A multi-stage stochastic programming approach in master production scheduling

Date
2011
Authors
Körpeoğlu, E.
Yaman, H.
Aktürk, M. S.
Advisor
Instructor
Source Title
European Journal of Operational Research
Print ISSN
0377-2217
Electronic ISSN
1872-6860
Publisher
Elsevier
Volume
213
Issue
1
Pages
166 - 179
Language
English
Type
Article
Journal Title
Journal ISSN
Volume Title
Abstract

Master Production Schedules (MPS) are widely used in industry, especially within Enterprise Resource Planning (ERP) software. The classical approach for generating MPS assumes infinite capacity, fixed processing times, and a single scenario for demand forecasts. In this paper, we question these assumptions and consider a problem with finite capacity, controllable processing times, and several demand scenarios instead of just one. We use a multi-stage stochastic programming approach in order to come up with the maximum expected profit given the demand scenarios. Controllable processing times enlarge the solution space so that the limited capacity of production resources are utilized more effectively. We propose an effective formulation that enables an extensive computational study. Our computational results clearly indicate that instead of relying on relatively simple heuristic methods, multi-stage stochastic programming can be used effectively to solve MPS problems, and that controllability increases the performance of multi-stage solutions.

Course
Other identifiers
Book Title
Keywords
Controllable processing times, Flexible manufacturing, Master production scheduling, Stochastic programming
Citation
Published Version (Please cite this version)