ED-ELEC: A Robust Multi-Criteria Decision-Making Framework for Evaluating Sustainable Urban Delivery Systems

Document Type : Research Paper

Authors

1 . PhD Candidate, Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.

2 Prof., Faculty of Industrial Management & Technology, College of Management, University of Tehran, Tehran, Iran.

10.22059/imj.2026.405611.1008271

Abstract

Objective: The rapid expansion of online food delivery platforms has intensified the need for sustainable and efficient logistics solutions. Evaluating delivery alternatives across economic, environmental, technical, and social dimensions requires robust multi-criteria decision-making (MCDM) tools capable of managing incomplete, uncertain, and conflicting expert judgments. This study develops and validates ED-ELEC, an enhanced MCDM framework that extends D-numbers theory and integrates it with the ELECTRE III outranking method to enable reliable and transparent decision-making under imperfect information.
Methodology: A new integration function for D-numbers is introduced to convert incomplete expert evaluations into crisp values while retaining the effects of incompleteness and conflict. A two-stage validation was performed: first, an illustrative example compared the proposed integration function with existing ones and benchmarked the complete ED-ELEC framework against another hybrid method; second, the framework was applied to evaluate sustainable delivery systems involving gasoline motorcycles, electric motorcycles, and drones.
Results: The proposed integration function achieved balanced sensitivity to uncertainty and better interpretability. ED-ELEC produced robust, transparent, and stable rankings, identifying electric motorcycles as the most sustainable option, followed by drones and gasoline motorcycles.
Conclusion: The ED-ELEC framework effectively combines uncertainty modeling and outranking analysis, providing an adaptive decision-support tool for sustainability evaluation. It enables decision-makers to address incomplete data while improving ranking clarity and robustness in sustainable urban logistics planning.

Keywords

References

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Industrial Management Journal
Volume 18, Issue 2
2026
Pages 34-73

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