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Multi-period yard template planning in container terminals
Institution:1. School of Management, Shanghai University, Shanghai, China;2. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;3. Department of Industrial and Systems Engineering, National University of Singapore, Singapore;4. Logistics Research Center, Shanghai Maritime University, Shanghai, China;1. Sauder School of Business, University of British Columbia, Canada\n;2. Department of Decision Sciences, College of Business, San Francisco State University, United States;1. Strome College of Business, Old Dominion University, Norfolk, VA 23529, USA;2. School of Management, Shanghai University, Shanghai, 200444, China;1. Department of Logistics and Maritime Studies, The Hong Kong Polytechnic University, Kowloon, Hong Kong;2. School of Management, Shanghai University, Shang Da Road 99, Shanghai 200444, China;3. School of Civil and Environmental Engineering, University of Technology Sydney, Sydney, NSW 2007, Australia
Abstract:This paper is about yard management in container ports. As a tactical level decision-making tool in a port, a yard template determines the assignment of spaces (subblocks) in a yard for arriving vessels, which visit the port periodically. The objective of yard template planning is to minimize the transportation cost of moving containers around the yard. To handle yard template planning, a mixed integer programming model is proposed that also takes into account traffic congestion in the yard. A further complication is that the cycle time of the vessels' periodicities is not uniform and varies among them, perhaps being one week, ten days, or two weeks, etc. However, this multiple cycle time of the periodicities of vessel arrival patterns, which complicates the yard template decision, is also considered in the model. Moreover, a local branching based solution method and a Particle Swarm Optimization based solution method are developed for solving the model. Numerical experiments are also conducted to validate the effectiveness of the proposed model, which can save around 24% of the transportation costs of yard trucks when compared with the commonly used First-Come-First-Served decision rule. Moreover, the proposed solution methods can not only solve the proposed model within a reasonable time, but also obtain near-optimal results with about 0.1–2% relative gap.
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