Recently, social awareness, governmental legislations and competitive business environment have spurred researchers to pay much attention to closed-loop supply chain network design. In order to support the arising trend, this paper presents a comprehensive mathematical model for a multi-period, multi-product, multi-modal and bi-objective green closed-loop supply chain. The objective of the model is to minimize the total cost and environmental emissions through making the best decisions on facility location, transportation amounts and inventory balances. According to the inherent complexity of the problem and considering multi-product, multi-period and multimodality assumptions makes it hard to handle, and as for the solution approach, an effective accelerated benders decomposition algorithm is implemented. Then, computational results for a set of numerical example are discussed. Besides, the model and solution approach are applied on a wire-and-cable industry. Then, a sensitivity analysis is implemented in an effort to validate the model. Results reveal applicability of the proposed mathematical model and presented solution approach. Following the obtained results, it can be validly concluded that the suggested solution approach leads to more than 13 percent reduction in total cost for the studied case, and can be even employed for larger and more complex real-world industrial applications.

No abstract

No abstract

Abstract. This paper presents a multi-product, multi-period inventory problem in an uncertain environment where the main suppliers are prone to yield uncertainty. In order to overcome the arisen uncertainties, two basic approaches of emergency ordering and product substitutability are taken into consideration. In the proposed emergency ordering scheme, two sets of suppliers, i.e. cheap unreliable and expensive reliable (emergency) suppliers, are considered and a tradeo between the cheap price of the main suppliers and reliability of emergency supplier is attained. In product substitution scheme, the demand of each product is ful lled directly by the related product or other substitute products. A riskaverse decision maker is taken into consideration whose risk-averseness level is controlled by the portion of demand which should be de nitely satis ed and not backordered or lost. A robust optimization approach with two variability measures is proposed to minimize the variability of the model. The results reveal the value of emergency ordering and product substitution. In addition, the results suggest which measure should be selected according to the decision maker's attitude toward the desired pro t, variability, and service level.

No abstract