A multi-stage stochastic programming for lot-sizing and scheduling under demand uncertainty. (May 2018)

• Record Type: Journal Article
• Authors: Hu, Zhengyang; Hu, Guiping
• Is Part Of: Computers & industrial engineering. Volume 119(2018)
• Page Start: 157
• Page End: 166
• Publication Date: 2018-05
• Subjects: Multi-stage stochastic programming -- Lot-sizing and scheduling -- Demand uncertainty -- Automotive industry; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2018.03.033
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: We proposed a multi-stage stochastic lot-sizing and scheduling model. We identified number of scenarios to balance solution quality and computation time. Big EVPI and VSS values indicate the importance of considering uncertainty. The multi-stage solution was improved by 10%, comparing to the two-stage solution. Abstract: A stochastic lot-sizing and scheduling problem with demand uncertainty is studied in this paper. Lot-sizing determines the batch size for each product and scheduling decides the sequence of production. A multi-stage stochastic programming model is developed to minimize overall system costs including production cost, setup cost, inventory cost and backlog cost. We aim to find the optimal production sequence and resource allocation decisions. Demand uncertainty is represented by scenario trees using moment matching technique. Scenario reduction is used to select scenarios with the best representation of original set. A case study based on a manufacturing company has been conducted to illustrate and verify the model. We compared the two-stage stochastic programming model to the multi-stage stochastic programming model. The major motivation to adopt multi-stage stochastic programming models is that it extends the two-stage stochastic programming models by allowing revised decision at each period based on the previous realizations of uncertainty as well as decisions. Stability test and weak out-of-sample test are applied to find an appropriate scenario sample size. By using the multi-stage stochastic programming model, we improved the quality of solution by 10–13%.