An Integrated Simulation, Learning and Game-theoretic Framework for Supply Chain Competition

Persistent Link:
http://hdl.handle.net/10150/338942
Title:
An Integrated Simulation, Learning and Game-theoretic Framework for Supply Chain Competition
Author:
Xu, Dong
Issue Date:
2014
Publisher:
The University of Arizona.
Rights:
Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
Abstract:
An integrated simulation, learning, and game-theoretic framework is proposed to address the dynamics of supply chain competition. The proposed framework is composed of 1) simulation-based game platform, 2) game solving and analysis module, and 3) multi-agent reinforcement learning module. The simulation-based game platform supports multi-paradigm modeling, such as agent-based modeling, discrete-event simulation, and system dynamics modeling. The game solving and analysis module is designed to include various parts including strategy refinement, data sampling, game solving, equilibrium conditions, solution evaluation, as well as comparative statistics under varying parameter values. The learning module facilitates the decision making of each supply chain competitor under the stochastic and uncertain environments considering different learning strategies. The proposed integrated framework is illustrated for a supply chain system under the newsvendor problem setting in several phases. At phase 1, an extended newsvendor competition considering both the product sale price and service level under an uncertain demand is studied. Assuming that each retailer has the full knowledge of the other retailer's decision space and profit function, we derived the existence and uniqueness conditions of a pure strategy Nash equilibrium with respect to the price and service dominance under additive and multiplicative demand forms. Furthermore, we compared the bounds and obtained various managerial insights. At phase 2, to extend the number of decision variables and enrich the payoff function of the problem considered at phase 1, a hybrid simulation-based framework involving systems dynamics and agent-based modeling is presented, followed by a novel game solving procedure, where the procedural components include strategy refinement, data sampling, gaming solving, and performance evaluation. Various numerical analyses based on the proposed procedure are presented, such as equilibrium accuracy, quality, and asymptotic/marginal stability. At phase 3, multi-agent reinforcement learning technique is employed for the competition scenarios under a partial/incomplete information setting, where each retailer can only observe the opponent' behaviors and adapt to them. Under such a setting, we studied different learning policies and learning rates with different decay patterns between the two competitors. Furthermore, the convergence issues are discussed as well. Finally, the best learning strategies under different problem scenarios are devised.
Type:
text; Electronic Dissertation
Keywords:
Inventory Control; Reinforcement Learning; Simulation; Supply Chain Management; Game Theory; Systems & Industrial Engineering
Degree Name:
Ph.D.
Degree Level:
doctoral
Degree Program:
Graduate College; Systems & Industrial Engineering
Degree Grantor:
University of Arizona
Advisor:
Son, Young-Jun

Full metadata record

DC FieldValue Language
dc.language.isoen_USen
dc.titleAn Integrated Simulation, Learning and Game-theoretic Framework for Supply Chain Competitionen_US
dc.creatorXu, Dongen_US
dc.contributor.authorXu, Dongen_US
dc.date.issued2014-
dc.publisherThe University of Arizona.en_US
dc.rightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.en_US
dc.description.abstractAn integrated simulation, learning, and game-theoretic framework is proposed to address the dynamics of supply chain competition. The proposed framework is composed of 1) simulation-based game platform, 2) game solving and analysis module, and 3) multi-agent reinforcement learning module. The simulation-based game platform supports multi-paradigm modeling, such as agent-based modeling, discrete-event simulation, and system dynamics modeling. The game solving and analysis module is designed to include various parts including strategy refinement, data sampling, game solving, equilibrium conditions, solution evaluation, as well as comparative statistics under varying parameter values. The learning module facilitates the decision making of each supply chain competitor under the stochastic and uncertain environments considering different learning strategies. The proposed integrated framework is illustrated for a supply chain system under the newsvendor problem setting in several phases. At phase 1, an extended newsvendor competition considering both the product sale price and service level under an uncertain demand is studied. Assuming that each retailer has the full knowledge of the other retailer's decision space and profit function, we derived the existence and uniqueness conditions of a pure strategy Nash equilibrium with respect to the price and service dominance under additive and multiplicative demand forms. Furthermore, we compared the bounds and obtained various managerial insights. At phase 2, to extend the number of decision variables and enrich the payoff function of the problem considered at phase 1, a hybrid simulation-based framework involving systems dynamics and agent-based modeling is presented, followed by a novel game solving procedure, where the procedural components include strategy refinement, data sampling, gaming solving, and performance evaluation. Various numerical analyses based on the proposed procedure are presented, such as equilibrium accuracy, quality, and asymptotic/marginal stability. At phase 3, multi-agent reinforcement learning technique is employed for the competition scenarios under a partial/incomplete information setting, where each retailer can only observe the opponent' behaviors and adapt to them. Under such a setting, we studied different learning policies and learning rates with different decay patterns between the two competitors. Furthermore, the convergence issues are discussed as well. Finally, the best learning strategies under different problem scenarios are devised.en_US
dc.typetexten
dc.typeElectronic Dissertationen
dc.subjectInventory Controlen_US
dc.subjectReinforcement Learningen_US
dc.subjectSimulationen_US
dc.subjectSupply Chain Managementen_US
dc.subjectGame Theoryen_US
dc.subjectSystems & Industrial Engineeringen_US
thesis.degree.namePh.D.en_US
thesis.degree.leveldoctoralen_US
thesis.degree.disciplineGraduate Collegeen_US
thesis.degree.disciplineSystems & Industrial Engineeringen_US
thesis.degree.grantorUniversity of Arizonaen_US
dc.contributor.advisorSon, Young-Junen_US
dc.contributor.committeememberSon, Young-Junen_US
dc.contributor.committeememberLin, Wei Huaen_US
dc.contributor.committeememberLiu, Jianen_US
dc.contributor.committeememberAn, Linglingen_US
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