An Improved PSO Algorithm for Flexible Job Shop Scheduling with Lot-Splitting

In the multi-objective flexible job-shop scheduling with lot-splitting problem, not only the routing and sequencing sub-problems are taken into account, but also a job lot can be split into a number of sub-lots such that different sub-lot of the same job can be processed on distinct machines. This problem is an extension of classic flexible job-shop scheduling problem (FJSP), which provides a closer approximation to real scheduling problems. In view of the stubborn nature of the problem, a novel multi-objective flexible size lot-splitting particle swarm optimization algorithm (MFSLSPSO) with preference information of decision-maker was put forward. Because of incorporating preference information into the algorithm, the search results are concentrated in preferred region of the Pareto front such that the hard work of choosing a satisfying solution from numerous non-inferior solutions is eliminated. The algorithm not only can split lots into flexible size sub-lots according to machine workloads, but also can optimize the sub-lots routing and sequencing simultaneously. The performance of the proposed algorithm was evaluated through simulations, and the results demonstrate the feasibility and efficiency of the proposed algorithm.