A minimax p-robust optimization approach for planning under uncertainty

Kwang Kyu Seo, Jun Kim, Byung Do Chung

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

Planning under uncertainty is one of the important issues in production planning. The development of mathematical models under uncertainty has long been studied in order to avoid the impact of uncertain factors and to maintain stable and excellent performance of manufacturing system. In this paper, we propose a minimax p-robust production planning problem in the presence of parameter uncertainty. Particularly, we apply p-robust measure to a multi-period production planning and inventory control problem considering a set of demand scenarios. A scenario based robust optimization problem is extended to a minimax p-robust optimization problem by combining a p-robustness measure and a minimax objective function. The proposed model is compared with a deterministic model and a minimax model using simulation experiments. The results show that the minimax p-robust solution improves the average cost compared to other approaches while maintaining similar level of worst-case cost from the minimax model.

Original languageEnglish
Pages (from-to)1-10
Number of pages10
JournalJournal of Advanced Mechanical Design, Systems and Manufacturing
Volume9
Issue number5
DOIs
Publication statusPublished - 2015

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2014R1A1A1002934)

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Planning(NRF-2014R1A1A1002934)

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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