Particle Swarm Optimization method for rescheduling of job processing against machine breakdowns for nondisruptive cell manufacturing system

This paper proposes a novel method of reactive scheduling of job processing for non-disruptive cell manufacturing systems (CMS) against unexpected machine breakdown occurrence. Reactive scheduling problem for reassigning pending jobs in CMS is formulated as a discrete optimization problem of Mixed Integer Programming (MIP) and effective solving method of BPSO-SA which is hybridizing method of Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) is proposed to cope with time-critical recovery situation. A Binary Particle Swarm Optimization (BPSO) is adopted to explore near optimal feasible solution of the discrete rescheduling problem, and Simulated Annealing (SA) is used for the global optimization of locating a good approximation to the global optimum in a large search space. Numerical experiment demonstrates the effectiveness of the proposed model through case study problems of non-disruptive jobs processing in CMS with unexpected machine breakdowns to seek for multiple objectives to minimize tardiness, number of intercellular movements, as well as maximizing cell load balancing and the results show that BPSO-SA provides a near optimal solution with significant reducing of calculation time compared with mathematical optimization of MIP method.