A fuzzy goal programming approach with priority for channel allocation problem in steel industry

[[abstract]]A model is presented to address a steel supplier’s channel allocation problem that includes decisions of channel mix and capacity allocation for each distribution channel. The problem has been formulated as a fuzzy mixed integer multiple goal programming problem that includes business competitive advantages such as maximizing net profits, minimizing the rate of end user claims, and minimizing the rate of late lading, and is subject to constraints regarding manufacturing capacity, customer’s demand, channel capacity, channel quota flexibility, budget limitations, and so on. Realistic data from Taiwan’s largest steel company is implemented for the effectiveness of the model. The proposed model can also be applied to the allocation problem in other industries with preemptive priority and desired achievement level in a fuzzy environment

An overview of fuzzy techniques in supply chain management: bibliometrics, methodologies, applications and future directions

Every practice in supply chain management (SCM) requires decision making. However, due to the complexity of evaluated objects and the cognitive limitations of individuals, the decision information given by experts is often fuzzy, which may make it difficult to make decisions. In this regard, many scholars applied fuzzy techniques to solve decision making problems in SCM. Although there were review papers about either fuzzy methods or SCM, most of them did not use bibliometrics methods or did not consider fuzzy sets theory-based techniques comprehensively in SCM. In this paper, for the purpose of analyzing the advances of fuzzy techniques in SCM, we review 301 relevant papers from 1998 to 2020. By the analyses in terms of bibliometrics, methodologies and applications, publication trends, popular methods such as fuzzy MCDM methods, and hot applications such as supplier selection, are found. Finally, we propose future directions regarding fuzzy techniques in SCM. It is hoped that this paper would be helpful for scholars and practitioners in the field of fuzzy decision making and SCM