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Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries

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  • Gürler, Hasan Emin
  • Özçalıcı, Mehmet
  • Pamucar, Dragan

Abstract

One of the most critical metrics for evaluating countries’ logistics performance is the Logistics Performance Index (LPI). Although the LPI is an effective tool, it is also an index with some concerns regarding performance evaluation. The two main concerns with LPI are assuming equal criterion weights and ignoring operational logistics performance and macroeconomic indicators. To overcome these problems, this study presents a logistics performance evaluation model in which the criteria weights are determined by GA. This study used 11 techniques to determine the logistics performances of the EU countries across 33 indicators. Employing more than one MCDM techniques enabled robustness and consistency. Median, Mean, Linear and K nearest neighbor techniques are implemented to impute the missing values in the dataset. The Linear Regression method is found as the best-performing imputation technique among other techniques for the data of this research. The performance of the GA is compared with other well-known criteria weight determination procedures namely CRITIC, Entropy and equal weight. The ranking results of the MCDM tools were integrated with the Copeland method. The genetic algorithm assigned the highest three weights to the Goods, Value of Exports, Quality of roads, and GDP per capita, indicating that these criteria play an essential role in the countries' logistics performance. The importance of these criteria is not captured by other weight sets. The correlation with the actual LPI scores is higher with Genetic algorithm compared with other weight determination techniques (CRITIC, Entropy, equal weight). Moreover, random weights are created and the results are examined. Weight simulation indicates that, some of the countries ranking are higher than the other countries, regardless of the weights of the criteria. The paper's novelty is to use the Genetic Algorithm (GA) as a criteria weight determination tool for MCDM methods. The proposed system can be used as a decision support system for evaluating the logistics performances of countries. The LPI index is published every two years, but if an automatic or semi-automatic system can be developed using the proposed approach, the logistics performance ranking of countries can be examined more frequently. This can help countries that want to rank high on the LPI ranking determine which criteria to focus on to improve their logistics performance.

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  • Gürler, Hasan Emin & Özçalıcı, Mehmet & Pamucar, Dragan, 2024. "Determining criteria weights with genetic algorithms for multi-criteria decision making methods: The case of logistics performance index rankings of European Union countries," Socio-Economic Planning Sciences, Elsevier, vol. 91(C).
    • Handle: RePEc:eee:soceps:v:91:y:2024:i:c:s0038012123002707
    DOI: 10.1016/j.seps.2023.101758
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    1. Vosooghi, Zeinab & Mirzapour Al-e-hashem, S.M.J. & Lahijanian, Behshad, 2022. "Scenario-based redesigning of a relief supply-chain network by considering humanitarian constraints, triage, and volunteers’ help," Socio-Economic Planning Sciences, Elsevier, vol. 84(C).
    2. Jarosław Brodny & Magdalena Tutak, 2020. "Analyzing Similarities between the European Union Countries in Terms of the Structure and Volume of Energy Production from Renewable Energy Sources," Energies, MDPI, vol. 13(4), pages 1-37, February.
    3. Bahadir Fatih Yildirim & Burcu Adiguzel Mercangoz, 2020. "Evaluating the logistics performance of OECD countries by using fuzzy AHP and ARAS-G," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 10(1), pages 27-45, March.
    4. Luisa Martí & Juan Carlos Martín & Rosa Puertas, 2017. "A DEA-logistics performance index," Journal of Applied Economics, Universidad del CEMA, vol. 20, pages 169-192, May.
    5. Alamoodi, A.H. & Zaidan, B.B. & Zaidan, A.A. & Albahri, O.S. & Chen, Juliana & Chyad, M.A. & Garfan, Salem & Aleesa, A.M., 2021. "Machine learning-based imputation soft computing approach for large missing scale and non-reference data imputation," Chaos, Solitons & Fractals, Elsevier, vol. 151(C).
    6. Camacho-Sanchez, Carolay & Yie-Pinedo, Ruben & Galindo, Gina, 2023. "Humanitarian demining for the clearance of landmine-affected areas," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    7. Guo, Jia & Bard, Jonathan F., 2023. "A three-step optimization-based algorithm for home healthcare delivery," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    8. Lin, Hong-Zhi & Zhang, Yongping, 2023. "Scientific planning of urban cordon sanitaire for desired queuing time," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    9. Paul D. Larson, 2021. "Relationships between Logistics Performance and Aspects of Sustainability: A Cross-Country Analysis," Sustainability, MDPI, vol. 13(2), pages 1-13, January.
    10. Wang, Congke & Liu, Yankui & Yang, Guoqing, 2023. "Adaptive distributionally robust hub location and routing problem with a third-party logistics strategy," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    11. Rashidi, Kamran & Cullinane, Kevin, 2019. "Evaluating the sustainability of national logistics performance using Data Envelopment Analysis," Transport Policy, Elsevier, vol. 74(C), pages 35-46.
    12. Tezcan, Barış & Maass, Kayse Lee, 2023. "Human trafficking interdiction with decision dependent success," Socio-Economic Planning Sciences, Elsevier, vol. 87(PA).
    13. Marchetti, Dalmo & Wanke, Peter, 2020. "Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 135(C).
    14. Shang, Kuo-chung & Marlow, Peter B., 2005. "Logistics capability and performance in Taiwan's major manufacturing firms," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 41(3), pages 217-234, May.
    15. Luisa Martí & Juan Carlos Martín & Rosa Puertas, 2017. "A Dea-Logistics Performance Index," Journal of Applied Economics, Taylor & Francis Journals, vol. 20(1), pages 169-192, May.
    16. Jiang, Lijun & Wang, Xifu & Yang, Kai & Gao, Yiwen, 2023. "Bilevel optimization for the reorganization of inland river ports: A niche perspective," Socio-Economic Planning Sciences, Elsevier, vol. 86(C).
    17. Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2022. "A new artificial neural networks algorithm to analyze the nexus among logistics performance, energy demand, and environmental degradation," Structural Change and Economic Dynamics, Elsevier, vol. 60(C), pages 315-328.
    18. Hao, Yuchen & Liu, Chuang & Zhao, Lugang & Liu, Weibo, 2023. "A dual-clustering algorithm for a robust medical grid partition problem considering patient referral," Socio-Economic Planning Sciences, Elsevier, vol. 88(C).
    19. Rezaei, Jafar & van Roekel, Wilco S. & Tavasszy, Lori, 2018. "Measuring the relative importance of the logistics performance index indicators using Best Worst Method," Transport Policy, Elsevier, vol. 68(C), pages 158-169.
    20. Ecer, Fatih, 2021. "A consolidated MCDM framework for performance assessment of battery electric vehicles based on ranking strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
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    1. Kara, Karahan & Yalçın, Galip Cihan & Simic, Vladimir & Baysal, Zeynep & Pamucar, Dragan, 2024. "The alternative ranking using two-step logarithmic normalization method for benchmarking the supply chain performance of countries," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).

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