As a basic part of organizations’ logistics management, purchasing function has supplier selection as one of its main responsibilities. One of the main objectives a buyer follows in supplier selection is to determine optimal quota to be allocated to each supplier. How to allocate orders to different suppliers is as important task as it may affect efficiency of the whole chain. Also, due to variations in real-world business environment, order allocation process is always associated with uncertainties that make it complicated. Therefore, a three-stage integrated framework with environmental uncertainties considered is proposed to allocate orders; this framework can determine qualified suppliers to whom it assigns optimal quota. Considering multi-period purchases and uncertainties, this framework presents a multi-objective nonlinear programming model to determine optimal quota to be allocated to each qualified supplier within each specified period. In order to have the order allocation process closer to real-world cases while increasing the reliability of the obtained solutions, time value of money, inflation, transportation modes, supplier’s profit, and pricing strategy are considered in this model. According to uncertain structure of the proposed model, a solution strategy is proposed to convert this model into a single-objective deterministic model. Then, the resulted single-objective deterministic model is solved by proposing three evolutionary metaheuristic algorithms based on cuckoo optimization algorithm and imperialist competitive algorithm. Finally, a sample problem is presented together with some statistical tests and sensitivity analyses to assess and examine the proposed framework.
