lee et al., 1997b; So och Zheng, 2003).                                                                                 Syftet med studien är att utreda påverkande faktorer som bidrar till en varierande efterfrågan inom trävaruindustrin samt föreslå hur uppkomsten av dessa kan undvikas. För att svara på syftet har tre frågeställningar tagits fram och en fallstudie genomfördes på ett hyvleri. Informationsinsamlingen har skett genom intervjuer av anställda från olika avdelningar samt litteraturstudier. Studien visar att det finns flera utmaningar när det är stor variation på efterfrågan såsom brist på tillgång till prognoser och kommunikationsbrist med kunder. Det medför att planeringen av råvaruåtgången försvåras och det leder till svårigheter att uppnå leveransprecision.   Slutsatsen visar att de påverkande faktorer som bidrar till en varierad efterfrågan är prisvariationer, orderstorlek och orderfrekvens. Prisvariationer kan undvikas genom ABC-indelning av produkterna utifrån produktefterfrågan. Prognoser underlättar uppskattning av efterfrågan men för ett fungerande prognosarbete krävs samsyn, nära relation samt god kommunikation mellan kund och leverantör. Problem med orderstorlek och orderfrekvens kan reduceras om kunden får avgöra orderstorleken utan att specifika krav måste uppfyllas. Slutsatsen visar även att faktorer såsom väderlek, trender, mode, helgdagar och rotavdragets eventuella försvinnande påverkar variationen i efterfrågan.
According to Carlsson & Rönnqvist (2005) and Frayret et al. (2007) supply chain management in the wood products industry is getting more important. One of the challenges is to find a way to deal with customer’s fluctuating demand. Traditional solution to handle fluctuating demand is large inventory (Lee et al., 1997b; So & Zheng, 2003), which causes often high inventory cost for effective supply chain management.   The aim of the study is to investigate factors that affect a fluctuating demand in the wood products industry and suggest how to reduce the fluctuating demand through related factors analysis in order to improve Demand-Supply chain management efficiency. For this pursose, a case study on a planing is conducted. To collect data, interviews with employees from different departments have been made along with literature studies. The study presents that there are many challenges for the fluctuation demand such as lack of forecasts and lack of communication with customers. This, in turn, will cause problems with planning of the raw material as well as difficulties to deliver the goods on time.   The conclusion shows that the factors affecting a fluctuating demand are price variations, the orders batch size and order frequency. Price variations can be improved by ABC classification of the products by product demand. Forecasts will make the estimation of demand easier, although, in order to use forecasts properly a joint vision, close relationships and good communication with customer and supplier is required. Problems regarding batch size and order frequency can be reduced if the customer is allowed to decide the batch size with no specific requirements. The study also shows that factors such as weather, trends, fashion, holidays and disappearance of ROT work affects the fluctuating demand.

Background: The implementation of procurement concepts such as JIT or singlesourcing have resulted in the emergence of new supply risks forautomotive suppliers. The economic crisis in 2008 and volatiledemand in recent years had enormous impact on the sector.Consequently, in association with lean purchasing models, newdimensions of supply risks have emerged. This creates the need forautomotive suppliers to adapt and improve their supply riskmanagement in response to the increased risk potential. There hasbeen no research on how automotive suppliers have furtherdeveloped their supply risk management recently. Purpose: The purpose is to examine how automotive suppliers have adaptedtheir supply risk management in response to the fluctuatingeconomy since 2008. Frame of reference: In this section the Kraljic matrix and the risk management processare presented. The theories lead to a synthesis including the researchquestions for fulfilling the purpose. Method: This research is based on a qualitative multiple case study. In orderto gather the necessary in-depth data, four automotive suppliersfrom Germany and Northern Europe were interviewed by theauthors. Conclusions: Automotive suppliers have clearly reacted on increasedconsequences of supply risks. The general grown awareness andsensitivity have lead to the implementation of new managementtools. Particularly the cooperation between supply chain membershas considerably intensified and contributed to a better riskreduction. Moreover, the financial stability of vendors has risen inimportance and is considered more thoroughly. All the instrumentsand methods may, however, be more powerful and efficient ifautomotive suppliers had standardized and linked them into aconsecutive process.

