Title: Population-based dynamic scheduling optimisation for complex production process

Authors: Jing An; Qi Kang; Lei Wang; Qidi Wu

Addresses: Department of Control Science and Engineering, Tongji University, Shanghai 201804, China. ' Department of Control Science and Engineering, Key Laboratory of Embedded System and Service Computing, MOE, Tongji University, Shanghai 201804, China. ' Department of Control Science and Engineering, Key Laboratory of Embedded System and Service Computing, MOE, Tongji University, Shanghai 201804, China. ' Department of Control Science and Engineering, Key Laboratory of Embedded System and Service Computing, MOE, Tongji University, Shanghai 201804, China

Abstract: This paper presents a population-based approximate scheduling approach for complex production process, by using heuristic stochastic optimisation strategies. In this approach, particle swarm optimisation (PSO) is adopted to find a near optimal operation sequence and schedule strategy based on the criterion of minimal total make-span (TMS) in its admissible sequence space. Discrete dynamic programming method is integrated for the usage of fitness evaluation. A minifab model is studied to illustrate the proposed population-based scheduling algorithm (PSA), which can approach the optimal results by computing partial solution sequences.

Keywords: swarm intelligence; dynamic programming; population-based scheduling; approximate scheduling; scheduling optimisation; particle swarm optimisation; PSO.

DOI: 10.1504/IJCAT.2012.047154

International Journal of Computer Applications in Technology, 2012 Vol.43 No.4, pp.304 - 310

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 31 May 2012 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article