Industrial parks have played an indispensible role in boosting economic and industrial development. However, further development has been impeded by the shortage of land resources and by duplicate investments on common physical assets and services. This thesis proposes the concept of Supply Hub in Industrial Park (SHIP) as a public provider of warehousing and transportation services shared by manufacturing enterprises located within an industrial park. This research investigates four typical scenarios. The first scenario explores SHIP’s storage capacity pooling effect. Inventory models are built to simulate the supply chain in a typical industrial park: with and without SHIP. Under demand variations, two rules are presented for SHIP’s space allocation: the proportional dispatching rule (PDR), and the backorder cost priority dispatching rule (BPDR). The simulation results show that the industrial park can benefit from SHIP’s storage capacity pooling especially under complementarily seasonal or identically volatile demand pattern. BPDR is more beneficial than PDR to the industrial park, SHIP, and the manufacturer with the largest backorder cost rate. The second scenario evaluates SHIP’s value in consolidating shipments via numerically comparing the supply chain with SHIP with the traditional one. Genetic algorithm is used to solve the models developed for the two supply chains. Numerical outcomes show that the industrial park can obtain significant cost savings from applying SHIP. The cost reductions increase with the size of the supply chain, the vehicle capacity, and the rates of fixed transportation cost and holding costs of finished products at manufacturers. The third scenario investigates SHIP’s storage pricing strategies. A bilevel model is proposed to optimize SHIP’s decision on storage pricing, and individual manufacturer’s decisions on replenishment and delivery. A dynamic storage pricing depending on the storage length is adopted. The bilevel model is solved in closed-form in special cases. Through numerical experiments, SHIP is found to gain more profit by using the dynamic storage pricing than the constant one. It is observed that SHIP could not gain profit improvement by raising its delivery charge. Contrary to intuition, SHIP attracts more space demands from large production-scale manufacturers when charging higher on delivery or when the public warehouse’s delivery charge is lower, and more demands from small manufacturers when the market storage price is lower. The fourth scenario discusses how SHIP and manufacturers interact to optimize their decisions on shared transportation pricing and cycle-time, and raw material delivery schedules. A bilevel model is proposed to study this problem. The theoretical analysis of the model indicates that the SHIP’s storage price is the key in determining each partner’s behaviour. For comparison, a bilevel analytical model is constructed for direct transportation. The numerical outcomes show that SHIP’s profit may not always increase with the vehicle capacity. Manufacturers make more delivery orders when SHIP charges a significantly high storage price or the holding cost rate at manufacturers is significantly low. Sharing transportation service may bring benefits to SHIP and manufacturers with large demands. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Managing large scale supply chains are never an easy task. Numerous researches have put emphasis on supply chain modeling and optimization to assist businesses in searching for the best practices so as to endure the extremely competitive business landscape. To some, the paradigm of centralized supply chain management is adequate for solving its strategic and operational problems. Yet with the improper use of authoritative assumptions, the efficiency of the management process is often jeopardized. Furthermore, current researches in decentralized supply chain are mostly focused on dyadic or linear relationship and seldom consider quantitative modeling and analysis with scalability. Recent development in multi-agent systems provided a means for such a modeling methodology and hence researches in this area. To enhance model representativeness and computational efficiency, vision-based control models that are able to simulate individual operational and strategic traits are developed. In this research, pyramidal agent alignment is proposed for simulating the management-operation dimension with regards to decision exercising and bargaining power management. The system offers one thousand supply chain agents that are simulated in a mono-layer, multi-tier network in real time. Stochastic and dynamic behaviors of the network are handled by statistical regression on scenario-based model evaluation. The proposed design enabled grand scale supply chain modeling and optimization that follows a general or custom simulation supported optimization architecture. Network governance problems and dynamic steering problems are considered and solved using genetic algorithm and dynamic programming. The thesis looks into the potential benefits and limitations of the proposed methods in details, and future research directions are discussed. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

Thesis (M.S.)--Ohio University, August, 2002. / Title from PDF t.p.

Thesis (M. Phil.)--University of Hong Kong, 2008. / Also available in print.

Thesis (M.S.)--Ohio University, June, 2001. / Title from PDF t.p.

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2008. / Includes bibliographical references.

Thesis (Ph.D.)--Drexel University, 2008. / Includes abstract. Includes bibliographical references (leaves 141-148).

Thesis (Ph.D.)--Northwestern University, 2007. / Advisers: Mark S. Daskin; R. Canan Savaskan. Includes bibliographical references.

Thesis (Ph.D.)--Michigan State University. Dept. of Supply Chain Management, 2008. / Title from PDF t.p. (viewed on July 7, 2009) Includes bibliographical references (p. 146-150). Also issued in print.

Thesis (Ph. D.)--University of North Texas, Dec., 2009. / Title from title page display. Includes bibliographical references.