A hybrid approach development to solving the storage location assignment problem in a picker-to-parts system
Keywords:Warehouse operations, Order picking system, Storage policy, Non-dominated sorting genetic algorithm II (NSGA-II), Additive-veto model
Goal: 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).
Design / Methodology / Approach: 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.
Results: 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.
Limitations of the investigation: One limitation of this work was the consideration of known and predictable data.
Practical implications: 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.
Originality / Value: 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.
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