A biogeography-based optimization algorithm with local search for large-scale heterogeneous distributed scheduling with multiple process plans

Abstract

Since modern production mode has shifted from a single factory to a multi-factory production network, distributed scheduling has been derived. Distributed scheduling problem (DSP) is characterized by many varieties, large scale, redundant production factories, flexible production processes, and high-value products. Each factory in the heterogeneous DSP can be considered an individual entity, and there may be several production process plans. To address the heterogeneous DSP with multiple process plans, we consider minimizing the global makespan over all factories containing the transportation time delivering tasks from factories to their destinations and propose a biogeography-based optimization algorithm combined with local search based on heuristic rules (BBO-LH) to find the optimal production plan and enhance productivity. First, a new encoding scheme with three-segment representation has been developed to avoid illegal solutions and realize the information sharing between solutions selecting different process plans. Then two efficient local search approaches have been proposed based on different heuristic rules on sequence and equipment allocation respectively, to improve the search efficiency. Besides, BBO-LH has adopted a cosine migration rate model to replace the linear one to strengthen the ability to jump out of local optima. BBO-LH is compared with a genetic algorithm (GA_X), using generated examples to test the performance of the proposed strategies, and simulation results show the effectiveness of the proposed BBO-LH on large-scale heterogeneous DSP with multiple process plans.