Iterative Simulation and Optimization approach for job shop scheduling

In this paper, we present an iterative scheme integrating simulation with an optimization model, for solving complex problems, viz., job shop scheduling. The classical job shop scheduling problem which is NP-Hard, has often been modelled as Mixed-Integer Programming (MIP) model and solved using exact algorithms (for example, branch-and-bound and branch-and-cut) or using meta-heuristics (for example, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing). In the proposed Iterative Simulation-Optimization (ISO) approach, we use a modified formulation of the scheduling problem where the operational aspects of the job shop are captured only in the simulation model. Two new decision variables, controller delays and queue priorities are used to introduce feedback constraints, that help exchange information between the two models. The proposed method is tested using benchmark instances from the OR library. The results indicate that the method gives near optimal schedules in a reasonable computational time.