Adaptive particle swarm optimization for integrated quay crane and yard truck scheduling problem

• Published in: Computers & industrial engineering Vol. 153; p. 107075
• Main Authors: Hop, Dang Cong, Van Hop, Nguyen, Anh, Truong Tran Mai
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.03.2021
• Subjects: Particle Swarm Optimization (PSO); Quay crane; Scheduling; Yard truck; Scheduling; Yard truck; Particle Swarm Optimization (PSO); Quay crane
• ISSN: 0360-8352; 1879-0550
• DOI: 10.1016/j.cie.2020.107075

•This paper tackles the integrated quay crane and yard truck scheduling problem.•A new MIP model is formulated with two more conditions on the handled containers.•An APSO algorithm with all automatically adjusted parameters is proposed.•The proposed APSO gives closed optimal solutions for small sized problems.•It also gives better solution than other metaheuristic approaches for large sized problems. This study takes into account the integrated quay crane and yard truck scheduling problem in which the yard truck picks containers at quay crane and then transports required containers to the container yard then return to the quay crane without carrying exported containers. A new mixed – integer programming model is formulated to capture two more conditions on the number of containers to be handled by quay crane and yard truck at a time. The objective is to minimize total time to complete the unloading and transporting operations for all required containers. Moreover, an Adaptive Particle Swarm Optimization (APSO) algorithm with all automatically adjusted parameters of inertia weight, cognitive coefficient and social coefficient is developed to search for better solutions. The proposed APSO gives closed optimal solutions obtained from the mixed – integer program. It also gives better performance than similar metaheuristic approaches such as Fixed Particle Swarm Optimization (FPSO) and Grey Wolf Optimization (GWO) for large sized problems in reasonable time.