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.
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.
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.
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.
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.
This research provides an efficient scheduling method for solving the FJSP problem.
This research proposes an IH-PSO algorithm to solve the FJSP more efficiently and meet the needs of multi-objective optimization.
This work was supported by the National Natural Science Foundation of China [grant numbers U1637211, 51805253], Fundamental Research Funds for the Central Universities [grant number 3082018NS2018035] and National Defense Basic Scientific Research Program of China [grant number JCKY2018605C003].
Zhang, Y., Zhu, H. and Tang, D. (2019), "An improved hybrid particle swarm optimization for multi-objective flexible job-shop scheduling problem", Kybernetes, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/K-06-2019-0430Download as .RIS
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