Title: Dynamic vehicle path planning using an enhanced simulated annealing approach for supply chains
Authors: Hui Miao; Xiaodi Huang
Addresses: School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia. ' School of Computing and Mathematics, Charles Sturt University, Albury, NSW 2640, Australia
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.
Keywords: vehicle path planning; dynamic supply chains; simulated annealing; genetic algorithms; heuristics; supply chain management; SCM.
International Journal of Enterprise Network Management, 2012 Vol.5 No.2, pp.197 - 218
Available online: 05 Jul 2012 *Full-text access for editors Access for subscribers Purchase this article Comment on this article