Dynamic vehicle path planning using an enhanced simulated annealing approach for supply chains

Hui Miao, Xiaodi Huang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Evolutionary computation is an effective tool for solving optimisation problems. However, its significant computational demand has limited its real-time and online applications, e.g., mobile vehicles in supply chains. An enhanced SA approach incorporating with initial path selection heuristics and multiple mathematical operators is proposed in this paper for vehicle path planning in dynamic supply chain environments. It requires less computation times while giving better trade-offs among simplicity, far-field accuracy, and computational cost. The enhanced SA is analysed in several environments. The evaluation results demonstrate the ESA approach has the best performance for vehicle path planning in dynamic supply chains.
Original languageEnglish
Pages (from-to)197-218
Number of pages22
JournalInternational Journal of Enterprise Network Management
Volume5
Issue number2
DOIs
Publication statusPublished - Jul 2012

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