Seaside operation planning with evolutionary particle swarm optimization algorithm

Articles in Press

Document Type : Scientific - Research

Authors

1 M.Sc , Department of transportation planning, school of civil engineering, Iran university of science and technology

2 Assistant proffessor, Department of transportation planning, school of civil engineering, iran university of science and technology

Abstract

Seaside operation Planning at container terminals includes three problems; Berth Allocation Problem (BAP), Quay Crane Assignment Problem (QCAP), and Quay Crane Scheduling Problem (QCSP). This paper solves these problems in the two stages integrated manner. BAP and QCAP are represented in a mathematical model and solved with metaheuristic algorithms in the first stage. QCSP is represented in a separate mathematical model and solved with the dynamic programming heuristic algorithm in the second stage. Due to the NP-Hard nature of seaside operation problems, heuristic/metaheuristic algorithms are needed to solve them. In this paper, for the first time, an evolutionary version of the Particle Swarm Optimization (EPSO) algorithm is used to solve seaside operation problems. The performance of EPSO and its output is compared with the initial version of the same algorithm (PSO) and genetic algorithm (GA). The numerical results showing the EPSO algorithm has approximately the same answers as the GA algorithm (with a difference of 1%). However, in terms of runtime, it is faster than the GA algorithm. Compared to the PSO algorithm, about 6% report better responses but slightly slower runtime. The validation of the present article has been done with the real data in both container terminals of Shahid Rajaei port.

Keywords

Quarterly Journal of Transportation Engineering

Articles in Press, Accepted Manuscript
Available Online from 05 February 2022

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History

  • Receive Date: 13 November 2021
  • Revise Date: 29 December 2021
  • Accept Date: 01 January 2022

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