Thalles Vitelli Garcez tvgarcez@cdsid.org.br Federal University of Pernambuco, Caruauru, PE, Brazil. ABSTRACT 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.

中文翻译：

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混合解决方案开发，解决了点对点系统中的存储位置分配问题

Thalles Vitelli Garcez tvgarcez@cdsid.org.br巴西伯南布哥联邦大学，巴西卡鲁阿鲁。摘要目标：这项研究开发了一种结构化的决策模型，该模型能够使用多个关键绩效指标（KPI）来解决零件选择器系统中的存储位置分配问题（SLAP）。设计/方法论/方法：开发了一种混合方法。为此，考虑了三个适应度函数，使用了多目标遗传算法（MOGA），但可以考虑更多函数。通过MOGA，可以验证大量解决方案并将其简化为Pareto前沿。之后，使用多准则决策（MCDM）方法来选择最佳解决方案。结果：考虑到多个目标，该模型能够找到可行的解决方案，仓库限制和决策者的偏好，以及模拟案例所需的处理时间都微不足道。研究的局限性：这项工作的局限性是对已知和可预测数据的考虑。实际意义：提出的模型旨在帮助面临此类问题的公司，为需要最少信息和运营行动的SLAP提供解决方案。独创性/价值：SLAP是一个NP（不确定性多项式时间）复杂问题，在MOGA之后，解决方案的数量可能仍然很高，以便工程经理（决策制定者DM）做出最终决策。因此，开发的MOGA-MCDM混合方法能够将DM的偏好纳入补偿性观点，