Particle swarm optimization for just-in-time trucks scheduling in cross docking terminals
© IEOM Society International. In this paper, we present an application of Particle Swarm Optimization (PSO) for solving truck scheduling problem in a cross docking system in the content of just-in-time concept The objective is to find the schedule of inbound and outbound trucks that minimize the tot...
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| Main Authors: | , |
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| Format: | Conference Proceeding |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85018393600&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/55333 |
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| Institution: | Chiang Mai University |
| Summary: | © IEOM Society International. In this paper, we present an application of Particle Swarm Optimization (PSO) for solving truck scheduling problem in a cross docking system in the content of just-in-time concept The objective is to find the schedule of inbound and outbound trucks that minimize the total earliness and the total tardiness simultaneously. The mathematical model is first presented as a mixed integer programming (MIP) model and LINGO optimization solver is then used to find the optimal solution. Due to the limitation of LINGO to obtain only one single solution related to one objective at a time, it requires additional runs to get a solution in the other objective aspect. Moreover, when the problem size becomes very large, LINGO cannot find solutions in an acceptable time. Consequently, we apply a multi-objective particle swarm optimization (MOPSO) to find a set of truck schedules with minimum total earliness and total tardiness. The performances of MOPSO are evaluated using 20 generated instances and compared with those obtained from multi-objective Differential Evolution (MODE). The experimental results demonstrate that both MOPSO and MODE are capable of finding a set of diverse and high quality non-dominated solutions with reasonable computing time. © IEOM Society International. |
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