A hybrid evolutionary approach to job-shop scheduling with generic time lags

This paper addresses the job shop scheduling problem including time lag constraints. This is an extension of the job shop scheduling problem with many applications in real production environments, where extra (minimum and maximum) delays can be introduced between operations. It belongs to a category of problems known as NP-hard problems due to the large solution space. Biogeography-based optimization (BBO) is an evolutionary algorithm which is inspired by the migration of species between habitats, recently proposed by Simon (IEEE Trans Evol Comput 12:702–713, 2008) to optimize hard combinatorial optimization problems. BBO has successfully solved optimization problems in many different domains and has demonstrated excellent performance. We propose a hybrid biogeography-based optimization (HBBO) algorithm for solving the job shop scheduling problem with additional time lag constraints while minimizing total completion time. In the proposed HBBO, an effective greedy constructive heuristic is adapted to generate the initial habitat population. A local search metaheuristic is investigated in the mutation step in order to improve the solution quality and enhance the diversity of the population. To assess the performance of the HBBO, a series of experiments are performed on well-known benchmark instances for job shop scheduling problems with time lag constraints. The results prove the efficiency of the proposed algorithm in comparison with various other algorithms.