Integration of process planning and scheduling using chaotic particle swarm optimization algorithm
作者：
Highlights：
• Chaotic PSO algorithm is proposed to solve NP-hard IPPS problem.
• Ten chaotic maps are implemented to avoid premature convergence to local optimum.
• Makespan, balanced level of machine utilization and mean flow time are observed.
• Five experimental studies show that cPSO outperforms GA, SA, and hybrid algorithm.
• Scheduling plans are tested by mobile robot within a laboratory environment.
摘要
•Chaotic PSO algorithm is proposed to solve NP-hard IPPS problem.•Ten chaotic maps are implemented to avoid premature convergence to local optimum.•Makespan, balanced level of machine utilization and mean flow time are observed.•Five experimental studies show that cPSO outperforms GA, SA, and hybrid algorithm.•Scheduling plans are tested by mobile robot within a laboratory environment.
论文关键词：Process planning,Scheduling,Integrated process planning and scheduling,Particle swarm optimization,Chaos theory,Mobile robot
论文评审过程：Received 1 February 2016, Revised 24 May 2016, Accepted 3 August 2016, Available online 4 August 2016, Version of Record 10 August 2016.
论文官网地址：https://doi.org/10.1016/j.eswa.2016.08.019
