Scheduling optimization of a stochastic flexible job-shop system with time-varying machine failure rate

Due to environmental circumstances encountered in manufacturing processes, operating machines need to be maintained preventively, so as to ensure satisfactory operating condition. This paper investigates a scheduling problem in a flexible job-shop system with maintenance considerations where each operation can be processed by a machine out of a set of capable machines, and so, jobs may have alternative routes. Machine failure rates are assumed to be time-varying. This is a real assumption comes from a fact in realistic environments, where failure rate of a machine is variable when environmental situations like shop temperature, shop light, shop humidity or even worker skill change significantly. Moreover, in order to more close the addressed problem into the situations encountered in real world, the processing times and due dates are considered to be stochastic parameters. A mixed integer linear programming (MILP) model is constructed for addressed problem with the objective of number of tardy jobs and a minimum total availability constraint. Then a simulation-optimization framework based on a simulated annealing (SA) optimizer and Monte Carlo (MC) simulator is presented to solve the problem.