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postgraduate thesis: Stochastic supply chain network design under the meanCVaR criterion
Title  Stochastic supply chain network design under the meanCVaR criterion 

Authors  
Issue Date  2015 
Publisher  The University of Hong Kong (Pokfulam, Hong Kong) 
Citation  Zeng, Q. [曾慶培]. (2015). Stochastic supply chain network design under the meanCVaR criterion. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5610988 
Abstract  In the highly competitive market of the 21stcentury, organizations face the persistent challenge to improve supply chain efficiency, allowing products to move more quickly at lower cost. Unfortunately, some ostensible improvements also increase the supply chain’s vulnerability to uncertainties (e.g., uncertain customer demand and capacity), resulting in higher risk. Hence, there is a growing realization that the impact of risk to supply chains should be analyzed. Conditional Value at Risk (CVaR) is one of the newest method utilized to measure such risk.
This study develops a set of mathematical models to facilitate the analysis of the operating characteristics of a supply chain when its demand and capacity are uncertain involving three supply chain network design problems. The objective is to minimize the expected operating cost and its corresponding CVaR. The first problem focuses on facility location and distribution planning. The second problem extends the first one by incorporating procurement and manufacturing decisions on the basis of a timeexpanded network. In order to describe the situation where uncertainties are realized over time, the third problem extends the second one to a multistage (more than two stage).For each problem, both a basic model and a samplebased model are formulated. The basic model can be approximated accurately by the samplebased model of an adequate sample size.
The first samplebased model can be first reformulated to a setcovering problem, then decomposed into a master problem and a number of column generation pricingproblems. Then, a hybrid algorithm based upon Multipopulation Particle Swarm Optimization and Tabu Search (MPSOTS) is proposed to solve the pricingproblems. The computation time of MPSOTS rises proportionately with increases in sample size. Therefore, a tailormade simulation framework based on Sample Average Approximation (SAA) integrating the MPSOTS aided column generation algorithm is constructed to solve the basic model (also referred to the corresponding samplebased model of an adequate sample size). The effectiveness and robustness of the proposed methodologies are tested on a set of randomly generated problems.
In the second problem, the samplebased model can be decomposed into a master problem and a subproblem based on the Benders Decomposition (BD) scheme. To improve the convergence behavior of the standard BD algorithm, three accelerating strategies are utilized: adding logistical constraints, cutting disaggregation, and applying local branching (LB) strategy. Similar to the first problem, a tailormade simulation framework based on SAA integrating with the accelerated BD algorithm is constructed to solve the basic model. Results obtained from the experiments using randomly generated test problems show the effectiveness and efficiency of the proposed methodologies. Since the structure of the third samplebased model indicates that no exact solutions can be achieved, a corresponding model is developed under the mean criterion. Two simulation methods relying on a rolling horizon approach are proposed by using the accelerated BD algorithm to solve both these models. A lower bound analysis is also proposed to assist in the performance evaluation process. The superior effectiveness of these methodologies is validated by a set of randomly generated test problems. 
Degree  Doctor of Philosophy 
Subject  Management  Business logistics 
Dept/Program  Industrial and Manufacturing Systems Engineering 
Persistent Identifier  http://hdl.handle.net/10722/221209 
HKU Library Item ID  b5610988 
DC Field  Value  Language 

dc.contributor.author  Zeng, Qingpei   
dc.contributor.author  曾慶培   
dc.date.accessioned  20151104T23:12:00Z   
dc.date.available  20151104T23:12:00Z   
dc.date.issued  2015   
dc.identifier.citation  Zeng, Q. [曾慶培]. (2015). Stochastic supply chain network design under the meanCVaR criterion. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5610988   
dc.identifier.uri  http://hdl.handle.net/10722/221209   
dc.description.abstract  In the highly competitive market of the 21stcentury, organizations face the persistent challenge to improve supply chain efficiency, allowing products to move more quickly at lower cost. Unfortunately, some ostensible improvements also increase the supply chain’s vulnerability to uncertainties (e.g., uncertain customer demand and capacity), resulting in higher risk. Hence, there is a growing realization that the impact of risk to supply chains should be analyzed. Conditional Value at Risk (CVaR) is one of the newest method utilized to measure such risk. This study develops a set of mathematical models to facilitate the analysis of the operating characteristics of a supply chain when its demand and capacity are uncertain involving three supply chain network design problems. The objective is to minimize the expected operating cost and its corresponding CVaR. The first problem focuses on facility location and distribution planning. The second problem extends the first one by incorporating procurement and manufacturing decisions on the basis of a timeexpanded network. In order to describe the situation where uncertainties are realized over time, the third problem extends the second one to a multistage (more than two stage).For each problem, both a basic model and a samplebased model are formulated. The basic model can be approximated accurately by the samplebased model of an adequate sample size. The first samplebased model can be first reformulated to a setcovering problem, then decomposed into a master problem and a number of column generation pricingproblems. Then, a hybrid algorithm based upon Multipopulation Particle Swarm Optimization and Tabu Search (MPSOTS) is proposed to solve the pricingproblems. The computation time of MPSOTS rises proportionately with increases in sample size. Therefore, a tailormade simulation framework based on Sample Average Approximation (SAA) integrating the MPSOTS aided column generation algorithm is constructed to solve the basic model (also referred to the corresponding samplebased model of an adequate sample size). The effectiveness and robustness of the proposed methodologies are tested on a set of randomly generated problems. In the second problem, the samplebased model can be decomposed into a master problem and a subproblem based on the Benders Decomposition (BD) scheme. To improve the convergence behavior of the standard BD algorithm, three accelerating strategies are utilized: adding logistical constraints, cutting disaggregation, and applying local branching (LB) strategy. Similar to the first problem, a tailormade simulation framework based on SAA integrating with the accelerated BD algorithm is constructed to solve the basic model. Results obtained from the experiments using randomly generated test problems show the effectiveness and efficiency of the proposed methodologies. Since the structure of the third samplebased model indicates that no exact solutions can be achieved, a corresponding model is developed under the mean criterion. Two simulation methods relying on a rolling horizon approach are proposed by using the accelerated BD algorithm to solve both these models. A lower bound analysis is also proposed to assist in the performance evaluation process. The superior effectiveness of these methodologies is validated by a set of randomly generated test problems.   
dc.language  eng   
dc.publisher  The University of Hong Kong (Pokfulam, Hong Kong)   
dc.relation.ispartof  HKU Theses Online (HKUTO)   
dc.rights  The author retains all proprietary rights, (such as patent rights) and the right to use in future works.   
dc.rights  This work is licensed under a Creative Commons AttributionNonCommercialNoDerivatives 4.0 International License.   
dc.subject.lcsh  Management  Business logistics   
dc.title  Stochastic supply chain network design under the meanCVaR criterion   
dc.type  PG_Thesis   
dc.identifier.hkul  b5610988   
dc.description.thesisname  Doctor of Philosophy   
dc.description.thesislevel  Doctoral   
dc.description.thesisdiscipline  Industrial and Manufacturing Systems Engineering   
dc.description.nature  published_or_final_version   