A Biogeography-Based Optimization Algorithm with Heuristics for Distributed Multiproduct Scheduling Problem

Multiproduct batch plant scheduling problems arise in various industries, such as fine chemical, pharmaceutical, rubber, food, and daily cosmetics industries. To remain competitive in such a global economy, the modern production mode has shifted from a single factory to a multi-factory production network. Thus, distributed batch scheduling has attracted wide attention. To address the distributed batch scheduling with multiproduct and multi-factory production networks, we consider minimizing the global makespan over all factories and propose a biogeography-based optimization algorithm combined with heuristics to find the optimal production plan and enhance productivity. First, the time key encoding scheme and the First Come First Served rule have been adopted to improve the search efficiency. Then, the power of this heuristic rule has been enhanced by changing the production sequence based on the sequence of other stages instead of only the first stage. Besides, a high-efficient heuristic has been proposed to balance the load of different factories. The proposed algorithm is compared with three algorithms, and simulation results show the effectiveness of the proposed algorithm on distributed multiproduct batch plant scheduling problems.