Evaluating the performance of aggregate production planning strategies under uncertainty in soft drink industry

Jamalnia, Aboozar; Yang, Jian-Bo; Xu, Dong-Ling; Feili, Ardalan; Jamali, Gholamreza

Abstract

The present study is to evaluate the performance of different aggregate production planning (APP) strategies in presence of uncertainty. Therefore, the relevant models for APP strategies including the pure chase, the pure level, the modified chase, the modified level and the mixed chase and level strategies are constructed by using both multi-objective programming and simulation methods.

The models constructed for these strategies are run with respect to the corresponding objectives/criteria in order to provide business insights to operations managers about the effectiveness and practicality of various APP strategies in presence of uncertainty. The real world operational data are collected from soft drink industry to validate and implement the models.

In addition, multiple criteria decision making (MCDM) methods are used besides multi-objective optimisation to assess the overall performance of each APP strategy. A detailed sensitivity analysis is also conducted by changing the criteria weights in MCDM methods to evaluate the impacts that these weight changes can have on the final rank of each APP strategy.

The results of the simulation models are compared to those of multi-objective optimisation models. In general, in both mathematical programming and simulation models, the pure chase and the modified chase strategies presented the best performance, followed by the pure level strategy.

Citation

Jamalnia, A., Yang, J.-B., Xu, D.-L., Feili, A., & Jamali, G. (2019). Evaluating the performance of aggregate production planning strategies under uncertainty in soft drink industry. Journal of Manufacturing Systems, 50, 146-162. https://doi.org/10.1016/j.jmsy.2018.12.009

• Journal Article Type: Article
• Acceptance Date: Dec 18, 2018
• Online Publication Date: Jan 3, 2019
• Publication Date: 2019-01
• Deposit Date: Apr 15, 2024
• Journal: Journal of Manufacturing Systems
• Print ISSN: 0278-6125
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 50
• Pages: 146-162
• DOI: https://doi.org/10.1016/j.jmsy.2018.12.009
• Public URL: http://researchrepository.napier.ac.uk/Output/3593049