A fuzzy multi-objective multi-product supplier selection and order-allocation problem in supply chain under coverage and price considerations: An urban agricultural case study

Abstract

In this paper, a fuzzy multi-objective model is presented to select and allocate order to the suppliers in uncertain conditions, considering multi-period, multisource, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, ordering costs, and timely delivering (or deference shipment quality, or wastages) which are amongst major quality aspects. Partial and general coverage of suppliers with respect to distance and finally suppliers' weights makes the amounts of product orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, Multi-Objective Particle Swarm Optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated Sorting Genetic Algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm can be applied to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analyses.