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University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2013 Modeling and Optimization of Resource Allocation in Supply Chain Management Problems Qi Yuan [email protected] Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Industrial Engineering Commons, and the Operational Research Commons Recommended Citation Yuan, Qi, "Modeling and Optimization of Resource Allocation in Supply Chain Management Problems. " PhD diss., University of Tennessee, 2013. https://trace.tennessee.edu/utk_graddiss/1800 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Qi Yuan entitled "Modeling and Optimization of Resource Allocation in Supply Chain Management Problems." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the equirr ements for the degree of Doctor of Philosophy, with a major in Industrial Engineering. Xiaoyan Zhu, Major Professor We have read this dissertation and recommend its acceptance: Xueping Li, Joseph Wilck, Frank M. Guess Accepted for the Council: Carolyn R. Hodges Vice Provost and Dean of the Graduate School (Original signatures are on file with official studentecor r ds.) Modeling and Optimization of Resource Allocation in Supply Chain Management Problems A Dissertation Presented for the Doctor of Philosophy Degree The University of Tennessee, Knoxville Qi Yuan May 2013 c by Qi Yuan, 2013 All Rights Reserved. ii To my dear parents and Huimin. iii Acknowledgements First of all, I would like to express my deepest gratitude for the support and guidance of my major advisor Dr. Xiaoyan Zhu. I benefit a lot from her meticulous scholarship, straightforward personality and thoughtful kindness. Also, I am indebted to Dr. Xueping Li for his support all these years. I appreciate Dr. Joe Wick's tremendous help for providing academic and industry related information. Special thanks go to Dr. Frank Guess, who always encourages me and gives me the confidence to do better. I would also like to mention Dr. Alberto Garcia-Diaz, whose wonderful courses have brought me to a new horizon. It has been a lifetime honor to learn from these great professors. I am also thankful to Dr. Fong-Yuen Ding, Dr. Qingzhu, Dr. Yuerong Chen, Mr. Caiqiao Xu, Dr. Gewei Zhang, Dr. Laigang Song, Dr. Dengfeng Yang, Dr. Yan Chen, Dr. Zhe Zhang, Dr. Liang Dong, Mr. Cong Guo, and Ms. Zhaoxia Zhao. Thanks to all of you for your time and encouragement, as well as your willingness to share. You have made my adventure an easy journey. Finally, it is hard to express in words the deepest love for my parents, who brought me up and always take care of me along the way. I would also like to thank Ms. Huimin Zhou, the most beautiful lady in the world, for making my life complete. iv \You've got to find what you love." { Steve Jobs v Abstract Resource allocation in supply chain management studies how to allocate the limited available resources economically/optimally to satisfy the demands. It is an important research area in operations research. This dissertation focuses on the modeling and optimization of three problems. The first part of the dissertation investigates an important and unique problem in a supply chain distribution network, namely minimum cost network flow with variable lower bounds (MCNF-VLB). This type of network can be used to optimize the utilization of distribution channels (i.e., resources) in a large supply network, in order to minimize the total cost while satisfying flow conservation, lower and upper bounds, and demand/supply constraints. The second part of the dissertation introduces a novel method adopted from multi-product inventory control to optimally allocate the cache space and the frequency (i.e., resources) for multi-stream data prefetching in computer science. The objective is to minimize the cache miss level (backorder level), while satisfying the cache space (inventory space) and the total prefetching frequency (total order frequency) constraints. Also, efforts have also been made to extend the model for a multi-level, multi-stream prefetching system. The third part of the dissertation studies the joint capacity (i.e., resources) and demand allocation problem vi in a service delivery network. The objective is to minimize the total cost while satisfying a required service reliability, which measures the probability of satisfying customer demand within a delivery time interval. vii Contents 1 Introduction and Research Topics1 1.1 Introduction to resource allocation in supply chain management.......1 1.2 Three topics in this research direction......................2 1.2.1 The MCNF-VLB problem........................2 1.2.2 The data prefecthing problem......................3 1.2.3 The joint capacity and demand allocation problem..........4 1.3 Structure of the dissertation...........................5 2 Resource Allocation in Supply Chain Distribution Network: the MCNF- VLB Problem6 2.1 Literature review on the MCNF problem and the MCNF-VLB problem...6 2.1.1 Existing MCNF problems........................7 2.1.2 Extension to the MCNF-VLB problem................. 