A1 Journal article (refereed)
DESMILS : a decision support approach for multi-item lot sizing using interactive multiobjective optimization (2024)

Kania, A., Afsar, B., Miettinen, K., & Sipilä, J. (2024). DESMILS : a decision support approach for multi-item lot sizing using interactive multiobjective optimization. Journal of Intelligent Manufacturing, 35(3), 1373-1387. https://doi.org/10.1007/s10845-023-02112-5

JYU authors or editors

Publication details

All authors or editors: Kania, Adhe; Afsar, Bekir; Miettinen, Kaisa; Sipilä, Juha

Journal or series: Journal of Intelligent Manufacturing

ISSN: 0956-5515

eISSN: 1572-8145

Publication year: 2024

Publication date: 10/04/2023

Volume: 35

Issue number: 3

Pages range: 1373-1387

Publisher: Springer

Publication country: United States

Publication language: English

DOI: https://doi.org/10.1007/s10845-023-02112-5

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/86453

Abstract

We propose a decision support approach, called DESMILS, to solve multi-item lot sizing problems with a large number of items by using single-item multiobjective lot sizing models. This approach for making lot sizing decisions considers multiple conflicting objective functions and incorporates a decision maker’s preferences to find the most preferred Pareto optimal solutions. DESMILS applies clustering, and items in one cluster are treated utilizing preferences that the decision maker has provided for a representative item of the cluster. Thus, the decision maker provides preferences to solve the single-item lot sizing problem for few items only and not for every item. The lot sizes are obtained by solving a multiobjective optimization problem with an interactive method, which iteratively incorporates preference information and supports the decision maker in learning about the trade-offs involved. As a proof of concept to demonstrate the behavior of DESMILS, we solve a multi-item lot sizing problem of a manufacturing company utilizing their real data. We describe how the supply chain manager as the decision maker found Pareto optimal lot sizes for 94 items by solving the single-item multiobjective lot sizing problem for only ten representative items. He found the solutions acceptable and the solution process convenient saving a significant amount of his time.

Keywords: decision making; multi-objective optimisation; Pareto efficiency

Free keywords: lot sizes; inventory management; interactive method; multiple criteria optimization; NIMBUS

Contributing organizations

Related projects

Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 2