Flexible Data Driven Inventory Management with Interactive Multiobjective Lot Size Optimization
| Autor: | Vesa Ojalehto, Juha Sipilä, Kaisa Miettinen, Risto Heikkinen |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Pareto optimality
decision support Information Systems and Management Computer science inventory management data driven optimisation päätöksenteko myynti lot sizing päätöksentukijärjestelmät Management Science and Operations Research Management Information Systems Data-driven Inventory management multicriteria optimisation toimitusketjut optimointi Bayesian models varastot pareto-tehokkuus bayesilainen menetelmä interactive methods Industrial engineering demand forecasting monimuuttujamenetelmät kysyntä analyysi varastonvalvonta ennustettavuus mallit (mallintaminen) |
| Zdroj: | International Journal of Logistics Systems and Management. 1:1 |
| ISSN: | 1742-7975 1742-7967 |
| DOI: | 10.1504/ijlsm.2021.10042370 |
| Popis: | 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 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|