Background: Forecasting is an instrument that the managers rely upon for their anticipations of the future. Subcontractors control their operations according to the forecast volumes provided by the telecom mobile network equipment suppliers. The information in the forecasts is however not sufficient.

Purpose: The purpose of this thesis is to identify and test relevant and available market indicators for prediction of future demand fluctuations on the telecom mobile network equipment market.

Realisation: During a number of interviews, factors that are driving the network equipment market were clarified. The aim of this part was to identify possible market indicators. Hypotheses were set up to test the chosen indicators. In the second part, the indicators were tested statistically. Finally, the theoretical and logical support of the results was discussed.

Result: To predict future movements in network equipment demand, the market indicators should focus on the telecom mobile operators, and their ability, need, and willingness to make new investments. The market indicators proven to be of most importance after the regression analyses were long-term market interest rates and telecom corporate bond indices.

With background to recent price growth in Swedish real estate and consequently real estate stocks, our aim is to examine the relationship between real estate price development and real estate stock price development. To test our hypothesis, that real estate price development have had an impact on the return of real estate stocks, we built a capital asset pricing model. We divide the return of real estate stocks into two parts, the return in relation to the Swedish market premium and the excess return that is given for the exposure of the real estate market. We found that real estate exposure would treat the investor with an additional return beyond the return given from stock market exposure; hence, real estate price development has contributed to real estate stock returns.

The hotel industry is characterized by perishability, which means that its services cannot be stored and saved to a later sales opportunity. In that world, a room that is not booked is lost forever. Another thing that also characterizes the industry is that it is seasonally controlled, thus the demand varies between different periods of time and occasions. As hotels tend to have high fixed costs, companies often choose to price their services higher when the opportunity that consumers pay a higher price appears. This can lead to a situation when the consumers feel fooled by the fact that the hotel itself does not create the new conditions that makes the demand rise. As a support for the interviews conducted, a research focused on the real prices has also been conducted where the prices of fifteen hotels in Stockholm were checked for three months on three occasions. To investigate the relationship between these two phenomena, seven hotel employees have been interviewed and shared their experiences of the reality and strategies within hotel industry. In order to get an overall picture, fifty consumers having profiles of potential hotel guests, have been interviewed where they answer questions about different scenarios and estimated prices for hotels of three different classes in Stockholm. The research made it possible to determine some conclusions. It appeared to be important for hotel industry to optimize prices in a way that they reflect the demand. An important tool to achieve this optimization is yield management and it confirms by the driving forces that identifies most often. The result of this reality is that the hotels experience fluctuations of prices over time, with rarely occurring stable pricing curves. Price awareness of customers tend to be raised when their experience of similar purchases is high. It has also been found that high prices do not necessarily have to lower the consumer value and that hotel guests may be willing to pay a higher price due to certain external factors. The relation between pricing and customer value was found to be complex and as there are some factors preventing the customer value to get lowered by price increase, it might be beneficial for hotels to take these factors into account.
Hotellbranschen kännetecknas av förgänglighet, vilket innebär att dess tjänster inte går att spara till ett senare försäljningstillfälle. I den världen går ett rum som inte bokas för alltid förlorad. Något som också kännetecknar branschen är att den är säsongsstyrd och därmed varierar efterfrågan mellan olika tidsperioder och tillfällen. I och med att hotell tenderar att ha höga fasta kostnader, väljer företagen ofta att prissätta sina tjänster högre när möjligheten för att konsumenter betalar ett högre pris uppstår. Detta kan leda till att konsumenterna känner sig lurade i och med att hotellet inte självt skapar de nya förutsättningarna som gör att efterfrågan stiger. Som ett stöd för de genomförda intervjuerna har även en prisundersökning genomförts där priserna på femton hotell i Stockholm under fyra månaders tid kontrollerades vid tre tillfällen. För att utreda hur relationen mellan dessa två fenomen ser ut har sju hotellmedarbetare intervjuats och beskrivit den verkligheten och de strategierna som finns. För att kunna få en helhetsbild har även femtio konsumenter, alltså potentiella hotellgäster, intervjuats där de svarar på frågor om olika scenarier samt uppskatta priser på hotell av olika klasser i Stockholm. Slutsatserna till undersökningen förblev att hotell till en övervägande majoritet använder sig av verktyg som yield management för att hantera dessa fluktuationer i efterfrågan och därmed ser också ett hotells priskurva väldigt sällan stabil ut. De potentiella hotellgästerna ställer sig kluvna i frågan om hur priset på ett hotellrum påverkar det upplevda värdet, vilket också delvis ställer det teoretiska ramverket och det empiriska resultatet emot varandra. Slutsatsen av det är att det finns en komplexitet i detta fenomen, där det finns ett flertal faktorer som erfordras att undersökas vidare för att nå en djupare och bredare förståelse.

