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

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 failureprone 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. KEYWORDScontrollable processing times, failure-prone manufacturing system, modified hedging point policy, Pareto optimal solutions, stochastic job shop scheduling | INTRODUCTIONFailure-prone manufacturing systems (FPMSs) can be defined as a subcategory of flexible systems with stochastic breakdown and maintenance of machines. Indeed, the major goal is to identify the production rate; accordingly, holding, shortage, and maintenance costs could be reduced in a long-run planning horizon. FPMSs are models for studying manufacturing systems considering system ambiguities. Buffers in manufacturing systems play the role of decreased impact of machine breakdown on meeting demand. Thus, it seems indispensable to identify the optimal level of buffers to reduce holding and shortage costs. Seeing as machines in flexible manufacturing systems can produce with various speeds, identifying the optimal speed is particularly significant and is defined as the hedging point policy (HPP) in FPMS. Job shop problem is an NP-hard problem in terms of complexity (Garey, Tarjan, & Wilfong, 1988). Given that, the stochastic job shop problem is an NP-hard problem, as well, considering machine breakdown and variable processing speed.

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

“…Several authors have resorted to the speed adjusting strategy; however, only machine processing speed (MS) has been considered (see, e.g., [24,30,31,33,[50][51][52][53]). The exception is the work in [54] that considers simultaneously adjusting the machine processing speed and the vehicle traveling speed.…”

“…Besides the performance and economy objectives, a few other objectives have been considered, although in just a very small number of papers. These include machine performance goals such as machines workload (ML) [16,31,35], rescheduling disruption (RD) [57,103], machines' fault probability (FP) [117], and system reliability (Rel) [8,51]; product-related goals such as work in process (W IP) [16], raw material usage (RM) [64], quality and defective rate (Q) [19,22], and customer satisfaction (CuS) [35]; and social objectives, included mainly through working conditions such as noise level (N) [32,101,118], ergonomic risk (Erg) [119], and vibration (Vib) [101].…”

Expert systems and risk analytics in service engineering

“…In Amelian et al (2019), multi‐objective optimisation of the stochastic failure‐prone job‐shop scheduling problem is sought wherein the job processing time appears to be controllable. It endeavours to determine the best sequence of jobs, optimal production rate, and optimum preventive maintenance period for simultaneous optimisation of three criteria of sum of earliness and tardiness, system reliability, and energy consumption.…”

Modern Optimization and Simulation Methods in Managerial and Business Economics: A Review

“…In these cases, the use of procedures that integrate simulation and metaheuristics is widely contrasted and quite versatile, regardless of the manufacturing industry of application (from the chemical industry to the textile manufacturing one). In the case of job-shop problems, some of the most relevant manuscripts in recent years are the ones by Amelian et al (2019) and Belkaid et al (2016). With the main objective of enhancing competitiveness and improving effectiveness, many countries have decided to change from a monopoly-based electric power generation to one of perfect competition (Section 5).…”