The Berth-Quay Cranes and Trucks Scheduling Optimization Problem by Hybrid Intelligence Swam Algorithm

The Berth-Quay Cranes and Trucks Scheduling Optimization Problem by Hybrid Intelligence Swam Algorithm

Yi Liu (Hangzhou Dianzi University, Management School, Hangzhou, China) and Sabina Shahbazzade (University of California, Electrical Engineering and Computer Sciences, Berkeley, CA, USA)
DOI: 10.4018/IJCINI.2017040105
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Abstract

Considered the cooperation of the container truck and quayside container crane in the container terminal, this paper constructs the model of the quay cranes operation and trucks scheduling problem in the container terminal. And the hybrid intelligence swarm algorithm combined the particle swarm optimization algorithm(PSO) with artificial fish swarm algorithm (AFSA) was proposed. The hybrid algorithm (PSO-AFSA) adopt the particle swarm optimization algorithm to produce diverse original paths, optimization of the choice nodes set of the problem, use AFSA's preying and chasing behavior improved the ability of PSO to avoid being premature. The proposed algorithm has more effectiveness, quick convergence and feasibility in solving the problem. The results of stimulation show that the scheduling operation efficiency of container terminal is improved and optimized.
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Introduction

The port container terminal mainly is composed by container ships, loading bridges, container trucks, etc. The container truck is responsible for the transportation between the wharf and container yard in container yard, which its efficiency directly affects the working time of the transportation and storage tasks. The whole dock operation should be coordinated and the handling time of berthing ships should shorten as far as possible, in order to improve the service quality and efficiency of the container terminal. The scheduling problem of the quay cranes operation and yard trucks is a key point to improve the operation efficiency of container terminals. At the same time, the quay cranes operation and yard trucks scheduling problem in the container terminal is a NP-hard problem. So, it has the extremely vital significance to research how to optimization this problem.

The container truck and quay crane scheduling problem has been widely studied. Kim (1999) study on the optimization problem of loading forklift and field bridge taking box path, using integer programming method to make modeling and solving (Kim, 1999). Ebru et al. (2001) established the dispatch model of the container port truck vehicle, and proposes a heuristic algorithm to make the ship stay in the port at the shortest time. Etsuko Nishimura and Akio Imai (2005) proposed the truck dispatching method of container terminal under the “dynamic path”, and defined the cooperation of the “crane → yard → crane” as the truck operation route, and proposed the truck scheduling method of container terminal under the “dynamic path” by heuristic algorithm. Bish (2005) focused on the truck scheduling and storage allocation problem but did not consider the travel time and waiting time of trucks explicitly. In practice, during peak hours, trucks need to wait for an idle yard crane to unload the containers from them, after which they can proceed to new jobs. Nishimura (2005) established the optimization model of truck dynamic path using genetic algorithm to solve the model by compared static and dynamic scheduling method of truck. Ming Jun Ji and Zhi Hong Le (2007) established the truck route optimization model considered the shortest crane operation time and truck transport time, to research truck scheduling problem under the loading and unloading container operations at the same time. Mauro (2007) studied the truck routing problem with time window constraints and proposed the heuristic algorithm in detailed.

In this paper, particle swarm optimization algorithm and artificial fish school algorithm are combined to solve the quay cranes operation and yard trucks scheduling combined problem. In the outer cycle, the particle swarm optimization algorithm to analysis the crane scheduling problem, while in the inner cycle, the artificial fish school algorithm is used to find the global optimum of truck routing problem.

This paper is organized as follows. Section 1 gives background and related work of container truck routing problems in container Terminal. Section 2 describe the container truck scheduling problem and presents its corresponding mixed integer programming model. Section 3 presents the PSO–AFSA hybrid algorithm, and demonstrates how it solves the container truck scheduling problem. Section 4 provides computational experiments and the global optimum analysis of the PSO-AFSA algorithms in detail.

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