Hybrid enhanced discrete fruit fly optimization algorithm for scheduling blocking flow-shop in distributed environment

Scheduling in distributed production environments is becoming widespread in recent years due to the increasing advantages of multi-factory manufacture. This paper investigates the distributed blocking flow-shop scheduling problem (DBFSP) with the objective of minimizing the makespan. To solve this problem, a hybrid enhanced discrete fruit fly optimization algorithm (HEDFOA) is proposed. In the proposed algorithm, an effective constructive heuristic is developed based on a new assignment rule of jobs and an insertion-based improvement procedure to initialize the common central location of all fruit fly swarms. In the smell-based foraging, an effective insertion-based neighborhood operator is designed for exploration in global scope. In the vision-based foraging, a local search is embedded to intensify the exploitation ability of algorithm in local region. Meanwhile, a simulated annealing-like acceptance criterion is employed to help algorithm escape from the local optimum. Finally, an extensive computational experiment is conducted. Experimental results show that the proposed HEDFOA is more effective than the existing state-of-the-art methods. Furthermore, 516 best known solutions out of 720 benchmark instances are also updated. (C) 2019 Elsevier Ltd. All rights reserved.