Research on Continuous Berth-Quay Crane Joint Allocation Optimization Problem Based on Improved Multi-Population Genetic Search Algorithm

DOI

10.2991/978-94-6463-514-0_47How to use a DOI?

Keywords

continuous berth-quay crane joint allocation; tabu search algorithm; multi-population genetic search algorithm

Abstract

The utilization rate of port berth shorelines is directly related to the port’s operational efficiency. A reasonable berthing plan can improve port operational efficiency and thereby reduce port operational costs. This paper first establishes a multi-objective optimization model based on the ship’s preferred berth cost, ship delay departure cost, ship delay arrival cost and quay crane scheduling cost, and then designs an improved multi-population genetic search algorithm (Improved multi-population genetic search algorithm, IMPGA). By changing the control parameters of the genetic algorithm to increase the evolutionary diversity of the population, the tabu search algorithm is used to randomly select outstanding individuals of the population for tabu search. Under the conditions of the same length of dock berth coastline and the same ship arrival data, a numerical simulation experiment was designed. The results show that the improved multi-population genetic search algorithm can solve the joint allocation problem of continuous berths and quay cranes, and this algorithm is better than the static quay cranes. The optimization effect of scheduling is better.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

TY  - CONF
AU  - Lei Cheng
AU  - Guangru Li
AU  - Hangtian Guo
PY  - 2024
DA  - 2024/09/28
TI  - Research on Continuous Berth-Quay Crane Joint Allocation Optimization Problem Based on Improved Multi-Population Genetic Search Algorithm
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 470
EP  - 482
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_47
DO  - 10.2991/978-94-6463-514-0_47
ID  - Cheng2024
ER  -