Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/246372 
Year of Publication: 
2019
Citation: 
[Journal:] Operations Research Perspectives [ISSN:] 2214-7160 [Volume:] 6 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 1-9
Publisher: 
Elsevier, Amsterdam
Abstract: 
We propose herein the application of Benders decomposition with stochastic linear programming instead of the mix integer linear programming (MILP) approach to solve a lot sizing problem under uncertain demand, particularly in the case of a large-scale problem involving a large number of simulated scenarios. In addition, a special purpose method is introduced to solve the sub problem of Benders decomposition and reduce the processing time. Our experiments show that Benders decomposition combined with the special purpose method (BCS) requires shorter processing times compared to the simple MILP approach in the case of large-scale problems. Furthermore, our BCS approach shows a linear relationship between the processing time and the number of scenarios, whereas the MILP approach shows a quadratic relationship between those variables, indicating that our approach is suitable in solving such problems.
Subjects: 
Benders decomposition
Stochastic linear programming
Large-scale problem
Lot sizing problem
Uncertain demand
Persistent Identifier of the first edition: 
Creative Commons License: 
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Document Type: 
Article

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