Multi-objective and prioritized berth allocation in container ports

This paper considers a berth allocation problem (BAP) which requires the determination of exact berthing times and positions of incoming ships in a container port. The problem is solved by optimizing the berth schedule so as to minimize concurrently the three objectives of makespan, waiting time, and degree of deviation from a predetermined priority schedule. These objectives represent the interests of both port and ship operators. Unlike most existing approaches in the literature which are single-objective-based, a multi-objective evolutionary algorithm (MOEA) that incorporates the concept of Pareto optimality is proposed for solving the multi-objective BAP. The MOEA is equipped with three primary features which are specifically designed to target the optimization of the three objectives. The features include a local search heuristic, a hybrid solution decoding scheme, and an optimal berth insertion procedure. The effects that each of these features has on the quality of berth schedules are studied.

[1]  Chung-lun Li,et al.  Scheduling with multiple-job-on-one-processor pattern , 1998 .

[2]  Akio Imai,et al.  Berth allocation planning in the public berth system by genetic algorithms , 2001, Eur. J. Oper. Res..

[3]  Kap Hwan Kim,et al.  Berth scheduling for container terminals by using a sub-gradient optimization technique , 2002, J. Oper. Res. Soc..

[4]  Akio Imai,et al.  Berth allocation with service priority , 2003 .

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

[6]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[7]  Kap Hwan Kim,et al.  Berth scheduling by simulated annealing , 2003 .

[8]  Andrew Lim,et al.  The berth planning problem , 1998, Oper. Res. Lett..

[9]  Dirk Thierens,et al.  The balance between proximity and diversity in multiobjective evolutionary algorithms , 2003, IEEE Trans. Evol. Comput..

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

[11]  Marco Laumanns,et al.  Performance assessment of multiobjective optimizers: an analysis and review , 2003, IEEE Trans. Evol. Comput..

[12]  Chung-Lun Li,et al.  A multiprocessor task scheduling model for berth allocation: heuristic and worst-case analysis , 2002, Oper. Res. Lett..

[13]  Nikos E. Mastorakis,et al.  Applications of genetic algorithms , 2009 .

[14]  Akio Imai,et al.  BERTH ALLOCATION IN A CONTAINER PORT: USING A CONTINUOUS LOCATION SPACE APPROACH , 2005 .

[15]  K. K. Lai,et al.  A STUDY OF CONTAINER BERTH ALLOCATION. , 1992 .

[16]  Carlos M. Fonseca,et al.  Multiobjective genetic algorithms with application to control engineering problems. , 1995 .

[17]  Akio Imai,et al.  Efficient planning of berth allocation for container terminals in Asia , 1997 .

[18]  Akio Imai,et al.  Berth allocation at indented berths for mega-containerships , 2007, Eur. J. Oper. Res..

[19]  Kay Chen Tan,et al.  Solving multiobjective vehicle routing problem with stochastic demand via evolutionary computation , 2007, Eur. J. Oper. Res..

[20]  Kay Chen Tan,et al.  Solving the Exam Timetabling Problem via a Multi-Objective Evolutionary Algorithm - A More General Approach , 2007, 2007 IEEE Symposium on Computational Intelligence in Scheduling.

[21]  Kap Hwan Kim,et al.  A scheduling method for Berth and Quay cranes , 2003 .

[22]  Lothar Thiele,et al.  Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach , 1999, IEEE Trans. Evol. Comput..

[23]  Gerald G. Brown,et al.  Optimizing Ship Berthing , 1994 .

[24]  Gerald G. Brown,et al.  Optimizing Submarine Berthing with a Persistence Incentive , 1997 .