Multi‐objective optimization for stochastic failure‐prone job shop scheduling problem via hybrid of NSGA‐II and simulation method. Issue 2 (28th July 2019)

• Record Type: Journal Article
• Authors: Amelian, Sayed Shahab; Sajadi, Seyed Mojtaba; Navabakhsh, Mehrzad; Esmaelian, Majid
• Other Names: Wu Desheng Dash guestEditor.; Hall Jon guestEditor.; Belezamo Baloka guestEditor.; Eken Süleyman guestEditor.; Avci Cafer guestEditor.
• Is Part Of: Expert systems. Volume 39:Issue 2(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2019-07-28
• Subjects: controllable processing times -- failure‐prone manufacturing system -- modified hedging point policy -- Pareto optimal solutions -- stochastic job shop scheduling; Expert systems (Computer science); 006.33
• DOI: 10.1111/exsy.12455
• Languages: English
• ISSNs: 0266-4720

Abstract

Abstract: 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.