Multi-objective genetic algorithm for berth allocation problem considering daytime preference

Daytime preference is identified as an objective of berth allocation problem.Delayed workloads and workloads operated at night are minimized.A multi-objective genetic algorithm is developed to solve the bi-objective model. Maximization of operational efficiency and minimization of cost are pursued by terminal operators, whereas daytime preference is increasingly emphasized by governments, terminal operators and workers. Daytime preference in berth allocation schedule refers to schedule the workloads in nights as fewer as possible, which improves working comfort, safety, and green and energy-savings degrees, but may decrease the throughput and total operational efficiency. By extending existing dynamic discrete berth allocation model, a bi-objective model considering daytime preference is established to minimize the delayed workloads and the workloads in nights. Based on the well known NSGA-II algorithm, a multi-objective genetic algorithm (moGA) is developed for solving the bi-objective model by using a two-part representation scheme. The sensitivities of the algorithmic parameters and tradeoffs between daytime preference and delayed workloads are analyzed by numerical experiments. The algorithmic aspects of the proposed approach and the effects of daytime preference on solutions are all examined. Finally, the managerial implications are discussed.

[1]  Eric C. Jackson,et al.  North American Container Port Capacity: A Literature Review , 2005 .

[2]  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.

[3]  G. Syswerda,et al.  Schedule Optimization Using Genetic Algorithms , 1991 .

[4]  Graham Kendall,et al.  Handling diversity in evolutionary multiobjective optimization , 2005, 2005 IEEE Congress on Evolutionary Computation.

[5]  Stefan Voß,et al.  Container terminal operation and operations research - a classification and literature review , 2004, OR Spectr..

[6]  S. S. Al Sharif,et al.  A 0-1 goal programming model for nurse scheduling , 2005, Comput. Oper. Res..

[7]  Ceyda Oguz,et al.  A Note on Formulations of Static and Dynamic Berth Allocation Problems , 2003 .

[8]  Scott Davis,et al.  Night Shift Work and Levels of 6-Sulfatoxymelatonin and Cortisol in Men , 2013, Cancer Epidemiology, Biomarkers & Prevention.

[9]  B. McCall,et al.  The impact of shift work on the risk and severity of injuries for hospital employees: an analysis using Oregon workers' compensation data. , 2004, Occupational medicine.

[10]  J. Dorrian,et al.  Work hours, workload, sleep and fatigue in Australian Rail Industry employees. , 2011, Applied ergonomics.

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

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

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

[14]  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..

[15]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

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

[17]  Sara Arber,et al.  Effects of night work on sleep, cortisol and mood of female nurses, their husbands and children , 2013 .

[18]  Yusin Lee,et al.  An optimization heuristic for the berth scheduling problem , 2009, Eur. J. Oper. Res..

[19]  Bryan Vila,et al.  Shift work and long-term injury among police officers. , 2013, Scandinavian journal of work, environment & health.

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

[21]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

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

[23]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation) , 2006 .

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

[25]  Seyda Topaloglu,et al.  A multi-objective programming model for scheduling emergency medicine residents , 2006, Comput. Ind. Eng..

[26]  Ching-Chih Chang,et al.  Evaluating the effects of green port policy: Case study of Kaohsiung harbor in Taiwan , 2012 .

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

[28]  Mitsuo Gen,et al.  Network Models and Optimization: Multiobjective Genetic Algorithm Approach , 2008 .

[29]  David W. Coit,et al.  Multi-objective optimization using genetic algorithms: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[30]  Christian Bierwirth,et al.  Heuristics for the integration of crane productivity in the berth allocation problem , 2009 .

[31]  Richard Y. K. Fung,et al.  Berth allocation considering fuel consumption and vessel emissions , 2011 .

[32]  E. van Rensburg,et al.  Night Shift Working Mothers: Mutual Perceptions with Adolescent Children , 2011 .

[33]  Samer Madanat,et al.  Unintended environmental impacts of nighttime freight logistics activities , 2009 .

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

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

[36]  Cara Williams,et al.  Work-life balance of shift workers , 2008 .

[37]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

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

[39]  Xiangpei Hu,et al.  Disruption recovery model for berth and quay crane scheduling in container terminals , 2011 .

[40]  Lee Di Milia,et al.  Shift Work, Sleepiness and Long Distance Driving , 2006 .

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

[42]  Fouad Ben Abdelaziz,et al.  Solution approaches for the multiobjective stochastic programming , 2012, Eur. J. Oper. Res..

[43]  Jesper Larsen,et al.  Models for the discrete berth allocation problem: A computational comparison , 2011 .

[44]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

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

[46]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[47]  Tom Cherrett,et al.  Study on berth planning problem in a container seaport: Using an integrated programming approach , 2012, Comput. Ind. Eng..

[48]  R. Stevens.  Light-at-night, circadian disruption and breast cancer: assessment of existing evidence. , 2009, International journal of epidemiology.

[49]  C Dambrosio.  Quality of life of shift workers , 2003 .

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

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

[52]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[53]  G. Kac,et al.  Night-shift work and cardiovascular risk among employees of a public university. , 2012, Revista da Associacao Medica Brasileira.

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

[55]  U. Srivastava,et al.  Shift Work Related to Stress, Health and Mood States , 2010 .

[56]  T. Lacksonen.  Empirical comparison of search algorithms for discrete event simulation , 2001 .

[57]  Adriano Marçal Pimenta,et al.  Trabalho noturno e risco cardiovascular em funcionários de universidade pública , 2012 .

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

[59]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[60]  J. B. Erickson,et al.  Estimates of injury risks for healthcare personnel working night shifts and long hours , 2009, Quality and Safety in Health Care.

[61]  Drew Dawson,et al.  Shiftwork experience and the value of time , 2004, Ergonomics.

[62]  Belén Melián-Batista,et al.  Biased random key genetic algorithm for the Tactical Berth Allocation Problem , 2014, Appl. Soft Comput..

[63]  Rebecca Jane Loudoun,et al.  Injuries sustained by young males in construction during day and night work , 2010 .