Multi-objective optimization for stochastic failure-prone job shop scheduling problem via hybrid of NSGA-II and simulation method

Sayed Shahab Amelian, Seyed Mojtaba Sajadi, Mehrzad Navabakhsh, Majid Esmaelian

Research output: Contribution to journalArticlepeer-review

17 Citations (Scopus)


Production scheduling and reliability of machinery are prominent issues in flexible manufacturing systems that are led to decreasing of production costs and increasing of system efficiency. In this paper, multiobjective optimization of stochastic failure-prone job shop scheduling problem is sought wherein that job processing time seems to be controllable. It endeavours to determine the best sequence of jobs, optimal production rate, and optimum preventive maintenance period for simultaneous optimization of three criteria of sum of earliness and tardiness, system reliability, and energy consumption. First, a new mixed integer programming model is proposed to formulate the problem. Then, by combining of simulation and NSGA-II algorithm, a new algorithm is put forward for solving the problem. A set of Pareto optimal solutions is achieved through this algorithm. The stochastic failure-prone job shop with controllable processing times has not been investigated in the earlier research, and for the first time, a new hedging point policy is presented. The computational results reveal that the proposed metaheuristic algorithm converges into optimal or near-optimal solution. To end, results and managerial insights for the problem are presented.

Original languageEnglish
Article numbere12455
JournalExpert Systems
Issue number2
Early online date28 Jul 2019
Publication statusPublished - Feb 2022
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2019 John Wiley & Sons, Ltd


  • controllable processing times
  • failure-prone manufacturing system
  • modified hedging point policy
  • Pareto optimal solutions
  • stochastic job shop scheduling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computational Theory and Mathematics
  • Artificial Intelligence


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