Robust Scheduling for Berth Allocation and Quay Crane Assignment Problem

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

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

[2]  Mario Rodríguez-Molins,et al.  Integrated intelligent techniques for remarshaling and berthing in maritime terminals , 2011, Adv. Eng. Informatics.

[3]  Christian Bierwirth,et al.  A survey of berth allocation and quay crane scheduling problems in container terminals , 2010, Eur. J. Oper. Res..

[4]  Xiongwen Quan,et al.  Robust berth scheduling with uncertain vessel delay and handling time , 2012, Ann. Oper. Res..

[5]  Mitsuo Gen,et al.  Genetic algorithms and engineering optimization , 1999 .

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

[7]  J. Christopher Beck,et al.  Slack-based Techniques for Robust Schedules , 2014 .

[8]  Ujjwal Maulik,et al.  A Simulated Annealing-Based Multiobjective Optimization Algorithm: AMOSA , 2008, IEEE Transactions on Evolutionary Computation.

[9]  Lu Zhen,et al.  A bi-objective model for robust berth allocation scheduling , 2012, Comput. Ind. Eng..

[10]  Lucas Bradstreet,et al.  A Fast Way of Calculating Exact Hypervolumes , 2012, IEEE Transactions on Evolutionary Computation.

[11]  Tomoyuki Hiroyasu,et al.  SPEA2+: Improving the Performance of the Strength Pareto Evolutionary Algorithm 2 , 2004, PPSN.

[12]  Marco Laumanns,et al.  Robust cyclic berth planning of container vessels , 2010, OR Spectr..

[13]  Leyuan Shi,et al.  The allocation of berths and quay cranes by using a sub-gradient optimization technique , 2010, Comput. Ind. Eng..

[14]  Matteo Salani,et al.  Modeling and Solving the Tactical Berth Allocation Problem , 2010 .

[15]  Jorge Puente,et al.  A genetic algorithm for robust berth allocation and quay crane assignment , 2014, Progress in Artificial Intelligence.

[16]  Zhi-Hua Hu,et al.  Berth and quay-crane allocation problem considering fuel consumption and emissions from vessels , 2014, Comput. Ind. Eng..

[17]  Christian Blum,et al.  Hybrid metaheuristics in combinatorial optimization: A survey , 2011, Appl. Soft Comput..

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

[19]  Qingfu Zhang,et al.  Multiobjective evolutionary algorithms: A survey of the state of the art , 2011, Swarm Evol. Comput..

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

[21]  Jean-Charles Billaut,et al.  Introduction to Flexibility and Robustness in Scheduling , 2010 .

[22]  Stefan Voß,et al.  Operations research at container terminals: a literature update , 2007, OR Spectr..

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

[24]  Erhan Kozan,et al.  A hybrid constructive heuristic and simulated annealing for railway crew scheduling , 2014, Comput. Ind. Eng..

[25]  Qiushuang Chen,et al.  A feedback procedure for robust berth allocation with stochastic vessel delays , 2010, 2010 8th World Congress on Intelligent Control and Automation.

[26]  Jean-Charles Billaut,et al.  Flexibility and Robustness in Scheduling , 2008 .

[27]  Lawrence Henesey,et al.  Multi-Agent Systems for Container Terminal Management , 2006 .

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

[29]  Uzay Kaymak,et al.  A Bi-Objective Evolutionary Approach to Robust Scheduling , 2007, 2007 IEEE International Fuzzy Systems Conference.

[30]  Mario Rodríguez-Molins,et al.  A GRASP-based metaheuristic for the Berth Allocation Problem and the Quay Crane Assignment Problem by managing vessel cargo holds , 2013, Applied Intelligence.

[31]  Kwang Ryel Ryu,et al.  Planning for remarshaling in an automated container terminal using cooperative coevolutionary algorithms , 2009, SAC '09.

[32]  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..

[33]  Xavier Gandibleux,et al.  Hybrid Metaheuristics for Multi-objective Combinatorial Optimization , 2008, Hybrid Metaheuristics.

[34]  Erik Demeulemeester,et al.  Proactive and reactive strategies for resource-constrained project scheduling with uncertain resource availabilities , 2008, J. Sched..

[35]  Ali H. Diabat,et al.  An Integrated Quay Crane Assignment and Scheduling Problem Using Branch-and-Price , 2014, 2016 International Conference on Computational Science and Computational Intelligence (CSCI).

[36]  Dirk C. Mattfeld,et al.  Evolutionary Search and the Job Shop - Investigations on Genetic Algorithms for Production Scheduling , 1996, Production and Logistics.