DESMILS: a decision support approach for multi-item lot sizing using interactive multiobjective optimization

Adhe Kania (), Bekir Afsar, Kaisa Miettinen and Juha Sipilä
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Adhe Kania: University of Jyvaskyla, Faculty of Information Technology
Bekir Afsar: University of Jyvaskyla, Faculty of Information Technology
Kaisa Miettinen: University of Jyvaskyla, Faculty of Information Technology
Juha Sipilä: JAMK University of Applied Sciences, School of Technology

Journal of Intelligent Manufacturing, 2024, vol. 35, issue 3, No 24, 1373-1387

Abstract: 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: Lot sizes; Inventory management; Interactive method; Multiple criteria optimization; NIMBUS (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10845-023-02112-5

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