碩士
國立臺北科技大學
工業工程與管理系碩士班
101
The steel industry is an important basis for industrial development, and it’s characteristics are technology-intensive, capital-intensive and energy-intensive, and closely link with its upstream and downstream industries.　Covered the important raw material for livelihood industries. All of those fields are intimately with steel industry. However, Taiwan is the island-oriented country. Although being provided with the geographical advantage, but Taiwan lacks of natural resources.　The majority of steel-making raw materials dependent on imports. With rising transportation cost of raw materials trading around the worldwide markets, this situation will trigger the problem of oil price-hike, leading to the production costs of the steel industry stays high. Many factors will affect steel price, including production cost relationship between supply and demand, and market environment; but dismiss the impact of the environment, the production cost is a direct factor of the price fluctuation. More important, the relationship between supply and demand of steel products is the main factors to influence the trend of the involving prices. The purpose of this study is to combine honey-bee mating algorithms and neural network, then establishing a set of demand fluctuation forecast about steel finished goods. We expect the certainty of the future of the steel product demand situation. Not only to protect the domestic steel market from foreign low-priced competitive strategy, but also to guarantee to the domestic steel of supply chain from middle to downstream. Finally, to create ALL-WIN for whole supply chain of the both sides；to be more precisely, making Taiwan''s iron and steel industry more competitive. To compared HBMO-based SOM with SOM. The experimental results show that HBMO-based SOM with factors selection is better than SOM with all factors, and the model of divided-HBMOSOM can more accurate the Taiwan steel demand fluctuation. Shows different data pre-processing mode will definitely affect the steel demand fluctuation forecast accuracy.

碩士
國立臺北科技大學
工業工程與管理系碩士班
102
Development of steel industry affected by global markets, are the basis of economic and industrial development, although a direct factor causing changes in steel prices is the cost of production, but the relationship between supply and demand is an important factor affecting the price trend. Therefore, the primary task of the domestic steel industry, which is to maintain the supply-demand balance of steel, the only certainty on the future of the steel situation analysis in order to protect the domestic steel industry from low marketing strategy abroad, while middle and lower reaches of the domestic demand for steel also can borrow this is guaranteed, and create mutual benefits of both supply and demand situation, so that Taiwan''s steel industry more competitive. The experimental results show that our proposed multi-attribute genetic algorithm combined with the weight of the moving average method is superior to the right than the prediction model prediction model and a single attribute moving average single attribute genetic algorithm combined with the weighted moving average method, and enhance 4 % to 5% of the prediction error; while in the second phase of multiple properties combined with the genetic algorithm results and weight of the moving average method right after the interval and Price comparison accuracy literature, in the hot-rolled stainless steel coils and steel, the interval study are 45.00% accuracy rate, and superior results literature. Shows that the accuracy rate is calculated in a linear fashion up the decline of demand for steel products, thereby enhancing the accuracy range of steel products demand.

