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This book is focused on the impact of ocean transport logistics on global supply chains. It is the first book solely dedicated to the topic, linking the interaction of parties along this chain, including shippers, terminal operators and line carriers. While ocean container transport logistics has been greatly studied, there are many important issues that have yet to receive the attention they deserve. The editors and contributing authors of Ocean Container Transport Logistics: Making Global Supply Chain Effective seek to address these topics and shed new light on the subject.The book is divided into three parts. Part I examines the innovation, trends, competition and business model of container terminal operations. In Part II, the book looks at how tactical and operational management is used in shipping liners. The chapters cover topics such as empty container repositioning, slow steaming, routing, network design and disruption management. Finally Part III explores at shippers and global supply chain management, with chapters on transportation service procurement, hinterland transportation, green corridors, as well as competition and co-operation in maritime logistics operations. The eighteen chapters of the book all highlight the immediate effect of ocean transport logistics on global supply chain. Prof. Chung-Yee Leeis Chair Professor/Cheong Ying Chan Professor of Engineering, Department of Industrial Engineering Logistics Management (IELM) and Director of the Logistics and Supply Chain Management Institute at The Hong Kong University of Science and Technology. He served as Department Head of IELM in 2001-2008. He is a Fellow of the Institute of Industrial Engineers and Hong Kong Academy of Engineering Science. Before joining HKUST in 2001, he was Rockwell Professor in the Department of Industrial Engineering at Texas AM University. His research areas are in logistics and supply chain management, scheduling, and inventory management. He has published more than 130 papers in refereed journals and has engaged in more than 40 research projects sponsored by RGC, ITC, NSF and industries in U.S. and Hong Kong. According to an article in Int. J. of Prod. Econ.(v. 120, 2009, p.540-551), which looked at all papers published in the 20 core journals during last 50 years in the field of production and operations management, Prof. Lee was ranked No.6 among all researchers worldwide in h-index. He is currently leading a theme-based research project on 'Promoting Hong Kong's Ocean Container Transport Logistics Network' sponsored by Hong Kong Research Grant Council.He received his Bachelor degree in Electronic Engineering (1972) and a Master degree in Management Sciences (MBA Program) (1976) from National Chiao-Tung University in Taiwan. He also received a Master degree in Industrial Engineering from Northwestern University in 1980 and his Ph.D. degree in Operations Research from Yale University in 1984.Dr. Qiang Meng is currently an Associate Professor in the Department of Civil and Environmental Engineering at National University of Singapore. He received his PhD in the Department of Civil Engineering at the Hong Kong University of Science Technology in 2000. Dr. Meng has three major research areas transportation network modeling and optimization, shipping and intermodal freight transportation analysis and quantitative risk assessment of transport operations. He has authored or co-authored about 100 articles in the international referred journals including Transportation B, Transportation Science, Transportation Research Part E, and Risk Analysis Accident. He has led two joint research projects with a liner container shipping company and a maritime consultant company in Singapore.Dr. Meng is now an Associate Editor of Transportation Research Part Eand Journal of Transportation Engineering (ASCE), an editorial board member of Transportation Research Part B, Transportation Research Part C, Journal of Advanced Transportation, Transportmetrica, Asian Transport Studies and Analytical Methods in Accident Research. He has been serving Scientific Committee of the World Conference on Transportation Research (WCTR) Society, Freight Transportation Planning and Logistics Committee of Transportation Research Board (TRB). He has clinched a number of research awards and prizes, including 13thWorld Conference on Transportation Research (WCTR) Society Prize for the best paper (2013), Best Paper Award for Methodological Development in the 9thEASTS (East Asia Society for Transportation Studies) International Conference (2011), Best Paper Award of AHB40 - Highway Capacity and Quality of Service Committee - in the 90thTRB Annual Meeting (2011), and Singapore MOT (Ministry of Transportation) Minister's Innovation Award 2009.