10 2.2 Mixed integer linear programming (MILP) models............... 11 2.2.1 The MCNF-VLB model......................... 11 2.2.2 A numerical example........................... 15 2.3 Computational testing with CPLEX....................... 18 viii 2.3.1 Test instances............................... 19 2.3.2 Computational experiments....................... 21 2.3.3 Computational results.......................... 24 2.3.4 Computational tests on the effects of demand tightness........ 27 2.4 Summary for the MCNF-VLB problem..................... 28 3 Resource Allocation in Data Prefetching: from a Supply Chain Modeling Perspective 30 3.1 Introduction to data prefetching problem and (Q; r) models in supply chain management.................................... 30 3.2 Literature review and problem mapping..................... 34 3.2.1 Data cache management and prefetching................ 34 3.2.2 Inventory management and (Q; r) models................ 35 3.2.3 Connections between data prefetching and inventory management.. 38 3.3 Constrained multi-stream (Q; r) models and optimization........... 40 3.3.1 Formulas.................................. 41 3.3.2 Constrained multi-stream (Q; r) model................. 46 3.3.3 Optimal solutions............................. 47 3.3.4 Modified model for numerical computations.............. 49 3.4 Numerical optimization results.......................... 50 3.4.1 Regression results on disk access time.................. 52 3.4.2 Numerical examples........................... 53 3.4.3 Sensitivity analysis............................ 56 ix 3.5 Table-based online method............................ 63 3.6 Summary for multi-stream data prefetching problem.............. 65 3.7 Multi-level, multi-stream (Q; r) model and optimization for data prefetching 65 3.7.1 Multi-level, multi-stream prefetching problem mapping........ 66 3.7.2 Notation and formulas for multi-level, multi-stream prefetching.... 67 3.7.3 Constrained multi-level, multi-stream (Q; r) model.......... 71 3.7.4 Summary for multi-level, multi-stream prefetching problem...... 72 4 Resource Allocation for a Service Delivery Network in Supply Chain Management 73 4.1 Introduction.................................... 73 4.2 Model formulation................................ 76 4.2.1 Notation.................................. 76 4.2.2 Demand allocation............................ 77 4.2.3 Service reliability............................. 78 4.2.4 Average waiting time........................... 79 4.2.5 Joint capacity and demand allocation model.............. 80 4.3 Properties of the model and numerical optimization.............. 81 4.3.1 Numerical optimization examples.................... 81 4.4 Sensitivity analysis................................ 85 4.4.1 Effect of the total demand rate..................... 85 4.4.2 Effect of the delivery time interval.................... 86 4.4.3 Effect of the required service reliability................. 86 x 4.5 Model extensions................................. 87 4.5.1 Extension one: maximum service reliability............... 88 4.5.2 Extension two: limited total capacity.................. 89 4.5.3 Extension three: delay before service.................. 89 4.5.4 Extension four: other demand distributions.............. 90 4.5.5 Extension five: different customer priorities............... 91 4.6 Summary for the service delivery network problem............... 92 5 Conclusions and Future Research 93 5.1 Summary..................................... 93 5.2 Potential applications............................... 94 5.3 Future research.................................. 94 Bibliography 96 Appendix 107 A.1 Proofs of propositions1 to5........................... 108 A.2 The KKT and second order sufficient conditions................ 110 Vita 114 xi List of Tables 2.1 Computational results for the MCNF and MCNF-VLB examples....... 18 2.2 Results for solving loose-bound instances with demand tightness ρ = 0:2... 23 2.3 Results for solving tight-bound instances
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