Optimisation for job scheduling at automated container terminals using genetic algorithm

被引：58

作者：

Skinner, Bradley ^{[1
]}

Yuan, Shuai ^{[1
]}

Huang, Shoudong ^{[1
]}

Liu, Dikai ^{[1
]}

Cai, Binghuang ^{[1
]}

Dissanayake, Gamini ^{[1
]}

Lau, Haye ^{[2
]}

Bott, Andrew ^{[2
]}

Pagac, Daniel ^{[2
]}

机构：

[1] Univ Technol Sydney, FEIT, Sch Elect Mech & Mechatron Syst, Sydney, NSW 2007, Australia

[2] Patrick Technol Syst, Botany, NSW 2019, Australia

来源：

COMPUTERS & INDUSTRIAL ENGINEERING | 2013年 / 64卷 / 01期

基金：

澳大利亚研究理事会;

关键词：

Modelling; Scheduling; Genetic algorithms; Autonomous straddle carrier; Automated seaport container terminals; DELIVERY PROBLEM; OPERATIONS-RESEARCH; DISPATCHING METHOD; TIME WINDOWS; PICKUP; VEHICLES;

D O I：

10.1016/j.cie.2012.08.012

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper presents a genetic algorithm (GA)-based optimisation approach to improve container handling operations at the Patrick AutoStrad container terminal located in Brisbane Australia. In this paper we focus on scheduling for container transfers and encode the problem using a two-part chromosome approach which is then solved using a modified genetic algorithm. In simulation experiments, the performance of the GA-based approach and a sequential job scheduling method are evaluated and compared with different scheduling scenarios. The experimental results show that the GA-based approach can find better solutions which improve the overall performance. The GA-based approach has been implemented in the terminal scheduling system and the live testing results show that the GA-based approach can reduce the overall time-related cost of container transfers at the automated container terminal. Crown Copyright (C) 2012 Published by Elsevier Ltd. All rights reserved.

引用

页码：511 / 523

页数：13

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