Demand fluctuations will bias the measurement of productivity and efficiency. This dissertation described three ways to characterize the effect of demand fluctuations. First, a two-dimensional efficiency decomposition (2DED) of profitability is proposed for manufacturing, service, or hybrid production systems to account for the demand effect. The first dimension identifies four components of efficiency: capacity design, demand generation, operations, and demand consumption, using Network Data Envelopment Analysis (Network DEA). The second dimension decomposes the efficiency measures and integrates them into a profitability efficiency framework. Thus, each component's profitability change can be analyzed based on technical efficiency change, scale efficiency change and allocative efficiency change. Second, this study proposes a proactive DEA model to account for demand fluctuations and proposes input or output adjustments to maximize effective production. Demand fluctuations lead to variations in the output levels affecting measures of technical efficiency. In the short-run, firms can adjust their variable resources to address the demand fluctuates and perform more efficiently. Proactive DEA is a short-run capacity planning method, proposed to provide decision support to a firm interested in improving the effectiveness of a production system under demand uncertainty using a stochastic programming DEA (SPDEA) approach. This method improves the decision making related to short-run capacity expansion and estimates the expected value of effectiveness given demand. In the third part of the dissertation, a Nash-Cournot equilibrium is identified for an oligopolistic market. The standard assumption in the efficiency literature that firms desire to produce on the production frontier may not hold in an oligopolistic market where the production decisions of all firms will determine the market price, i.e. an increase in a firm's output level leads to a lower market clearing price and potentially-lower profits. Models for both the production possibility set and the inverse demand function are used to identify a Nash-Cournot equilibrium and improvement targets which may not be on the strongly efficient production frontier. This behavior is referred to as rational inefficiency because the firm reduces its productivity levels in order to increase profits.

碩士
國立中央大學
工業管理研究所
98
The focus of this study is to discuss a product with fluctuations prices which sells in the market, make use of another products price as a leading indicator to estimate its expected value in next period, retailers expect to respond the trend of price volatility in the future through by leading indicator, they could detailed consideration of the ordering strategy for the future. To complete strategies,increase profit revenue and reduce stock losses, we add the exceed cost and shortage costs in newsboy model and derived it to determine the order quantity, and compare with variant EOQ ordering strategy.

碩士
雲林科技大學
工業工程與管理研究所碩士班
98
In recent years, the development of Internet and Business-to-Customer had quickly changed in freight transportation on supply and demand. Thus, logistics systems require a diversified and multi-temperature distribution planning capability to meet requirements from all customers. In this study, multi-temperature co-delivery vehicle routing planning is focused on combines the single-temperature vehicles and the vehicle with cold box to delivery goods for all customers. A mathematical model and the associated solution procedure are developed to minimize the total cost and find the near optimal vehicle routings. The objective of this model is to minimize the total cost which includes the total vehicle costs and the total transportation costs. In the first stage of solution procedure, we use the nearest neighbor algorithm to construct an initial solution. The second stage is a heuristic algorithm by using the logic of Tabu Search. Finally three test examples are used to verify the mathematical model and the proposed heuristic algorithm. The solution results indicate that the model proposed in this study is attractive on the following view points: the number of vehicles to be used, the total vehicle costs, and the total transportation costs.

博士
國立交通大學
運輸與物流管理學系
102
Airline network design is crucial to an airline’s operation performance and competition. A hub-and-spoke air cargo network system is widely used by airliners for its effect on economies of scale. And the establishment of air transport hub, seen as an engine for growth, has always been an end goal for major airport worldwide for the development of aviation related industries. However, in reality, there are all sorts of fluctuations with seasonal and regional features which affect airline network design and even the long-term strategic choices of air transport hub. Airliners should evaluate and prepare themselves on relevant impacts in advance. This research develops an analytic model by formulating and minimizing the logistics costs to design the hub-and-spoke air cargo network of an airline, instead of using a mathematical programming model, commonly adopted in previous studies, which may not present the feature of economies of scale clearly. The model in this research is capable of reflecting the impacts of cargo time value, aircraft type, cargo flow, relative locations between O/D points and air transport hub on logistics costs. And it plays key effects on the observation of economies of scale on hub-and-spoke air network. Besides, through the model, we can also evaluate the impacts of demand fluctuation on the reliability of aircraft types, flight frequencies and routes. In addition, because of the consolidation effect on major route of hub-and-spoke air cargo network, the fluctuations of cargo flows may be quite severe and the chance of adjusting its optimal aircraft types and flight frequencies may increase as well. This research also designs three scenarios to study the impacts of regional economic development in Asia-pacific region on air network design and even the development of air transport hub, the result in this research turns out that geographical location play more crucial role on the local traffic flows for an airport, if it is going to develop as an air transport hub. Therefore, Seoul and Shanghai benefit the most to become a regional air transport hub among major airports in the Asia-pacific region, due to the rise of Mainland China shifting the air transport market share northwardly.

