An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem

Abstract

Purpose With the continuous upgrading of the production mode of the manufacturing system, the characteristics of multi-variety, small batch and mixed fluidization are presented, and the production environment becomes more and more complex. To improve the efficiency of solving multi-objective flexible job shop scheduling problem (FJSP), an improved hybrid particle swarm optimization algorithm (IH-PSO) is proposed. Design/methodology/approach After reviewing literatures on FJSP, an IH-PSO algorithm for solving FJSP is developed. First, IH-PSO algorithm draws on the crossover and mutation operations of genetic algorithm (GA) algorithm and proposes a new method for updating particles, which makes the offspring particles inherit the superior characteristics of the parent particles. Second, based on the improved simulated annealing (SA) algorithm, the method of updating the individual best particles expands the search scope of the domain and solves the problem of being easily trapped in local optimum. Finally, analytic hierarchy process (AHP) is used in this paper to solve the optimal solution satisfying multi-objective optimization. Findings Through the benchmark experiment and the production example experiment, it is verified that the proposed algorithm has the advantages of high quality of solution and fast speed of convergence. Research limitations/implications This method does not consider the unforeseen events that occur during the process of scheduling and cause the disruption of normal production scheduling activities, such as machine breakdown. Practical implications IH-PSO algorithm combines PSO algorithm with GA and SA algorithms. This algorithm retains the advantage of fast convergence speed of traditional PSO algorithm and has the characteristic of inheriting excellent genes. In addition, the improved SA algorithm is used to solve the problem of falling into local optimum. Social implications This research provides an efficient scheduling method for solving the FJSP problem. Originality/value This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.

A Review of Prediction and Optimization for Sequence-Driven Scheduling in Job Shop Flexible Manufacturing Systems

“…Palacio et al [43] proposed a GA hybridized with TS and heuristic seeding to minimize the total time needed to complete all jobs, thereby increasing the feasibility and connectivity of their algorithms. Zhang et al [53] developed a hybrid algorithm that combined PSO with GA and SA. This algorithm was designed to utilize the fast convergence speed of the traditional PSO algorithm, which inherits excellent genes.…”

“…The job sequence represents the job allocation inside the machines. Allocation problems that combine resource and job sequences are typical in JS-FMS planning and scheduling [20,21,24,31,32,36,40,42,46,48,50,53,56,57,59,62]; this is because the workflow of a job shop is unidirectional or recursive, as there are no constraints on the machines that perform only the first operation of a job or the last operation of the job [8]. Meanwhile, the product sequence concentrates on the sequence of products when a specific product enters a machine.…”

“…Some of the studies applied SD [19,29,30,41,42,44,45,49,53,56,57], SPT [20,22], and FIFO [22,35,63]. A few studies applied more than two dispatching rules for machine and job allocation.…”

“…Various setup times, diverse processing times, maintenance activities, and machine breakdowns are representative uncertainty factors that act as constraints. Some articles impose SDST as an uncertainty factor in their dispatching rules [19,26,29,30,32,41,44,45,49,53,56,57,76]. This time dependency from SDST can make it difficult and uncertain to determine resource and job allocations.…”

Research on Construction and Application for the Model of Multistage Job Shop Scheduling Problem

“…Dabah et al [40] paralleled the tabu search algorithm to solve the blocking job shop scheduling problem and used data in the existing literature to verify that the designed algorithm can improve upon the best results achieved by a large number of benchmark methods in the literature. Zhang et al [41] proposed an improved hybrid particle swarm optimization algorithm to study the multiobjective flexible job shop scheduling problem. e benchmark experiment showed that the algorithm has the advantages of high quality and fast convergence.…”