Recent models and solution methodologies for optimization problems in supply chain management under fuzziness

Supply chain (SC) involves collaborating with business partners which uniquely specialize on only a few key strategic activities. The network structures formed in SC's have emerged in the last decade with the accelerated developments in globalization, outsourcing and information technology. The complex network structures have introduced novel problems to both industry and academia while traditional complications are yet investigated. The intensification points of SC problems are mainly configuration of distribution networks, forming distribution strategies, trade-off analyses, managing inventory and cash-flow. One of the main challenges in modeling and solving these problems is to deal with the uncertainties involved in the complex nature of SC. Demand has been the main uncertain aspect of the problems of the related literature followed by internal parameters, supplier related parameters, environmental parameters and price. The uncertainty issues have been commonly dealt with fuzzy approaches in the literature. Fuzzy approaches become beneficial under uncertainties such as the absence of data, use of qualitative data or the need for subjective judgments. Hence, fuzzy techniques in SC optimization problems are vastly implemented in the literature. The purpose of this study is basically to summarize the fuzzy techniques employed for SC optimization models, their past applications, solutions algorithms and offer directions for future research.