碩士
國立臺北科技大學
工業工程與管理研究所
95
Nowadays, there are many decision makers struggled to make good decision. The NPV method, the most common way decision makers would use to exercise, can not provide appropriate decision in this changing and dynamic environment. Over the past decade, the development and application of real options become more and more mature in the decision of engineering projects. In this investigation we verify that the fluctuation of market demand has become one of the most important factors while the enterprise executives make decisions in today’s ever-changing environment. In this investigation, a more accurate model is provided for decision makers. The value of game theory is that it can show the interacting situations of competition, whereas the value of real options is that the analysis concept can help decision makers find the suitable discount rate, show the dynamic competition strategy, and provide the evaluating outcomes accurately. This investigation combines the decision flexibility of real options with the analysis approach of game theory to reduce the defects, the fixed discount rate and the inability to postpone the investment, in NPV method. This investigation utilizes the risk-neutral method to analyze Nash-Cournot and Nash-Stackelberg equilibrium again. In this study, we find the value of projects in traditional NPV is underestimated when the demand fluctuates, decision makers may make an inappropriate decision. After integrating the risk-neutral method with game theory, decision makers can get a more accurate value of project and make better decisions.

Dans cette thèse, je me suis intéressé aux effets des fluctuations du prix de pétrole sur l'activité macroéconomique selon la cause sous-jacente ces fluctuations. Les modèles économiques utilisés dans cette thèse sont principalement les modèles d'équilibre général dynamique stochastique (de l'anglais Dynamic Stochastic General Equilibrium, DSGE) et les modèles Vecteurs Autorégressifs, VAR. Plusieurs études ont examiné les effets des fluctuations du prix de pétrole sur les principaux variables macroéconomiques, mais très peu d'entre elles ont fait spécifiquement le lien entre les effets des fluctuations du prix du pétrole et la l'origine de ces fluctuations. Pourtant, il est largement admis dans les études plus récentes que les augmentations du prix du pétrole peuvent avoir des effets très différents en fonction de la cause sous-jacente de cette augmentation. Ma thèse, structurée en trois chapitres, porte une attention particulière aux sources de fluctuations du prix de pétrole et leurs impacts sur l'activité macroéconomique en général, et en particulier sur l'économie du Canada. Le premier chapitre examine comment les chocs d'offre de pétrole, de demande agrégée, et de demande de précaution de pétrole affectent l'économie du Canada, dans un Modèle d'équilibre Général Dynamique Stochastique estimé. L'estimation est réalisée par la méthode Bayésienne, en utilisant des données trimestrielles canadiennes sur la période 1983Q1 à 2010Q4. Les résultats montrent que les effets dynamiques des fluctuations du prix du pétrole sur les principaux agrégats macro-économiques canadiens varient en fonction de leurs sources. En particulier, une augmentation de 10% du prix réel du pétrole causée par des chocs positifs sur la demande globale étrangère a un effet positif significatif de l'ordre de 0,4% sur le PIB réel du Canada au moment de l'impact et l'effet reste positif sur tous les horizons. En revanche, une augmentation du prix réel du pétrole causée par des chocs négatifs sur l'offre de pétrole ou par des chocs positifs de la demande de pétrole de précaution a un effet négligeable sur le PIB réel du Canada au moment de l'impact, mais provoque une baisse légèrement significative après l'impact. En outre, parmi les chocs pétroliers identifiés, les chocs sur la demande globale étrangère ont été relativement plus important pour expliquer la fluctuation des principaux agrégats macroéconomiques du Canada au cours de la période d'estimation. Le deuxième chapitre utilise un modèle Structurel VAR en Panel pour examiner les liens entre les chocs de demande et d'offre de pétrole et les ajustements de la demande de travail et des salaires dans les industries manufacturières au Canada. Le modèle est estimé sur des données annuelles désagrégées au niveau industriel sur la période de 1975 à 2008. Les principaux résultats suggèrent qu'un choc positif de demande globale a un effet positif sur la demande de travail et les salaires, à court terme et à long terme. Un choc négatif sur l'offre de pétrole a un effet négatif relativement faible au moment de l'impact, mais l'effet devient positif après la première année. En revanche, un choc positif sur la demande précaution de pétrole a un impact négatif à tous les horizons. Les estimations industrie-par-industrie confirment les précédents résultats en panel. En outre, le papier examine comment les effets des différents chocs pétroliers sur la demande travail et les salaires varient en fonction du degré d'exposition commerciale et de l'intensité en énergie dans la production. Il ressort que les industries fortement exposées au commerce international et les industries fortement intensives en énergie sont plus vulnérables aux fluctuations du prix du pétrole causées par des chocs d'offre de