Abstract
ABSTRACTIn this study, the application of type-II fuzzy sets is addressed to design a multi-product, multi-level, multi-period supply chain networks. The proposed model provides integrated approach to make optimal decisions such as location–allocation, production, procurement and distribution subject to operational and tactical constraints. In the context of fuzzy linear programming, this study involves type-II fuzzy numbers for the right-hand side of constraints regarding three sources of uncertainty: demand, manufacturing and supply. According to fuzzy components considered, a type-II fuzzy mixed-integer linear programming is converted into an equivalent auxiliary crisp model using linear fuzzy type-reducer models. The final models are linear and the global optimum solutions can be achieved using commercial OR softwares. The contributions of this study are three folds: (1) introducing a new integrated supply chain network design problem; (2) considering a solution procedure based on type-II fuzzy sets and (3) presenting a linear fuzzy type-II reducer. Finally, the proposed model and solution approach are illustrated through a numerical example to demonstrate the significance.
Highlights
Chain (SC) is a network of organisations, suppliers and distributors that produces value in the form of products and services and bring them to ultimate customer or market [1, 2]
Fuzzy linear programming (FLP) is an optimisation technique applied in real-world problems with imprecise data
A new multi-echelon, multi-product, multi-period location–allocation production and distribution supply chain network model with type-II fuzzy sets with due attention to the model proposed by Tsiakis and Papageorgiou [14] is developed
Summary
Chain (SC) is a network of organisations, suppliers and distributors that produces value in the form of products and services and bring them to ultimate customer or market. Uncertainty is presented as variability and inexact forecasting of supplier operations and demand, and poorly reliable production and manufacturing process [5]. The uncertainty in SC has already been modelled through several paradigms such as robust optimisation [6, 7], stochastic programming [8, 9], fuzzy mathematical programming [5, 10] and hybrid models [11]. Due to our best knowledge, there is no study which addresses the type-II fuzzy numbers on integrated location–allocation, production and distribution activities in supply chain. Considering different sources of uncertainty in demand, manufacturing and supply processes affecting the SC.
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