Natural gas supply chain under uncertainty condition

In today’s competitive world, uncertainty is an integral part of all optimization problems. One of the cases where uncertainty has the greatest impact on optimization issues is SCN design. In most of the conducted studies, parameters such as demand, transportation cost and capacity of tehsils have been published in an uncertain manner. In this type of problem, various methods have been used to control these uncertainty parameters, which can be referred to as fuzzy programming, robust optimization, two-stage stochastic programming, multi-stage stochastic programming, multi-stage fuzzy stochastic programming, fuzzy robust optimization. Each of the mentioned methods has limitations in terms of its implementation. in the fuzzy programming method, there is no deviation from the data collected by experts’ opinions. In probabilistic methods, it is very difficult to determine the exact type of distribution function. Therefore, many researchers have investigated the strengths and weaknesses of each method in their studies. Therefore, in this paper we try to review the strength and weakness of these methods to apply the best approaches for different situations. In this paper, a real-world case study of a natural gas supply chain is investigated. By using concepts related to natural gas industry and the relations among the components of transmission and distribution network, a Five-level supply chain has been introduced and presented schematically.