This paper which is based on previous research proposes a scheduling algorithm which contains Discrete Particle Swarm Optimization and readjustment strategy (RS-DPSO) for dynamic flexible job shop scheduling problems (DFJSP) taking into the new workpieces arrival. This proposed algorithm divides FJSP in dynamic environment into several continuous scheduling subintervals and sets readjustment mechanism which is activated when the new workpieces arrives. DPSO algorithm is used to obtain the appropriate scheduling scheme which meets the requirements of dynamic environment for every subintervals. The feasibility and effectiveness of the proposed algorithm is verified by applying it to a practical case for the DFJSP taking into the new workpieces arrival.
For the flexible job shop scheduling problem with the objective of minimizing the maximum make-time, an adaptive discrete particle swarm algorithm is proposed. The algorithm adopts an initialization method that combines random generation and process-based global load minimum selection machine initialization. At the same time, in order to improve the convergence speed of the algorithm, an adaptive inertia weight is added to the particle position update method, and crossover and mutation operations are introduced. Through comparative experiments and numerical analysis of benchmark examples, the effectiveness of the proposed adaptive hybrid discrete particle swarm optimization algorithm is verified.
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