AN INTEGRATED METHODOLOGY FOR SUSTAINABLE SUPPLIER SELECTION AND ORDER ALLOCATION TO TACKLE GLOBAL SUPPLY CHAIN UNCERTAINTIES

The existence of pandemic and natural catastrophe situations have created unprecedented challenges for global supply chains, resulting in uncertainty about the specific demand, cost, and supply of the commodities. This study proposes an integrated framework considering global supply chain uncertainties during sustainable supplier selection and order allocation. The suggested technique is divided into three phases. The best-worst method (BWM) is used to compute the criteria weights and suppliers' performance scores in the first phase. A scenario-based multi-objective linear programming (SMOBLP) model is developed in the second phase. Later, the e-constraint and LP-metrics methods are utilized to determine the Pareto front of the developed SMOBLP model. The technique for order preference by similarity to ideal solution (TOPSIS) is applied in phase three to get an ultimate optimal solution from the non-dominated solutions. Finally, a real-life case study of the garment manufacturing organization is offered to demonstrate the practicability of the proposed methodology.