@article{Fontana_Nepomuceno_Garcez_2020, title={A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system}, volume={17}, url={https://bjopm.org.br/bjopm/article/view/853}, DOI={10.14488/BJOPM.2020.005}, abstractNote={<p><strong>Goal:</strong> This study developed a structured decision model capable of solving the storage location assignment problem (SLAP) in a picker-to-parts system, using multiples key performance indicators (KPIs).</p> <p><strong>Design / Methodology / Approach:</strong> A hybrid approach was developed. For that, a Multi-Objective Genetic Algorithm (MOGA) was used considering three fitness functions, but more functions may be considered. Through MOGA it was possible to verify a high number of solutions and reduce it into a Pareto frontier. After that, a Multiple-Criteria Decision-Making (MCDM) approach was used to choose the best solution.</p> <p><strong>Results:</strong> This model was able to find viable solutions considering multiples objectives, warehouse restrictions and decision makers’ preferences, and the required processing time for the simulated cases was insignificant.</p> <p><strong>Limitations of the investigation:</strong> One limitation of this work was the consideration of known and predictable data.</p> <p><strong>Practical implications:</strong> The proposed model was developed with the purpose of assisting companies that face this type of problem, providing a solution for SLAP requiring the minimum information and operational actions.</p> <p><strong>Originality / Value:</strong> SLAP is a NP (Non-Deterministic Polynomial time) complex problem and, after the MOGA, the number of solution can be still high for the final decision making by the engineering manager (decision maker - DM). Thus, the MOGA–MCDM hybrid approach developed was able incorporate the DM’ preferences into a compensatory view, vetoing alternatives that were worse in any of the KPIs, to recommend a final solution.</p>}, number={1}, journal={Brazilian Journal of Operations & Production Management}, author={Fontana, Marcele Elisa and Nepomuceno, Vilmar Santos and Garcez, Thalles Vitelli}, year={2020}, month={Feb.}, pages={1–14} }