Particle swarm optimisation algorithm and multi-start simulated annealing algorithm for scheduling batches of parts in multi-cell flexible manufacturing system

Abstract

This paper considers the problem of scheduling batches of parts in a multi-cell flexible manufacturing system (MCFMS) with sequence dependent batch setup time. The goal is to find the best sequence of batches and hence to minimise the makespan. Two mathematical models are developed namely: batch availability model and job availability model. As the problem is known to be NP-hard, particle swarm optimisation (PSO) algorithm and multi-start simulated annealing (MSA) algorithm are proposed to solve the problem. The proposed algorithms are validated by testing the benchmark problems available in the literature. In addition to that, 80 problems with various sizes have been generated at random and then the performance of the proposed MSA and PSO algorithms are compared with CPLEX solver. The experimental results show that MSA provides better solution compared with PSO, the same solution as CPLEX and very close to the lower bound value provided by CPLEX.

Modeling and Solving the Flow‐Shop Scheduling Problem with Sequence‐Dependent Setup Times by Firefly Algorithm (Case Study: Automotive Industry)

“…In their recent article, Allahverdi et al [11] provide a comprehensive overview of the studies conducted on the problems of flow-shop and permutation flow-shop. In recent studies, Balaji and Porselvi [12] consider the problem of scheduling batches of parts in a multicell flexible manufacturing system with batch setup time. e goal is to find the best sequence of batches and thus minimize the time interval.…”