AbstractThis article studies the inventory competition under yield uncertainty. Two firms with random yield compete for substitutable demand: If one firm suffers a stockout, which can be caused by yield failure, its unsatisfied customers may switch to its competitor. We first study the case in which two competing firms decide order quantities based on the exogenous reliability levels. The results from the traditional inventory competition are generalized to the case with yield uncertainty and we find that quantity and reliability can be complementary instruments in the competition. Furthermore, we allow the firms to endogenously improve their yield reliability before competing in quantity. We show that the reliability game is submodular under some assumptions. The results indicate that the competition in quantity can discourage the reliability improvement. With an extensive numerical study, we also demonstrate the robustness of our analytical results in more general settings. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 107–126, 2015

AbstractThis paper considers a new class of scheduling problems arising in logistics systems in which two different transportation modes are available at the stage of product delivery. The mode with the shorter transportation time charges a higher cost. Each job ordered by the customer is first processed in the manufacturing facility and then transported to the customer. There is a due date for each job to arrive to the customer. Our approach integrates the machine scheduling problem in the manufacturing stage with the transportation mode selection problem in the delivery stage to achieve the global maximum benefit. In addition to studying the NP‐hard special case in which no tardy job is allowed, we consider in detail the problem when minimizing the sum of the total transportation cost and the total weighted tardiness cost is the objective. We provide a branch and bound algorithm with two different lower bounds. The effectiveness of the two lower bounds is discussed and compared. We also provide a mathematical model that is solvable by CPLEX. Computational results show that our branch and bound algorithm is more efficient than CPLEX. © 2005 Wiley Periodicals, Inc. Naval Research Logistics, 2005

AbstractIn this paper, we present an optimization model for coordinating inventory and transportation decisions at an outbound distribution warehouse that serves a group of customers located in a given market area. For the practical problems which motivated this paper, the warehouse is operated by a third party logistics provider. However, the models developed here may be applicable in a more general context where outbound distribution is managed by another supply chain member, e.g., a manufacturer. We consider the case where the aggregate demand of the market area is constant and known per period (e.g., per day). Under an immediate delivery policy, an outbound shipment is released each time a demand is realized (e.g., on a daily basis). On the other hand, if these shipments are consolidated over time, then larger (hence more economical) outbound freight quantities can be dispatched. In this case, the physical inventory requirements at the third party warehouse (TPW) are determined by the consolidated freight quantities. Thus, stock replenishment and outbound shipment release policies should be coordinated. By optimizing inventory and freight consolidation decisions simultaneously, we compute the parameters of an integrated inventory/outbound transportation policy. These parameters determine: (i) how often to dispatch a truck so that transportation scale economies are realized and timely delivery requirements are met, and (ii) how often, and in what quantities, the stock should be replenished at the TPW. We prove that the optimal shipment release timing policy is nonstationary, and we present algorithms for computing the policy parameters for both the uncapacitated and finite cargo capacity problems. The model presented in this study is considerably different from the existing inventory/transportation models in the literature. The classical inventory literature assumes that demands should be satisfied as they arrive so that outbound shipment costs are sunk costs, or else these costs are covered by the customer. Hence, the classical literature does not model outbound transportation costs. However, if a freight consolidation policy is in place then the outbound transportation costs can no longer be ignored in optimization. Relying on this observation, this paper models outbound transportation costs, freight consolidation decisions, and cargo capacity constraints explicitly. © 2002 Wiley Periodicals, Inc. Naval Research Logistics 49: 531–556, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/nav.10030

