Addressing a Supplier Selection Framework Considering Lot Sizing impact with Quantity Discounts under Disruptional Risks by a hybrid Imperialist Competitive Algorithm

In this paper, a supplier selection and lot sizing problem is addressed by a new scenario-based stochastic mixed integer non-linear programming approach. Also, the proposed model considers that each facility has its resilient level. The related literature is reviewed comprehensively in order to extract resilience measures. According to the literature review, less attention has been reported to quantity discounts between supplier selection networks under disruption risk. In this regard, this study not only develops a supplier selection framework considering lot sizing impact but also adds quantity discounts for the developed problem under disruption risk. To address the developed problem, a hybrid version of Imperialist Competitive Algorithm (ICA) is used. The algorithm is not only compared with its general idea but also Particle Swarm Optimization (PSO) as a well-known swarm algorithm is utilized. The algorithms are validated by an exact solver in small sizes. Finally, results confirm the efficiency of developed model and the performance of proposed algorithms during different criteria in experimental results for the most of test problems.

کلیدواژه ها:

Supplier selection ، Quantity discount ، Disruption risk ، Lot sizing ، Relief distribution plan ، imperialist competitive algorithm