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- Title
基于计算物流和群集智能的多集装箱码头泊位分配.
- Authors
李 斌; 唐志斌
- Abstract
Based on the integration and synergy of multiple container terminal operating space resources by port operators, this paper makes an in- depth discussion on the multi- terminal dynamic and continuous berth allocation problem (MDC-BAP) by considering berth depth constraint and export container transferable operations. The MDC-BAP is abstracted as a heterogeneous multiple knapsack problem for operation modelling by computational logistics, and then a mixed integer linear programming model is established to minimize the total running cost of both sides of port and shipping. Subsequently, a kind of two- stage improved imperialist competitive algorithm (TSI- ICA) is designed to solve the MDC-BAP model by the integration of computational logistics and swarm intelligence. Finally, the numerical experiments of twelve large-scale MDC-BAP examples in three planning periods are executed intensively, and the comprehensive solving performance of diversiform improved imperialist competitive algorithms and multifarious heuristic rules on the MDC-BAP model are compared and analyzed. The computing framework of“meta-heuristic algorithm + heuristic rule”designed by TSI-ICA is obviously superior to the resource allocation mode of“heuristic rule + heuristic rule”on large-scale examples. Moreover, it illustrates that multi-terminal cooperative production is superior to single-terminal independent service mode from two aspects of operating costs and running toughness. Consequently, it provides a favorable intelligent decision support solution for collaborative berth allocation of multiple container terminals.
- Subjects
MIXED integer linear programming; METAHEURISTIC algorithms; CONTAINER terminals; KNAPSACK problems; SWARM intelligence; EXTRATERRESTRIAL resources; IMPERIALIST competitive algorithm
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 16, p262
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2304-0278