A bi-objective stochastic order planning problem in make-to-order multi-site textile manufacturing. (August 2021)

• Record Type: Journal Article
• Authors: Zhang, Zhenzhong; Guo, Chunxiang; Wei, Qu; Guo, Zhaoxia; Gao, Lei
• Is Part Of: Computers & industrial engineering. Volume 158(2021)
• Publication Date: 2021-08
• Subjects: Stochastic order planning -- Printing and dyeing industry -- Distributed manufacturing -- Sustainable objectives -- Scenario generation; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107367
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Addressed a multi-site stochastic order planning problem with real-world features. Proposed a multi-objective evolutionary stochastic optimization method. Integrated scenario generation with multi-objective discrete optimization. Demonstrated the application of the proposed model using real-world data. Abstract: This study addresses a multi-site stochastic order planning problem with real-world features existing in make-to-order textile manufacturing supply chains widely. A mathematical model of the problem is presented with the consideration of uncertain orders and heterogeneous carbon emission levels of different plants. The problem aims at minimizing the total tardiness and the excessive carbon emissions. By combining a single-objective-guided multi-objective evolution algorithm and a scenario generation technique, a multi-objective evolutionary stochastic optimization approach is proposed to solve this problem. For the first time, the single-objective-guided multi-objective evolutionary algorithm is adapted to handle discrete optimization problems. The iterative scenario selection-based scenario generation approach is used in evaluating the candidate solutions to the problem with uncertain orders. Extensive experimental results demonstrate the effectiveness of the proposed approach and the necessity of considering uncertain orders in real-world production planning.