pétrole ou des chocs de demande globale. Le dernier chapitre examine les implications en terme de bien-être social de l'introduction des inventaires en pétrole sur le marché mondial à l'aide d'un modèle DSGE de trois pays dont deux pays importateurs de pétrole et un pays exportateur de pétrole. Les gains de bien-être sont mesurés par la variation compensatoire de la consommation sous deux règles de politique monétaire. Les principaux résultats montrent que l'introduction des inventaires en pétrole a des effets négatifs sur le bien-être des consommateurs dans chacun des deux pays importateurs de pétrole, alors qu'il a des effets positifs sur le bien-être des consommateurs dans le pays exportateur de pétrole, quelle que soit la règle de politique monétaire. Par ailleurs, l'inclusion de la dépréciation du taux de change dans les règles de politique monétaire permet de réduire les coûts sociaux pour les pays importateurs de pétrole. Enfin, l'ampleur des effets de bien-être dépend du niveau d'inventaire en pétrole à l'état stationnaire et est principalement expliquée par les chocs sur les inventaires en pétrole.
In this thesis, I am interested in the effects of fluctuations in oil prices on macroeconomic activity depending on the underlying cause of these fluctuations. The economic models used in this thesis include the Dynamic Stochastic General Equilibrium (DSGE) Models and Vector Autoregressive (VAR) Models. Several studies have examined the effects of fluctuations in oil price on the main macroeconomic variables, but very few of theses studies have specifically made the link between the effects of fluctuations in oil prices and the origin of these fluctuations. However, it is widely accepted in more recent studies that oil price increases may have very different effects depending on the underlying cause of that increase. My thesis, structured in three chapters, is focused on the sources of fluctuations in oil price and their impacts on the macroeconomic activity in general, and in particular on the canadian economy. The first chapter of the thesis investigates how oil supply shocks, aggregate demand shocks, and precautionary oil demand shocks affect Canada's economy, within an estimated Dynamic Stochastic General Equilibrium (DSGE) model. The estimation is conducted using Bayesian methods, with Canadian quarterly data from 1983Q1 to 2010Q4. The results suggest that the dynamic effects of oil price shocks on Canadian macroeconomic variables vary according to their sources. In particular, a 10% increase in the real price of oil driven by positive foreign aggregate demand shocks has a significant positive effect of about 0.4% on Canada's real GDP upon impact and the effect remains positive over time. In contrast, an increase in the real price of oil driven by negative foreign oil supply shocks or by positive precautionary oil demand shocks causes an insignificant effect on Canada's real GDP upon impact but causes a slightly significant decline afterwards. The intuition is that a positive innovation in aggregate demand tends to increase the demand for Canada's overall exports. Oil supply disruptions in foreign countries or positive precautionary oil demand shocks increase the uncertainty about future oil prices, which leads firms to postpone irreversible investment expenditures, and tends to reduce Canada's real GDP. Furthermore, among the identified oil shocks, foreign aggregate demand shocks have been relatively more important in explaining the variations of most of Canadian macroeconomic variables over the estimation period. The second chapter examines the links between oil demand and supply shocks and labor market adjustments in Canadian manufacturing industries using a panel structural VAR model. The model is estimated with disaggregated annual data at the industry level from 1975 to 2008. The results show that a positive aggregate demand shock increases both labor and the price of labor over a 20-year period. A negative oil supply shock has a relatively small negative effect upon impact but the effect turns positive after the first year. In contrast, a positive precautionary oil demand shock has a negative impact over all horizons. The paper also examines how the responses to different types of oil shocks vary from industry to industry. The results suggest that industries with higher net trade exposure/oil-intensity are more vulnerable to oil price increases driven by oil supply shocks and aggregate demand shocks. The third chapter examines the welfare implications of introducing competitive storage on the global oil market using a three country DSGE model characterized by two oil-importing countries and one oil-exporting country. The welfare gains are measured by consumption compensating variation under two alternative monetary policy rules. The main results indicate that the introduction of oil storage has negative welfare effects for each of the two oil importing countries, while it has positive welfare effects for the oil exporting country, whatever the monetary policy rule. I also found that including the exchange rate depreciation in the monetary policy rules allows to slightly reduce the welfare costs for both oil importing countries. Finally, the magnitude of the welfare effects depends on the steady state level of oil storage and is mainly driven by oil storage shocks.

