Solving realistic industrial scheduling problems using a multi-objective improved hybrid particle swarm optimisation algorithm (Q2627583)

Summary: In this paper, the real-world multistage hybrid flow shop scheduling problem (HFSSP) is contemplated. The HFSSP is strongly an NP-hard (non-deterministic polynomial time hard) problem. Due to their theoretical and practical significance, several researchers have tackled the HFSSPs with a single objective function (makespan). However, many industrial scheduling problems involve multiple conflicting objectives and hence such problems are more complex to solve. But, multi-objective optimisation algorithms are relatively scarce in the HFSSP literature. This paper proposes a hybrid algorithm based on particle swarm optimisation (PSO) for the multi-objective HFSSPs. The proposed multi-objective improved hybrid particle swarm optimisation (MOIHPSO) algorithm searches the Pareto optimal solution for makespan and total flow time objectives. In the proposed MOIHPSO algorithm, two different sub-populations for the two objectives are generated and different dispatching rules are used to improve the solution quality. Moreover, the mutation operator is incorporated in this MOIHPSO to avoid the solution to be trapped in local optima. Data from a steel furniture manufacturing company is used to illustrate the proposed methodology. Simulation results demonstrate the effectiveness of the proposed algorithm.