ABSTRACT Global imperatives, such as climate change, environmental concerns, and carbon emissions, make green transformation an inevitability in the logistics sector. Green logistics strategy formulation is an economic choice problem, but it also turns out to be a multicriteria decision‐making (MCDM) process that encompasses the triple bottom line (TBL) of sustainability. MCDM techniques are widely used in current state‐of‐the‐art, but the conventional model has limitations in modeling the stochastic nature of this process. This research, for the first time, uses a novel framework based on the bipolar fuzzy set theory‐double normalization‐based multiple aggregation (DNMA) method, which combines the Copula operator, as well as the Dombi operator, for more accurate decision‐making for green logistics strategies formulation. The use of the Copula–Dombi operator provides greater universality and flexibility, enabling a more realistic assessment of stakeholders' aggregated preferences for this process. The Copula–Dombi DNMA model allows flexible sequencing of green logistics strategies, leading to more accurate results despite uncertainty. The proposed evaluation structure, including the identified criteria and strategies, operationalizes three pillars of sustainability simultaneously within a unified decision‐making framework. By embedding these interdependent sustainability dimensions into a bipolar fuzzy decision environment, the proposed model extends the TBL framework from a conceptual sustainability paradigm to a quantitatively operational and uncertainty‐aware decision‐support structure. Sensitivity analysis of the results confirms that geography, government credence, and regulatory and compliance issues are the most important strategies in this phase. On the other hand, time, energy, and stakeholders are the most important factors influencing this study. This method introduces an entirely new form of decision‐making normalization that includes stakeholder interaction, unlike other MCDM process models.
Görçün et al. (Tue,) studied this question.
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