AbstractThe container relocation problem (CRP) is concerned with emptying a single yard‐bay which contains J containers each following a given pickup order so as to minimize the total number of relocations made during their retrieval process. The CRP can be modeled as a binary integer programming (IP) problem and is known to be NP‐hard. In this work, we focus on an extension of the CRP to the case where containers are both received and retrieved from a single yard‐bay, and call it the dynamic container relocation problem. The arrival (departure) sequences of containers to (from) the yard‐bay is assumed to be known a priori. A binary IP formulation is presented for the problem. Then, we propose three types of heuristic methods: index based heuristics, heuristics using the binary IP formulation, and a beam search heuristic. Computational experiments are performed on an extensive set of randomly generated test instances. Our results show that beam search heuristic is very efficient and performs better than the other heuristic methods.Copyright © 2014 Wiley Periodicals, Inc. Naval Research Logistics 61: 101–118, 2014

AbstractIn this article we introduce a 2‐machine flowshop with processing flexibility. Two processing modes are available for each task: namely, processing by the designated processor, and processing simultaneously by both processors. The objective studied is makespan minimization. This production environment is encountered in repetitive manufacturing shops equipped with processors that have the flexibility to execute orders either individually or in coordination. In the latter case, the product designer exploits processing synergies between two processors so as to execute a particular task much faster than a dedicated processor. This type of flowshop environment is also encountered in labor‐intensive assembly lines where products moving downstream can be processed either in the designated assembly stations or by pulling together the work teams of adjacent stations. This scheduling problem requires determining the mode of operation of each task, and the subsequent scheduling that preserves the flowshop constraints. We show that the problem is ordinary NP‐complete and obtain an optimal solution using a dynamic programming algorithm with considerable computational requirements for medium and large problems. Then, we present a number of dynamic programming relaxations and analyze their worst‐case error performance. Finally, we present a polynomial time heuristic with worst‐case error performance comparable to that of the dynamic programming relaxations. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2004.

AbstractThis paper studies a scheduling problem arising in a beef distribution system where pallets of various types of beef products in the warehouse are first depalletized and then individual cases are loaded via conveyors to the trucks which deliver beef products to various customers. Given each customer's demand for each type of beef, the problem is to find a depalletizing and truck loading schedule that fills all the demands at a minimum total cost. We first show that the general problem where there are multiple trucks and each truck covers multiple customers is strongly NP‐hard. Then we propose polynomial‐time algorithms for the case where there are multiple trucks, each covering only one customer, and the case where there is only one truck covering multiple customers. We also develop an optimal dynamic programming algorithm and a heuristic for solving the general problem. By comparing to the optimal solutions generated by the dynamic programming algorithm, the heuristic is shown to be capable of generating near optimal solutions quickly. © 2003 Wiley Periodicals, Inc. Naval Research Logistics, 2003

AbstractIn this article, we consider a loss‐averse newsvendor with stochastic demand. The newsvendor might procure options when demand is unknown, and decide how many options to execute only after demand is revealed. If the newsvendor reserves too many options, he would incur high reservation costs. Yet reserving too few could result in lost sales. So the newsvendor faces a trade‐off between reservation costs and losing sales. When there are multiple options available, the newsvendor has to consider how many units of each to reserve by studying the trade‐off between flexibility and costs. We show how the newsvendor's loss aversion behavior affects his ordering decision, and propose an efficient algorithm to compute his optimal solution in the general case with n options. We also present examples showing how the newsvendor's ordering strategy changes as loss aversion rises. © 2014 Wiley Periodicals, Inc. 62:46–59, 2015

AbstractThis article studies the optimal control of a periodic‐review make‐to‐stock system with limited production capacity and multiple demand classes. In this system, a single product is produced to fulfill several classes of demands. The manager has to make the production and inventory allocation decisions. His objective is to minimize the expected total discounted cost. The production decision is made at the beginning of each period and determines the amount of products to be produced. The inventory allocation decision is made after receiving the random demands and determines the amount of demands to be satisfied. A modified base stock policy is shown to be optimal for production, and a multi‐level rationing policy is shown to be optimal for inventory allocation. Then a heuristic algorithm is proposed to approximate the optimal policy. The numerical studies show that the heuristic algorithm is very effective. © 2011 Wiley Periodicals, Inc. Naval Research Logistics 58: 43–58, 2011