One of the primary concerns on supply chain management is to select appropriate suppliers based on different criteria, which are often in serious conflict. In this paper, we present a fuzzy TOPSIS technique to prioritize suppliers on reverse logistic network based on different criteria and then the proposed model of this paper applies fuzzy linear programming technique to determine optimum order quantity associated with each supplier. There are three criteria associated with each supplier including technical skills, equipment and ability to analyze. The implementation of the proposed method has been demonstrated in case study.

With an ongoing progress in industries and technologies, most firms and businesses highly depend on cooperation and collaborations within a supply chain. In today’s competitive business environment, customer satisfaction plays an important role for the business survival. These matters can be secured by “supply chain”, a chain consists of various companies working together to maintain customers’ satisfaction. If various stages of different chains cooperate with each other, “supply chain network” (SCN) will be the outcome. In order to have a supply chain network or just a supply chain-operation in its best possible way, some optimization techniques are needed. In this paper, we present a close look at previous studies in the field of SCN optimization techniques and classify them based on the relative important characteristics.

Many supply chain problems are involved with different parameters, which are under uncertainties. One of the primary concerns on supplier selection is to handle the uncertainty under different circumstances. The primary objective of this paper is to design a model to select suppliers and to determine the amount of purchase from any supplier in the supply chain system. For this purpose, we select the most important criteria using fuzzy questionnaires where the questionnaire uses experts’ opinions in terms of linguistic values. Then, a hierarchy multiple criteria decision-making (MCDM) model based on fuzzy-sets theory is proposed to rank different suppliers and using a goal programming approach, we determine the amount of order product from each supplier. The implementation of the proposed model is demonstrated using a real-world case study.

Reverse logistics plays a very critical role in the overall strategy of a business and hence need to be very effective in meeting its objectives. Studies have come up with various insights to optimize reverse logistics arrangements within a specific industry or a sector, but presently there is no study which provides an approach to share knowledge drawn out of reverse logistics arrangements, across dissimilar industries and sectors. Such a study is significant because the response to a reverse logistics arrangement is not uniform in an industry or sector in all the countries, due to different market maturity levels, dissimilar consumer behaviour, and the state of the economy itself. Therefore, the purpose of this paper is to provide a guide for logistics planners through which they can utilize the learning outcomes that emerge from dissimilar industries or sectors within the same economy also. The research findings show that the reverse logistics arrangements can be categorised into various types on the basis of origin and reason for return. It is shown that the products with dissimilar characteristics can be grouped together into six types depending on the common supply chain member interests. Further, the reverse logistics arrangements change from one type to another as a product moves across its life stages. It highlights an approach using which the knowledge drawn from a reverse logistics type in one sector/industry can be applied to the same type in another sector/industry, by focusing on the product types, whose return share similar supply chain member interests. Logistics network planners can apply the insights that have emerged from this analysis to effectively design reverse logistics channels.

In this paper, we study a reverse supply chain consists of three layers of the supply chain including suppliers, producers and customers by considering customers’ requirements. In the customer layer, we analyze the customer’s data to identify and fulfill their needs by collecting a list of customers’ views into consideration. In this case, the proposed model analyzes the customers view in the three areas of transport, production and quality and it uses the coding system for getting customers’ opinions. Then, by using the K-means algorithm, which is one of the data analyzing algorithms, the proposed model clusters the data so that similar data enter to the same cluster. The mathematical model is developed for each of the categories and Lingo software package is employed to solve the resulted problem in each category.