DESMILS : a decision support approach for multi-item lot sizing using interactive multiobjective optimization
Kania, A., Afsar, B., Miettinen, K., & Sipilä, J. (2023). DESMILS : a decision support approach for multi-item lot sizing using interactive multiobjective optimization. Journal of Intelligent Manufacturing, Early online. https://doi.org/10.1007/s10845-023-02112-5
Published inJournal of Intelligent Manufacturing
DisciplineMultiobjective Optimization GroupLaskennallinen tiedeMultiobjective Optimization GroupComputational Science
© 2023 the Authors
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. ...
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Related funder(s)Research Council of Finland
Funding program(s)Research profiles, AoF; Academy Project, AoF
Additional information about fundingThis research was partly funded by LPDP, Indonesian Endowment Fund for Education (grant number S-5302/LPDP.4/2020), and the Academy of Finland (grant numbers 322221 and 311877). The research is related to the thematic research area DEMO (Decision Analytics utilizing Causal Models and Multiobjective Optimization, jyu.fi/demo) of the University of Jyväskylä.
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