Multi-objective bacterial colony optimization algorithm for integrated container terminal scheduling problem

This paper proposes a multi-objective integrated container terminal scheduling problem considering three key components: berth allocation, quay cranes assignment and containers transportation in port operation process. In the suggested problem, one of the objectives is to shorten service time of ships with by coordinating of quay cranes, and the other is to reduce operating costs of quay cranes and yard trucks. Then, a Multi-objective Bacterial Colony Optimization algorithm (MOBCO) incorporating concepts of multi-swarm, topology, personal best and global best, named Multi-objective BCO with ring topology (MORBCO), is designed to handle the resulting problem. The extension of standard MOBCO to the MORBCO involves the addition of three specialized strategies: global chemotaxis operation, elite reproduction strategy and personal best archive with neighborhood communication mechanism. In order to test the performance of the MORBCO, benchmark tests are performed and compared with traditional MOBCO and three other well-known multi-objective algorithms first. The computational results indicate that the proposed algorithm can outperform other rivals and efficiently solve a variety of multi-objective problems in most of cases. Subsequently, MORBCO and two best performing algorithms from the previous test are applied to three instances generated by the proposed model. Judging by quality and diversity of obtained non-dominant solutions, we find that MORBCO has superior performance, especially for large instances of the container terminal problem.

[1]  Carlos F. Daganzo,et al.  THE CRANE SCHEDULING PROBLEM , 1989 .

[2]  Gilbert Laporte,et al.  Models and Tabu Search Heuristics for the Berth-Allocation Problem , 2005, Transp. Sci..

[3]  Nathan Huynh,et al.  Integrated quay crane and yard truck scheduling for unloading inbound containers , 2015 .

[4]  Ben Niu,et al.  Bacterial Colony Optimization for Integrated Yard Truck Scheduling and Storage Allocation Problem , 2014, ICIC.

[5]  Lixin Miao,et al.  A bi-objective robust model for berth allocation scheduling under uncertainty , 2017 .

[6]  Li-feng Xi,et al.  A proactive approach for simultaneous berth and quay crane scheduling problem with stochastic arrival and handling time , 2010, Eur. J. Oper. Res..

[7]  Ching-Jung Ting,et al.  Particle swarm optimization algorithm for the berth allocation problem , 2014, Expert Syst. Appl..

[8]  Lothar Thiele,et al.  Comparison of Multiobjective Evolutionary Algorithms: Empirical Results , 2000, Evolutionary Computation.

[9]  Lifeng Xi,et al.  A tabu search algorithm for the integrated scheduling problem of container handling systems in a maritime terminal , 2007, Eur. J. Oper. Res..

[10]  Ebru K. Bish,et al.  A multiple-crane-constrained scheduling problem in a container terminal , 2003, Eur. J. Oper. Res..

[11]  Akio Imai,et al.  The Dynamic Berth Allocation Problem for a Container Port , 2001 .

[12]  Kap Hwan Kim,et al.  A crane scheduling method for port container terminals , 2004, Eur. J. Oper. Res..

[13]  Hu Zhi Berth and crane allocation problem based on cost analysis of quay cranes for container terminal , 2013 .

[14]  Christian Bierwirth,et al.  A fast heuristic for quay crane scheduling with interference constraints , 2009, J. Sched..

[15]  Ruiyou Zhang,et al.  Heuristic-based truck scheduling for inland container transportation , 2010, OR Spectr..

[16]  Frank Kursawe,et al.  A Variant of Evolution Strategies for Vector Optimization , 1990, PPSN.

[17]  Akio Imai,et al.  Yard trailer routing at a maritime container terminal , 2005 .

[18]  Ponnuthurai Nagaratnam Suganthan,et al.  Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems: Research Articles , 2006 .

[19]  Akio Imai,et al.  The simultaneous berth and quay crane allocation problem , 2008 .

[20]  Peter J. Fleming,et al.  Multiobjective optimization and multiple constraint handling with evolutionary algorithms. II. Application example , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[21]  Rick Siow Mong Goh,et al.  Multi-objective optimization of large scale berth allocation and quay crane assignment problems , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[22]  Youfang Huang,et al.  A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning , 2009, Comput. Ind. Eng..

[23]  Mihalis M. Golias,et al.  Berth scheduling by customer service differentiation: A multi-objective approach , 2009 .

[24]  Ben Niu,et al.  Bacterial Colony Optimization: Principles and Foundations , 2012, ICIC.

[25]  Yang Yang,et al.  Multi-objective hybrid genetic algorithm for quay crane dynamic assignment in berth allocation planning , 2011, J. Intell. Manuf..

[26]  Kap Hwan Kim,et al.  A routing algorithm for a single transfer crane to load export containers onto a containership , 1997 .

[27]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[28]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for solving multiobjective optimization problems , 2006, Int. J. Intell. Syst..

[29]  Carlos A. Coello Coello,et al.  Handling multiple objectives with particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[30]  Changchun Liu,et al.  Reactive strategy for discrete berth allocation and quay crane assignment problems under uncertainty , 2018, Comput. Ind. Eng..