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Mathematical Problems in Engineering
Volume 2014 (2014), Article ID 834927, 17 pages
http://dx.doi.org/10.1155/2014/834927
Research Article
Robust Scheduling for Berth Allocation and Quay Crane Assignment Problem
M. Rodriguez-Molins, M. A. Salido, and F. Barber
Instituto de Automàtica e Informàtica Industrial, Universitat Politècnica de València, Camino de Vera, s/n, 46022 València, Spain
Received 22 July 2014; Revised 24 November 2014; Accepted 1 December 2014; Published 31 December 2014
Academic Editor: Andrzej Swierniak
Copyright © 2014 M. Rodriguez-Molins et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
Decision makers must face the dynamism and uncertainty of real-world environments when they need to solve the scheduling problems. Different incidences or breakdowns, for example, initial data could change or some resources could become unavailable, may eventually cause the infeasibility of the obtained schedule. To overcome this issue, a robust model and a proactive approach are presented for scheduling problems without any previous knowledge about incidences. This paper is based on proportionally distributing operational buffers among the tasks. In this paper, we consider the berth allocation problem and the quay crane assignment problem as a representative example of scheduling problems. The dynamism and uncertainty are managed by assessing the robustness of the schedules. The robustness is introduced by means of operational buffer times to absorb those unknown incidences or breakdowns. Therefore, this problem becomes a multiobjective combinatorial optimization problem that aims to minimize the total service time, to maximize the buffer times, and to minimize the standard deviation of the buffer times. To this end, a mathematical model and a new hybrid multiobjective metaheuristic is presented and compared with two well-known multiobjective genetic algorithms: NSGAII and SPEA2+.
