Assessment of third-party logistics provider using multi-criteria decision-making approach based on interval rough numbers

Third-party logistics (3PL) has involved a significant response among researchers and practitioners in the recent decade. In the global competitive scenario, multinational companies (MNCs) not only improve quality of service and increase efficiency, but also decrease costs by means of 3PL. However, the assessment and selection of 3PL is a very critical decision, comprising intricacy due to the existence of various imprecisely based criteria. Also, uncertainty is an unavoidable part of the information in the decision-making process and its importance in the selection process is relatively high and needs to be carefully considered. Consequently, incomplete and inadequate data or information may occur among other various selection criteria, which can be termed as a multi-criteria decision-making (MCDM) problem. Interval rough numbers are very flexible to model this type of uncertainty occurring in MCDM problems. Thus this paper presents a new integrated interval rough number (IRN) approach based on the Best Worst Method (BWM) and Weighted Aggregated Sum Product Assessment (WASPAS) method along Multi-Attributive Border Approximation area Comparison (MABAC) to evaluate 3PL providers. The hybrid IRN-BWM based methodology is used for computing the priority weights of criteria while IRN-WASPAS and IRN-MABAC are employed to achieve the final ranking of 3PL providers. A computational study is performed to illustrate the proposed approaches along with a sensitivity analysis on different sets of criteria weight coefficient values to validate the stability of the suggested methodology. Consequently, a comparative analysis of the obtained ranking results with their crisp and fuzzy counterparts is also conducted for checking the reliability of the proposed approach. The results display stability in ranking of alternative results and prove the feasibility of the proposed approach to handle MCDM problems with IRNs.