Resources Planning for Container Terminal in a Maritime Supply Chain Using Multiple Particle Swarms Optimization (MPSO)

Resources planning is an important task in a supply chain in order to achieve a good result. The better the utilization of resources, especially scarce resources, the better the performance of a supply chain. This research focuses on allocating two scarce resources, i.e., berth and quay cranes (QCs), to ships that call at a container terminal in a maritime supply chain. As global container shipments continue to grow, improving the efficiency of container terminals is important. A two-stage approach is used to find the optimal/near-optimal solution, in which the first stage is devoted to generating alternative ship placement sequences as inputs to the second stage that subsequently employs an event-based heuristic to place ships, resolve overlaps of ships, and assign/adjust QCs so as to develop a feasible solution. For identifying a better approach, various heuristics/metaheuristics, including first-come first-served (FCFS), particle swarm optimization (PSO), improved PSO (PSO2), and multiple PSO (MPSO), have been employed in the first stage, respectively. The experimental results show that combining the MPSO with the event-based heuristic leads to a better result.

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

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

[3]  Chiang,et al.  An Improved Shuffled Frog-Leaping Algorithm for Solving the Dynamic and Continuous Berth Allocation Problem (DCBAP) , 2019, Applied Sciences.

[4]  Zuhua Jiang,et al.  A berth allocation strategy using heuristics algorithm and simulation optimisation , 2008, Int. J. Comput. Appl. Technol..

[5]  Jui-Lin Lai,et al.  An efficient bi-objective personnel assignment algorithm based on a hybrid particle swarm optimization model , 2010, Expert Syst. Appl..

[6]  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..

[7]  Loo Hay Lee,et al.  A decision model for berth allocation under uncertainty , 2011, Eur. J. Oper. Res..

[8]  Daofang Chang,et al.  Integrating Berth Allocation and Quay Crane Assignments , 2010 .

[9]  Xiaohui Yuan,et al.  Application of enhanced PSO approach to optimal scheduling of hydro system , 2008 .

[10]  Fawaz S. Al-Anzi,et al.  A PSO and a Tabu search heuristics for the assembly scheduling problem of the two-stage distributed database application , 2006, Comput. Oper. Res..

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

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

[13]  Akio Imai,et al.  Corrigendum to “The dynamic berth allocation problem for a container port” [Transportation Research Part B 35 (2001) 401–417] , 2004 .

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

[15]  Guohai Liu,et al.  Randomization in particle swarm optimization for global search ability , 2011, Expert Syst. Appl..

[16]  Xiaojun Wang,et al.  An optimization approach for coupling problem of berth allocation and quay crane assignment in container terminal , 2012, Comput. Ind. Eng..

[17]  Lu Zhen,et al.  Scheduling quay cranes and yard trucks for unloading operations in container ports , 2019, Ann. Oper. Res..

[18]  Birger Raa,et al.  An enriched model for the integrated berth allocation and quay crane assignment problem , 2011, Expert Syst. Appl..

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

[20]  Z. Caner Taskin,et al.  Optimal berth allocation and time-invariant quay crane assignment in container terminals , 2014, Eur. J. Oper. Res..

[21]  Ling Wang,et al.  An effective hybrid PSO-based algorithm for flow shop scheduling with limited buffers , 2008, Comput. Oper. Res..

[22]  Chengle Ma,et al.  Cranes scheduling in frame bridges based automated container terminals , 2018, Transportation Research Part C: Emerging Technologies.

[23]  Hsien-Pin Hsu,et al.  A HPSO for solving dynamic and discrete berth allocation problem and dynamic quay crane assignment problem simultaneously , 2016, Swarm Evol. Comput..

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

[25]  Hsien-Pin Hsu,et al.  A Hybrid GA with Variable Quay Crane Assignment for Solving Berth Allocation Problem and Quay Crane Assignment Problem Simultaneously , 2019, Sustainability.

[26]  Andrew Lim,et al.  A stochastic beam search for the berth allocation problem , 2007, Decis. Support Syst..

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

[28]  Hai-gui Kang,et al.  Study on berth and quay-crane allocation under stochastic environments in container terminal , 2008 .

[29]  Mario Rodríguez-Molins,et al.  A decision support system for managing combinatorial problems in container terminals , 2012, Knowl. Based Syst..

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

[31]  Akio Imai,et al.  Marine container terminal configurations for efficient handling of mega-containerships , 2013 .

[32]  Youfang Huang,et al.  A quay crane dynamic scheduling problem by hybrid evolutionary algorithm for berth allocation planning , 2009, Comput. Ind. Eng..

[33]  Der-Horng Lee,et al.  Integrated discrete berth allocation and quay crane scheduling in port container terminals , 2010 .

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

[35]  Luca Maria Gambardella,et al.  A survey on metaheuristics for stochastic combinatorial optimization , 2009, Natural Computing.

[36]  Christian Bierwirth,et al.  A follow-up survey of berth allocation and quay crane scheduling problems in container terminals , 2015, Eur. J. Oper. Res..