Population-based dynamic scheduling optimisation for complex production process

  • Authors:

  • Jing An;Qi Kang;Lei Wang;Qidi Wu

  • Affiliations:

  • 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

  • Venue:
  • International Journal of Computer Applications in Technology
  • Year:
  • 2012

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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.