A multi-objective robust possibilistic programming approach to sustainable switchgrass-based bioethanol supply chain network design

Purpose The purpose of this paper is to present a study in developing a cost-effective meat supply chain network design aiming to minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. The developed model was also used for determining the optimum numbers and allocations of farms and abattoirs that need to be established and the optimal quantity flow of livestock from farms to abattoirs and meat products from abattoirs to retailers. Design/methodology/approach A multi-objective possibilistic programming model was formulated with a focus on minimizing the total cost of transportation, the number of transportation vehicles and the delivery time of meat products. Three sets of Pareto solutions were obtained using the three different solution methods. These methods are the LP-metrics method, the ɛ-constraint method and the weighted Tchebycheff method, respectively. The TOPSIS method was used for seeking a best Pareto solution as a trade-off decision when optimizing the three conflicting objectives. Findings A case study was also applied for examining the effectiveness and applicability of the developed multi-objective model and the proposed solution methods. The research concludes that the ɛ-constraint method has the superiority over the other two proposed methods as it offers a better solution outcome. Research limitations/implications This work addresses as interesting avenues for further research on exploring the delivery planner under different types of uncertainties and transportation means. Also, environmentalism has been increasingly becoming a significant global problem in the present century. Thus, the presented model could be extended to include the environmental aspects as an objective function. Practical implications The developed design methodology can be utilized for food supply chain designers. Also, it could be applied to realistic problems in the field of supply chain management. Originality/value The paper presents a methodology that can be used for tackling a multi-objective optimization problem of a meat supply chain network design. The proposed optimization method has the potential in solving the similar issue providing a compromising solution due to conflicting objectives in which each needs to be achieved toward an optimum outcome to survive in the competitive sector of food supply chains network.

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

10.21203/rs.3.rs-168565/v1 ◽

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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