Genetic algorithm for large-size multi-stage batch plant scheduling

• Published in: Chemical engineering science Vol. 62; no. 5; pp. 1504 - 1523
• Main Authors: He, Yaohua, Hui, Chi-Wai
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.03.2007; Elsevier
• Subjects: Active schedule; Applications of mathematics to chemical engineering. Modeling. Simulation. Optimization; Applied sciences; Chemical engineering; Economics. Management. Design assessment; Exact sciences and technology; Genetic algorithm; Heuristic rule; Mixed-integer linear programming; Multi-stage scheduling; Multi-stage scheduling; Heuristic rule; Genetic algorithm; Mixed-integer linear programming; Active schedule; Batchwise; Linear programming; Mixed integer programming; Scheduling; Heuristic approach; Mathematical programming; Handling
• ISSN: 0009-2509; 1873-4405
• DOI: 10.1016/j.ces.2006.11.049

This paper presents a heuristic approach based on genetic algorithm (GA) for solving large-size multi-stage multi-product scheduling problem (MMSP) in batch plant. The proposed approach is suitable for different scheduling objectives, such as total process time, total flow time, etc. In the algorithm, solutions to the problem are represented by chromosomes that will be evolved by GA. A chromosome consists of order sequences corresponding to the processing stages. These order sequences are then assigned to processing units according to assignment strategies such as forward or backward assignment, active scheduling technique or similar technique, and some heuristic rules. All these measures greatly reduce unnecessary search space and increase the search speed. In addition, a penalty method for handling the constraints in the problem, e.g., the forbidden changeovers, is adopted, which avoids the infeasibility during the GA search and further greatly increases the search speed.