Over time, the concepts of cost, time, delivery, space, quality, durability, and price began to gain more significance in the emerging trends of information technology when it comes to solving managerial decision-making problems in supply chain models, transportation issues, inventory control issues, and related areas. In uncertain situations, competition is becoming more difficult by the day. For instance, a number of diverse aspects, such as the cost of manufacturing and one's degree of income, frequently influence consumer demand. In these situations, the demand is either still unmet or remarkably difficult to meet in the real-world market. Due to their numeric membership functions, fuzzy sets cannot always represent this uncertainty explicitly. However, it has been discovered that type two, random fuzzy, bifuzzy, and fuzzy random sets are more suited to account for intrinsic uncertainty. These various fuzzy systems are able to manage greater degrees of ambiguity in increasingly difficult real-world issues. However, it is crucial to employ optimization techniques in order to obtain the ideal design as diverse fuzzy systems grow more complicated to conceive. This Reprint highlights novel modeling techniques and fuzzy optimization strategies applied to real-world problems, including sustainable green supply chain systems, relief logistics under uncertainty, and cloud-based software prediction, alongside theoretical developments in fuzzy systems.
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