A decentralised multi-objective sustainable supply chain model under intuitionistic fuzzy environment

AbstractDetermining the methods for fulfilling the continuously increasing customer expectations and maintaining competitiveness in the market while limiting controllable expenses is challenging. Our study thus identifies inefficiencies in the supply chain network (SCN). The initial goal is to obtain the best allocation order for products from various sources with different destinations in an optimal manner. This study considers two types of decision-makers (DMs) operating at two separate groups of SCN, that is, a bi-level decision-making process. The first-level DM moves first and determines the amounts of the quantity transported to distributors, and the second-level DM then rationally chooses their amounts. First-level decision-makers (FLDMs) aimed at minimizing the total costs of transportation, while second-level decision-makers (SLDM) attempt to simultaneously minimize the total delivery time of the SCN and balance the allocation order between various sources and destinations. This investigation implements fuzzy goal programming (FGP) to solve the multi-objective of SCN in an intuitionistic fuzzy environment. The FGP concept was used to define the fuzzy goals, build linear and nonlinear membership functions, and achieve the compromise solution. A real-life case study was used to illustrate the proposed work. The obtained result shows the optimal quantities transported from the various sources to the various destinations that could enable managers to detect the optimum quantity of the product when hierarchical decision-making involving two levels. A case study then illustrates the application of the proposed work.

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A Sustainable Supply Chain Model For Promoting Collaboration With Hydrocarbon Sector Suppliers And Contractors

10.2118/179459-ms ◽

2016 ◽

Author(s):

Luis Gabriel Carmona ◽

Fernando Correa

Keyword(s):

Supply Chain ◽

Chain Model ◽

Sustainable Supply Chain ◽

Supply Chain Model

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Multi-Echelon Supply Chain Modeling With Dynamic Continuous Review Inventory Policy

Computer Engineering ◽

10.4018/978-1-61350-456-7.ch609 ◽

2012 ◽

pp. 1505-1521

Author(s):

K. Narayana Rao ◽

K. Venkata Subbaiah

Keyword(s):

Decision Making ◽

Supply Chain ◽

Goal Programming ◽

Chain Model ◽

Continuous Review ◽

Fuzzy Environment ◽

Supply Chain Model ◽

Fuzzy Goal ◽

Supply Chain Modeling ◽

Continuous Review Policy

In this chapter, an integrated procurement, production and distribution supply chain model is developed in fuzzy environment and performance vector of the supply chain is determined by solving strategic model and tactical model iteratively. Mixed integer programming model is formulated through fuzzy goal programming approach in strategic level. In the tactical level, dynamic continuous review inventory policy for controlling of raw material inventory at supplier echelon, finished products at plant echelon and distribution center echelons is assumed. The inventory models are solved by considering the interdependency of economic order quantity and reorder point. The supply chain model, which is developed in fuzzy environment, finds compromise solution with multiple, vague and in-compatible objectives. Fuzzy goal programming techniques provide feasible solutions with flexible model formulation in decision-making problems, which involve human judgments in decision-making. Need for supply chin modeling with dynamic continuous review policy in fuzzy environment and the existing literature are outlined in Introduction. Fuzzy supply chain modeling with dynamic continuous review policy for controlling of the raw materials, finished products at plant and distribution center echelons is described in Fuzzy supply chain modeling section. Flow chart of the methodology is explained in Solution Methodology section. The proposed model is illustrated through a numerical example. Supply chain cost, Volume flexibility and unit costs are determined and presented in Results and Discussion section. Importance of the methodology and future scope is made in Conclusion section. This model finds application in the industries involving continuous production like oil and natural gas, steel manufacturing industries etc

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