Strategy building for renewable energy adoption in regionalized supply chains-based logistic systems using a hybrid fuzzy decision-making approach

Integrating renewable energy into logistics processes requires improvements in key indicators. Identifying the most essential items is necessary for businesses to allocate their limited resources effectively. However, limited research exists on prioritizing these factors in the supply chains. To satisfy this gap, this study aims to identify key strategies for renewable energy adoption in regionalized supply chain-based logistic systems. A novel model has been constructed that considers the Hartigan-Wong algorithm, Fermatean fuzzy sets, simple weight calculation (SIWEC), and evaluation based on distance from average solution (EDAS) techniques. The main contribution is developing a new model for reducing uncertainties in integrating renewable energy projects into regionalized supply chain-based logistics systems. The results of this model pave the way for determining the right strategies. The selection of the most effective expert team with the Hartigan-Wong algorithm is the main superiority of the proposed model. The opinions of a more qualified expert group are considered when establishing the decision-making model. This situation provides an objective and systematic approach to determining the most qualified expert team. It is concluded that the most essential criteria are advanced technologies for optimized systems and efficient inventory management.