Third-Party Logistic Selection for Logistic Aggregator Company using Multi-Criteria Decision Making
Muhammad Khakim Hidayad1 & Ditdit Nugeraha Utama1 Show affiliations
- Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina Nusantara University, Jakarta, Indonesia
Journal of Computer Science, Volume 18, Issue 9, pp. 811–820 · doi.org/10.3844/jcssp.2022.811.820
Abstract
The customer of AggregatorCompany for Logistics (ACL) faces a problem when creating an order in the aggregatorlogistics platform. They have difficulty making an order as there are a lot ofthird-Party Logistic (3PL) alternatives available but they do not have anyreferences for which 3PL they should select. This study helps the customer tochoose the best 3PL by determining the suitable criteria. The study's objectiveis to build a Multi-Criteria Decision-Making (MCDM) model for 3PL assessmentbased on historical data. To construct the model in doing the assessment, acombined model based on the Fuzzy AnalyticHierarchy Process (F-AHP) and the Techniquefor Order of Preference by Similarity to Ideal Solution (TOPSIS) Wasmethodically operated. F-AHP was used to weigh the criteria and TOPSISfunctioned to rank and score the 3PL alternatives. Sensitivity analysis is alsoperformed to verify the robustness of the proposed model. This study was takenplace in one of the Indonesian ACLs and the model could be beneficial forIndonesian ACL in evaluating, selecting, and recommending the best 3PL partner.
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Open access. This article is licensed under a Creative Commons Attribution 4.0 International Licence (CC BY 4.0), as printed on the article, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original authors and the source. No permission is needed.
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Hidayad, M. K. & Utama, D. N. (2022). Third-Party Logistic Selection for Logistic Aggregator Company using Multi-Criteria Decision Making. Journal of Computer Science, 18(9), 811–820. https://doi.org/10.3844/jcssp.2022.811.820
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