Production scheduling optimization in foundry using hybrid Particle Swarm Optimization algorithm

Scheduling in foundry consist a type of production where, the hot work-in-processes cannot wait between two successive operations and can be modeled as a flow shop scheduling problem with no-wait constraint. With the objective to reduce total flow time, the appropriate sequence of jobs for scheduling is essential, hence the problem can be observed as typical NP-hard combinatorial optimization problem. This paper, proposes hybridization of Particle Swarm Optimization with simulated annealing for planning and scheduling issues which are very complex because of the ever changing needs of customers and existing constraints in foundry. This Proposed Hybrid Particle Swarm Optimization algorithm represents solution by random key representation rule for converting the continuous position information values of particles to a discrete job permutation. The proposed hybrid particle swarm optimization algorithm initializes population efficiently with Nawaz-Enscore-Ham heuristic and uses evolutionary search guided by the mechanism of PSO and local search by mechanism of simulated Annealing by balancing both global exploration and local exploitation. The proposed hybrid particle swarm optimization algorithm try to bridge the gap between theory and practice by considering foundry environment, which will help planner to decide the sequence of production of jobs based against clients' orders and to develop efficient scheduling procedures for minimizing total flow time with relatively low computational efforts. Extensive computational experiments are carried out based on various casting's (job's) characteristics viz. casting type, mould size and type of alloy, where size of job (n) considered as 10,12,20,50 and 100. With respect to performance measure, Average Relative Percent Deviation which is popular in the scheduling literature, the proposed method performs better than Simulated Annealing and Particle Swarm Optimization.