Journal
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
Volume 56, Issue 12, Pages 4264-4282Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/00207543.2017.1413257
Keywords
multi-stage inventory; switching costs; machine learning; robust optimisation; MILP
Categories
Funding
- National Key Research and Development Program of China [2016YFB0901900]
- Fund for the National Natural Science Foundation of China [61374203]
- Fund for Innovative Research Groups of the National Natural Science Foundation of China [71621061]
- Major International Joint Research Project of the National Natural Science Foundation of China [71520107004]
- 111 Project [B16009]
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In this paper, we seek robust policies for a multi-stage production/inventory problem to minimise total costs, including switching, production, inventory or shortage costs. While minimising switching costs often leads to non-convexity in the model, 0-1 variables are introduced to linearise the objective function. Considering the impossibility of obtaining the exact distribution of uncertain demand, we study the production/inventory problem under worst cases to resist uncertainty. In contrast to traditional inventory problems. unexpected yields in production are considered. Robust support vector regression is developed to approximate the yields of each unit. A mixed-integer linear programming is proposed, employing the duality theory to address the min-max model. A practical case study from cold rolling is considered. Experiments on the actual steel production data are reported to illustrate the validity of the proposed approach.
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