A hierarchical analytical model for performance management of integrated logistics

Abstract

In this paper, a holistic hierarchical analytical model is proposed to assess the performance of enablers in an integrated logistics system. Due to the ambiguous and complex environment, various refinements are needed to assess enablers and prioritize for the criteria such as economic, operational, and environment. The proposed hierarchical model is developed by a systematic approach that includes fuzzy analytical hierarchy process (FAHP), triangular fuzzy numbers (TFN), an evidential reasoning algorithm (ERA), and expected utility theory (EUT). The FAHP is used to analyze and obtain the weights of the criteria by considering the expert’s opinions. ERA is used to synthesize the enablers based on the selected criteria. These enablers are represented using subjective assessment along with a set of evaluation grades for a qualitative attribute. EUT helps in obtaining crisp values of enablers for their performance estimation. With these set of methodologies, a hierarchical model is proposed that prevent low flexibility and inadequate appropriateness of the proposed model. Further, the model helps in scenario generation for the logistics professionals who are facing various problems in integrating logistics and incorporating sustainability due to lack of appropriate methodologies and evaluation techniques. Finally, sensitivity analysis is used for overall model validation.