A Bi-objective AHP-MINLP-GA Approach for Flexible Alternative Supplier Selection amid the COVID-19 Pandemic

Wang, Yu-Cheng Chen Toly

A decision maker may hold multiple viewpoints regarding the relative priorities of criteria simultaneously, but this has rarely been considered in past studies. Therefore, this study proposes a bi-objective analytic hierarchy process (AHP)–mixed integer nonlinear programming (MINLP)–genetic algorithm (GA) approach. First, AHP is applied to decompose the decision maker's judgment matrix into several sub-judgment matrices. Each sub-judgment matrix represents a single viewpoint and generates a priority set. To generate diversified priority sets, a bi-objective MINLP problem is solved using a GA, and multiple alternatives can be selected based on these priority sets. The proposed approach has been applied to the real case of choosing diversified alternative suppliers amid the COVID-19 pandemic to assess its effectiveness. Several existing methods were also applied to this case for comparison. Experimental results showed that only the proposed approach was able to diversify the recommended alternative suppliers that were simultaneously optimal, thereby enhancing decision-making flexibility. In addition, the application of GA increased the solution efficiency by up to 75%.