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Abstract

The growing demand for customized products and shorter delivery times has intensified the pressure on logistics and warehouse operations to become more efficient, flexible, and responsive. In this context, selecting an appropriate automated storage and retrieval system (AS/RS) is a complex multi-criteria decision-making (MCDM) problem that requires a systematic, quantitative evaluation approach. This study proposes an integrated decision-making framework that combines the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the most suitable automated storage technology for warehousing operations. Four key criteria were identified—Throughput, Accuracy, Ergonomics, and Space Utilization—and three alternatives were evaluated: Miniload, Vertical Carousel, and Vertical Lift. The AHP method was used to derive the relative importance of the criteria, producing consistent weights with a CR value of 8.28%. These weights were subsequently applied in the TOPSIS analysis to rank the alternatives based on their closeness to the ideal solution. The results indicate that Vertical Lift emerged as the optimal choice (CC = 0.6657), followed by Vertical Carousel (CC = 0.4657) and Miniload (CC = 0.3688). Sensitivity analysis, conducted through both One-at-a-Time (OAT) and scenario-based approaches, confirmed the robustness of the decision model under varying preference scenarios. This research contributes to the body of knowledge on warehouse automation decision-making by providing a transparent, quantitative, and adaptable framework that supports managers in enhancing operational performance and optimizing resource utilization.

Keywords

Warehouse Automation Automated Storage and Retrieval System AHP TOPSIS Multi-Criteria Decision-Making (MCDM)

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Purwanto, C., Ats-Tsauri, M. I., Syahliantina, A., Azizah, G. F., & Pratiwi, C. D. (2026). A Multi-Criteria Decision Making for Automated Storage System Selection in the Warehousing Industry Using AHP-TOPSIS. Golden Ratio of Data in Summary, 6(1), 48–65. https://doi.org/10.52970/grdis.v6i1.1929
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