Novel hybrid discrete differential evolution algorithm for the multi-stage multi-purpose batch plant scheduling problem

Abstract

The multi-stage multi-product batch plant scheduling problem is an important part of batch chemical industry scheduling problems. Different from the multi-purpose batch scheduling problem, this problem can be characterized by multiple stages with non-identical parallel units, and multiple batches of customer orders. Numerous methodologies for this problem have been investigated to addressing scheduling cases of different production systems in the past two decades. This paper focus on the large-scale batch plant scheduling problem by minimizing the make-span in the scheduling horizon. And a novel hybrid discrete differential evolutionary algorithm is proposed to handle this problem. First, a novel two-line encoding scheme is constructed based on discrete and continuous variables. The sequence of orders is represented by a time-based representation method. Second, two novel mutation methods are proposed within the framework of encoding method. Two methods provide multiple search directions which helps improve the exploration ability and diversity of the population. At last, two local permutation methods are applied to improve the local optimal for the algorithm. The proposed work is tested through several real industrial instances with different sizes and characteristics by comparing with mixed integer linear programming method and meta-heuristic algorithms. The improvement of proposed work is also analyzed by the instances. The results report the efficiency and effectiveness of the novel proposed evolutionary algorithm in solving large scale multi-stage multi-product batch plant scheduling problem.