scholarly journals A fuzzy goal programming approach to solve multi-objective supply chain network design problems

2013 ◽  
Vol 4 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Amir Abbas Kanani Nezhad ◽  
Emad Roghanian ◽  
Zahra Azadi
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Goal Programming ◽  
Fuzzy Goal Programming ◽  
Programming Approach ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Design Problems ◽  
Multi Objective ◽  
Fuzzy Goal

scholarly journals A Fuzzy Goal Programming Approach for Solving Multi-Objective Supply Chain Network Problems with Pareto-Distributed Random Variables

2019 ◽  
Vol 27 (04) ◽  
pp. 559-593 ◽  
Author(s):  
Vincent Charles ◽  
Srikant Gupta ◽  
Irfan Ali
Keyword(s):  
Supply Chain ◽  
Goal Programming ◽  
Fuzzy Goal Programming ◽  
Programming Approach ◽  
Supply Chain Network ◽  
Delivery Time ◽  
Demand And Supply ◽  
Multi Objective ◽  
Weighted Goal Programming ◽  
Fuzzy Goal

Uncertainty is unavoidable and addressing the same is inevitable. That everything is available at our doorstep is due to a well-managed modern global supply chain, which takes place despite its efficiency and effectiveness being threatened by various sources of uncertainty originating from the demand side, supply side, manufacturing process, and planning and control systems. This paper addresses the demand- and supply-rooted uncertainty. In order to cope with uncertainty within the constrained multi-objective supply chain network, this paper develops a fuzzy goal programming methodology, with solution procedures. The probabilistic fuzzy goal multi-objective supply chain network (PFG-MOSCN) problem is thus formulated and then solved by three different approaches, namely, simple additive goal programming approach, weighted goal programming approach, and pre-emptive goal programming approach, to obtain the optimal solution. The proposed work links fuzziness in transportation cost and delivery time with randomness in demand and supply parameters. The results may prove to be important for operational managers in manufacturing units, interested in optimizing transportation costs and delivery time, and implicitly, in optimizing profits. A numerical example is provided to illustrate the proposed model.

Sustainable tire supply chain network design and operations planning using a multi objective , scenario-based optimization approach

2021 ◽  
Author(s):  
Reza Yousefi Zenouz ◽  
Aboozar Jamalnia ◽  
Mojtaba Farrokh Farrokh ◽  
Mastaneh Asadi
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Technology Selection ◽  
Mixed Integer ◽  
Programming Approach ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Economic Dimension ◽  
Operations Planning ◽  
Multi Objective

Abstract Each year, millions of tires reach their end of life. Worn-out tires are either buried or burned, both of which harm the environment through polluting the air and groundwater. Companies need to consider their social responsibility, such as employment and regional development, and the environmental impact of their activities when making strategic and operational decisions. This study addresses the closed-loop supply chain network design (SCND) and operations planning problem with regard to the three dimensions of sustainability using a mathematical programming approach. The options of retreading, recycling, and energy recovery together with the use of green technologies are considered to minimize the environmental impacts. The proposed decision model can help supply chain managers in tire manufacturing industry make better-informed decisions in order to achieve the three-fold objectives of sustainability. The developed mathematical model turns out to be a multi-objective, multi-echelon, and multi-product mixed integer linear programming. The model is solved using the Lp-metric method and CPLEX solver. The scenario approach is used to address the uncertainty in demand of new products and the rate of return of worn-out tires. The model solutions are the optimal location of the facilities considering population density and unemployment rate in addition to economic dimension, the optimal amount of allocation, the flow of materials, and the best green technology selection. Sensitivity analysis is also conducted to validate the model and test the robustness of the obtained solutions. Finally, managerial implications are provided.

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