Multi-objective optimisation in inventory planning with supplier selection

Supplier selection and inventory planning are critical and challenging tasks in Supply Chain Management. There are many studies on both topics and many solution techniques have been proposed dealing with each problem separately. In this study, we present a two-stage integrated approach to the supplier selection and inventory planning. In the first stage, suppliers are ranked based on various criteria, including cost, delivery, service and product quality using Interval Type-2 Fuzzy Sets (IT2FS)s. In the following stage, an inventory model is created. Then, an Multi-objective Evolutionary Algorithm (MOEA) is utilised simultaneously minimising the conflicting objectives of supply chain operation cost and supplier risk. We evaluated the performance of three MOEAs with tuned parameter settings, namely NSGA-II, SPEA2 and IBEA on a total of twenty four synthetic and real world problem instances. The empirical results show that in the overall, NSGA-II is the best performing MOEA producing high quality trade-off solutions to the integrated problem of supplier selection and inventory planning.