Ranking Logistics System Configurations Based on Flexibility and Elasticity: A Case Study Using IMF SWARA-MABAC Approach

Zhouyu Zeng; Jinbo Song; Mengchun Zhou; Jianjun Wang

doi:10.31181/dmame7120241439

Authors

Zhouyu Zeng School of Transportation Management, Jiangxi Vocational and Technical College of Transportation, Nanchang 330013, China https://orcid.org/0009-0000-2996-4768
Jinbo Song School of Transportation Management, Jiangxi Vocational and Technical College of Transportation, Nanchang 330013, China & Key Laboratory of Maintenance and Intelligence of Transportation Infrastructure of Jiangxi Education Institutes, Nanchang 330013, China https://orcid.org/0000-0003-2779-953X
Mengchun Zhou Shangpu Expressway Project Construction Management Co., Ltd. in Shangrao City, Shangrao 334499, China https://orcid.org/0009-0007-1698-6627
Jianjun Wang Jiangxi Provincial Highway and Bridge Engineering Co., Ltd, Nanchang 330013, China https://orcid.org/0009-0007-4980-4412

DOI:

https://doi.org/10.31181/dmame7120241439

Keywords:

Logistics, System, Flexibility, Elasticity, IMF SWARA, MABAC

Abstract

Flexibility equips organisations with the capability to respond effectively to fluctuating market dynamics. This encompasses managing delivery delays, modifying delivery schedules and frequencies, and accommodating alterations in order requirements. Multiple forms of flexibility exist, each possessing unique attributes, which are explored further within the study. Conversely, elasticity within logistics typically denotes the supply chain's ability to reorganise and preserve core functions amidst internal or external disruptions. It represents the system’s potential to either restore its original condition or adapt to altered circumstances while maintaining an acceptable performance threshold. Given these considerations, the relevance of flexibility and elasticity to a logistics enterprise and its operational mechanisms is apparent. In response to this, the central objective of the study was to construct a detailed framework for ranking logistics systems according to these two attributes. To achieve this, the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF SWARA) technique was applied to ascertain the weighting of evaluation criteria, whereas the Multi-Attributive Border Approximation Area Comparison (MABAC) method facilitated the ranking procedure. The framework was tested by assessing five separate logistics systems across six pertinent criteria. Results demonstrated that, in terms of both flexibility and elasticity, Alternative A5—Automated Smart Logistics incorporating Internet of Things (IoT) sensors—emerged as the most effective system

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