Model and heuristic for berth allocation in tidal bulk ports with stock level constraints

We consider the problem of allocating berth positions for vessels in tidal bulk port terminals. A berth is defined as a specific location alongside a quay where a ship loader is available for loading or unloading vessels, accommodating only one vessel at a time. In tidal ports, draft conditions depend on high tide conditions, since available depth at low tide is not adequate for the movement of ships. Some port terminals are associated with important transnational enterprises which maintain strong control over the stock level of their goods. Since the stock level sometimes depends on a continuous process of consumption or production of minerals, the decision to load or unload vessels must consider the amount of the bulk cargo stored in the port yards. Therefore, a basic criterion for decision making is to give priority to the vessels related to the most critical mineral stock level. A second basic criterion is to decide what sequence of vessels reduces the overall demurrage within a given planning horizon. This paper presents an integer linear programming model based on the transportation problem to represent the Berth Allocation Problem in Tidal Bulk ports with Stock level conditions (BAPTBS). Problem instances are solved by a commercial solver and by a Simulated Annealing-based algorithm (SA). The SA employs a problem-specific heuristic, becoming a valid alternative for finding out good solutions for difficult instances.

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

[2]  Hannu Ahonen,et al.  A multi-mode resource-constrained scheduling problem in the context of port operations , 2006, Comput. Ind. Eng..

[3]  Pierre Hansen,et al.  Variable neighborhood search for minimum cost berth allocation , 2003, Eur. J. Oper. Res..

[4]  C. D. Gelatt,et al.  Optimization by Simulated Annealing , 1983, Science.

[5]  Kenneth Steiglitz,et al.  Combinatorial Optimization: Algorithms and Complexity , 1981 .

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

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

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

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

[10]  Akio Imai,et al.  Berthing ships at a multi-user container terminal with a limited quay capacity , 2008 .

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

[12]  西村 悦子,et al.  Berth Allocation Planning in the Public Berth System by Genetic Algorithms , 2000 .

[13]  Kap Hwan Kim,et al.  Container terminals and automated transport systems : logistics control issues and quantitative decision support , 2005 .

[14]  Iris F. A. Vis,et al.  Transshipment of containers at a container terminal: An overview , 2003, Eur. J. Oper. Res..

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

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

[17]  LorenzoniLuciano Lessa,et al.  A multi-mode resource-constrained scheduling problem in the context of port operations , 2006 .

[18]  David S. Johnson,et al.  Computers and Intractability: A Guide to the Theory of NP-Completeness , 1978 .

[19]  Michael Pinedo,et al.  Scheduling: Theory, Algorithms, and Systems , 1994 .