6533b824fe1ef96bd1280ba6
RESEARCH PRODUCT
Flexible Data Driven Inventory Management with Interactive Multiobjective Lot Size Optimization
Vesa OjalehtoJuha SipiläKaisa MiettinenRisto Heikkinensubject
Pareto optimalitydecision supportInformation Systems and ManagementComputer scienceinventory managementdata driven optimisationpäätöksentekomyyntilot sizingpäätöksentukijärjestelmätManagement Science and Operations ResearchManagement Information SystemsData-drivenInventory managementmulticriteria optimisationtoimitusketjutoptimointiBayesian modelsvarastotpareto-tehokkuusbayesilainen menetelmäinteractive methodsIndustrial engineeringdemand forecastingmonimuuttujamenetelmätkysyntäanalyysivarastonvalvontaennustettavuusmallit (mallintaminen)description
We study data-driven decision support and formalise a path from data to decision making. We focus on lot sizing in inventory management with stochastic demand and propose an interactive multi-objective optimisation approach. We forecast demand with a Bayesian model, which is based on sales data. After identifying relevant objectives relying on the demand model, we formulate an optimisation problem to determine lot sizes for multiple future time periods. Our approach combines different interactive multi-objective optimisation methods for finding the best balance among the objectives. For that, a decision maker with substance knowledge directs the solution process with one’s preference information to find the most preferred solution with acceptable trade-offs. As a proof of concept, to demonstrate the benefits of the approach, we utilise real-world data from a production company and compare the optimised lot sizes to decisions made without support. With our approach, the decision maker obtained very satisfactory solutions. peerReviewed
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-01-01 | International Journal of Logistics Systems and Management |