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All Journal JUTI: Jurnal Ilmiah Teknologi Informasi Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE)
Junhai Wang
Zhejiang Technical Institute of Economics
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Library & Information Science

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Decision Support System for Determining Strategic Warehouse Locations Using a Combination of the WENSLO Weighting and RAWEC Method Junhai Wang; Setiawansyah Setiawansyah; Temi Ardiansah; Faruk Ulum; Sumanto Sumanto
JUTI: Jurnal Ilmiah Teknologi Informasi Vol. 24, No. 1, January 2026
Publisher : Department of Informatics, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24068535.v24i1.a1456

Abstract

Determining the location of a strategic warehouse is a crucial decision in supply chain management as it directly affects distribution efficiency, logistics costs, and service levels. This problem is multi-criteria and complex, requiring an approach that can accommodate differences in the importance of criteria as well as variations in performance among alternatives objectively. This study aims to develop a Decision Support System to determine a strategic warehouse location by combining the Weights by Envelope and Slope (WENSLO) weighting method and the Ranking of Alternatives with Weights of Criterion (RAWEC) ranking method. The WENSLO method is used to generate criteria weights based on the nonlinear strength of each criterion, while the RAWEC method is applied to calculate the final values and determine the ranking of warehouse location alternatives. A case study was conducted on eleven alternative locations with the main criteria including location cost, accessibility, safety, distribution travel time, and proximity to suppliers. The study results showed that Location TR obtained the highest final score of 0.9673 and was designated as the top priority warehouse location, followed by Location RD with a score of 0.6235 and Location HO with a score of 0.338, while Location QC had the lowest score of −0.975. These findings demonstrate that the combination of the WENSLO and RAWEC methods can produce rankings that are objective, consistent, and easy to interpret, making them a reliable decision-support tool for determining strategic warehouse locations and potentially applicable to other logistics and distribution problems.
Decision Support System for Evaluating Textile Supplier Performance Based on Weights by Envelope and Slope and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria Setiawansyah Setiawansyah; Junhai Wang; Pritasari Palupiningsih; Sufiatul Maryana
Journal of Computer Science, Information Technology and Telecommunication Engineering Vol 7, No 1 (2026)
Publisher : Universitas Muhammadiyah Sumatera Utara, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30596/jcositte.v7i1.29131

Abstract

The textile industry is highly dependent on supplier performance in ensuring the quality of raw materials, timely delivery, price stability, and supply continuity. The complexity of supplier evaluation involving many criteria often leads to subjectivity and inconsistencies in decision-making when using conventional approaches. This study proposes a decision support system to evaluate textile supplier performance based on a combination of Weights by Envelope and Slope (WENSLO) and Mixed Aggregation by Comprehensive Normalization Technique for Multi-Criteria (MACONT). The WENSLO method is used to determine the weight of criteria objectively based on data distribution characteristics, while MACONT is applied to assess and rank supplier alternatives through a comprehensive normalization and aggregation process. The case study was conducted involving nine suppliers and five evaluation criteria, namely material quality, timeliness, price, supply capacity, and responsiveness. The results of the study indicate that the proposed model is capable of producing clear and stable supplier rankings, with Supplier A9, Supplier A7, and Supplier A2 occupying the top three positions. These findings demonstrate that the integration of WENSLO and MACONT can enhance the objectivity and consistency of decision-making, as well as provide a more reliable and relevant framework for evaluating textile suppliers to support data-driven supply chain management.
Co-Authors Pritasari Palupiningsih Setiawansyah Setiawansyah Sufiatul Maryana Sumanto, Sumanto Temi Ardiansah Ulum, Faruk