Cet ouvrage présente une analyse d'un système de demande dans le domaine énergétique. Il concerne le marché résidentiel québécois. Le but est de déterminer les facteurs importants qui causent des perturbations dans la demande énergétique. L'Enquête des dépenses des ménages de Statistique Canada servira de source de données afin d'analyser les comportements de ceux-ci. Ce système de demande permettra de comprendre les réactions des ménages à ces perturbations du marché. De plus, il servira à mesurer le niveau de sensibilité des ménages aux prix d'électricité et des autres combustibles. Un modèle économétrique de la forme AIDS sera utilisé pour arriver à trouver ces variables déterminantes pour bien expliquer la demande énergétique. L'explication de ce modèle sera présentée pour bien démontrer le respect de la théorie économique. La résolution de ce système permet d'établir une liste de variables importantes ayant un impact dans la demande des ménages. Notre étude permet également d'expliquer de façon précise les réactions des ménages à des fluctuations de prix du marché. Nos résultats démontrent que la sensibilité des ménages aux prix d'électricité n'est pas inexistante et n'est pas similaire pour tous les ménages. Ce résultat est expliqué en fonction des différentes carctéristiques des ménages. Certaines carctéristiques des ménages influencent plus que d'autres le niveau de sensibilité aux prix d'électricité. Le degré d'importance des différentes caractéristiques est établi afin d'expliquer les résultats obtenus. Finalement, notre étude permet de faire des suggestions aux différentes institutions impliquées dans ce marché. Ces conseils poursuivent comme but l'amélioration de leurs objectifs personnels.

碩士
樹德科技大學
經營管理研究所
104
Marketing and consumer behavior literature has empirically demonstrated that reference prices play a critical role in customer purchase decisions. In this study, we extend the classical economic order quantity model to allow for not only a function of price and reference price but also fluctuating with time. The objective is to find the optimal replenishment number, time scheduling and dynamic pricing strategy to maximize the total profit over a finite planning horizon. An effective search procedure is provided to find the optimal solution by employing the properties derived in this paper and particle swarm optimization algorithm. Several numerical examples are used to illustrate the features of the proposed model.