A Robust Optimization Model for Aggregate Production Planning with Postponement Policy

Document Type : Research Paper

Authors

Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

Abstract

The perishable products prices will drop significantly after specified periods, for example a season. Hence, over-production or shortage of such products is associated with loss of profit, respectively. In this paper, optimal aggregate production planning, is determined for the production of perishable products such as seasonal clothing, New Year gifts, calendars and almanacs by postponement policy in uncertainty conditions. The production process for these products is proposed to be divided into two phases, with applying the postponement concept. So, there are three production activities, including direct production, production of semi-finished products, and final assembly. A robust optimization model to solve aggregate production planning for these products will be developed, and the set of data will be used for model validation. The paper model can be used to solve real-world problems of aggregate production planning in an uncertain condition.

Keywords

Main Subjects

References

1.  Aviv, Y. and Federgruen, A. (2001a). “Capacitated multi-item inventory systems with random and seasonally fluctuating demand: implications for postponement strategies”, Management Science, Vol. 47, PP. 512–531.
2. Aviv, Y. and Federgruen, A. (2001b). “Design for postponement: A comprehensive characterization of its benefits under unknown demand distributions”, Operations Research, Vol. 49, No. 4, PP. 578-598.
3. Van Hoek, R. I. (2001). “The rediscovery of postponement a literature review and directions for research”, Journal of Operations Research, Vol. 19, PP. 161–184.
4. Mulvey, J. M., Vanderbei, R. J. and Zenios, S. A. (1995). “Robust optimization of large-scale systems”, Operations Research, Vol. 43, PP. 264–281.
5. Pagh, J. D. and Cooper, M. C. (1998). “Supply chain postponement and speculation strategies: How to choose the right strategy”, Journal of Business Logistics, Vol. 19, PP. 13–33.
6. Garg, A. and Tang, C. S. (1997). “On postponement strategies for product families with multiple points of differentiation”, IIE Transactions, Vol. 29, PP. 641–650.
7. Wang, R. C. and Fang, H. H. (2000). “Aggregate production planning in a fuzzy environment”, International Journal of Industrial Engineering Theory, Applications, and Practice, Vol. 7, PP. 5–14.
8. Baykasoglu, A. (2001). “MOAPPS 1.0: Aggregate production planning using the multiple-objective tabu search”, International Journal of Production Research, Vol. 39, No. 16, PP. 3685-3702.
9. Wang, R. C. and Fang, H. H. (2001). “Aggregate production planning with multipleobjectives in a fuzzy environment”, European Journal of Operational Research, Vol. 133, PP. 521-536.
10. Høyland, K. and Wallace, S. W. (2001). “Generating scenario trees for multistage decision problems”, Management Science, Vol. 47, PP. 295–307.
11. Bai, D., Carpenter, T. and Mulvey, J. (1997). “Making a case for robust optimization models”, Management Science, Vol. 43, No. 7, PP. 895-907.
12. Jang, Y. J., Jang, S. Y., Chang, B. M. and Park, J. (2002). “A combined model of network design and production/distribution planning for a supply network”, Computers & Industrial Engineering, Vol. 43, No. 1, PP. 263-281.
13. Park, Y. (2005). “An integrated approach for production and distribution planning in supply chain management”, International Journal of Production Research, Vol. 43, PP. 1205-1224.
14. Leung, S. C. H. and Ng, W. L. (2007a). “A goal programming model for production planning of perishable products with postponement”, Computers & Industrial Engineering, Vol. 53, No. 3, PP. 531-541.
15. Leung, S. C. H. and Ng, W. L. (2007b). “A stochastic programming modelfor production planning of perishable products with postponement”, Production Planning & Control, Vol. 18, No. 3, PP. 190-202.
16. Leung, S. C., Tsang, S. O., Ng, W. L. and Wu, Y. (2007c). “A robust optimization model for multi-site production planning problem in an uncertain environment”, European Journal of Operational Research, Vol. 181, No. 1, PP. 224-238.
17. Leung, S. C. H. and Chan, S. S. W. (2009). “A goal programming modelfor aggregate production planning with resource utilization constraint”,Computers & Industrial Engineering, Vol. 56, No. 3, PP. 1053-1064.
18. Mirzapour Al-e-hashem, S. M. J., Malekly, H. and Aryanezhad, M. B. (2011). “A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty”, International Journal of Production Economics, Vol. 134, No. 1, PP. 28-42.
19. Ghasemy Yaghin, R., Torabi, S. A. and Fatemi Ghomi, S. M. T. (2012). “Integrated markdown pricing and aggregate production planning in a two echelon supply chain: A hybrid fuzzy multiple objective approach”, Applied Mathematical Modelling, Vol. 36, No. 12, PP. 6011-6030.
20. José Alem, D. and Morabito, R. (2012). “Production planning in furniture settings via robust optimization”, Computers & Operations Research, Vol. 39, No. 2, PP. 139-150.
21. Bertsimas, D. and Sim, M. (2003). “Robust discrete optimization and network flows”, Mathematical Programming, Vol. 98, No. 1-3, PP. 43-71.
22. Rahmani, D., Ramezanian, R., Fattahi, P. and Heydari, M. (2013). “A robust optimization model for multi-product two-stage capacitated production planning under uncertainty”, Applied Mathematical Modelling, Vol. 37, No. 20, PP. 8957-8971.
23. Awudu, I. and Zhang, J. (2013). “Stochastic production planning for a biofuel supply chain under demand and price uncertainties”, Applied Energy, Vol. 103, PP. 189-196.
24. Niknamfar, A. H., Niaki, S. T. A. and Pasandideh, S. H. R. (2014). “Robust optimization approach for an aggregate production–distribution planning in a three-level supply chain”, The International Journal of Advanced Manufacturing Technology, Vol. 76, No. 1-4, PP. 623-634.
25. Jabbarzadeh, A., Fahimnia, B. and Sheu, J. B. (2015). “An enhanced robustness approach for managing supply and demand uncertainties”, International Journal of Production Economics, Vol. 183, PP. 620–631.
26. Mulvey, J. M. and Ruszczynski, A. (1995). “A new scenario decomposition method for large-scale stochastic optimization”, Operations Research, Vol. 43, PP. 477–490.
Advances in Industrial Engineering
Volume 51, Issue 4
ویژه نامه
January 2018
Pages 389-404

Files

Share

How to cite

Statistics