Swarm intelligence algorithms for Yard Truck Scheduling and Storage Allocation Problems

• Published in: Neurocomputing (Amsterdam) Vol. 188; pp. 284 - 293
• Main Authors: Niu, Ben, Xie, Ting, Tan, Lijing, Bi, Ying, Wang, Zhengxu
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 05.05.2016
• Subjects: Algorithms; Allocations; Bacteria; Bacterial colony optimization; Container terminal operations; Mathematical models; Optimization; Particle swarm optimization; Scheduling; Storage Allocation; Swarm intelligence; Trucks; Yard Truck Scheduling; Container terminal operations; Bacterial colony optimization; Yard Truck Scheduling; Particle swarm optimization; Storage Allocation
• ISSN: 0925-2312; 1872-8286
• DOI: 10.1016/j.neucom.2014.12.125

In this paper we focus on two scheduling problems in container terminal: (i) the Yard Truck Scheduling Problem (YTSP) which assigns a fleet of trucks to transport containers between the QCs and the storage yard to minimize the makespan, (ii) the integrated Yard Truck Scheduling Problem and Storage Allocation Problem (YTS–SAP) which extends the first problem to consider storage allocation strategy for discharging containers. Its object is to minimize the total delay for all jobs. The second model is improved to consider the truck ready time. Due to the computational intractability, two recently developed solution methods, based on swarm intelligence technique, are developed for problem solution, namely, particle swarm optimization (PSO) and bacterial colony optimization (BCO). As these two algorithms are originally designed for continuous optimization problems, we proposed a particular mapping method to implement them for YTSP and YTS–SAP, both of which are discrete optimization problems. Through comparing the PSO algorithms and BCO algorithm with GA by an experiment conducted on different scale instances, we can draw a conclusion that LPSO perform best in YTSP while BCO perform best in YTS–SAP.