maghsoud Amiri; mohsen shafiei nikabadi; Armin Jabbarzadeh
Abstract
In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply ...
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In recent years, the complexity of the environment, the intense competition of organizations, the pressure of governments on producers to manage waste products, environmental pressures and most importantly, the benefits of recycling products have added to the importance of designing a closed loop supply chain network. Also, the existence of inherent uncertainties in the input parameters is another important factor that the lack of attention them can affect the strategic, tactical and operational decisions of organizations. Given these reasons, this research aims to design a multi-product and multi period closed loop supply chain network model in uncertainty conditions. To this aim, first a mixed-integer linear programming model is proposed to minimize supply chain costs. Then, for coping with hybrid uncertain parameters effectively, randomness and epistemic uncertainty, a novel robust stochastic-possibilistic programming (RSPP) approach is proposed. Furthermore, several varieties of RSPP models are developed and their differences, weaknesses, strengths and the most suitable conditions for being used are discussed. Finally, usefulness and applicability of the RSPP model are tested via the real case study in an edible oil industry.
Masoud Rabbani,; Neda Manavizadeh; Amir Farshbaf-Geranmayeh
Volume 13, Issue 37 , July 2015, , Pages 5-35
Abstract
In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness ...
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In this paper, supply chain network design problem is modeled as a fuzzy multi objective mixed integer programming which seeks to locate the plants, DCs, and warehouses by considering disruption, supply and demand risk. Maximizing net present value of supply chain cash flow, minimizing delivery tardiness and maximizing reliability of suppliers are considered as objective functions in the proposed mathematic model. In order to have a more reliable model in case of disruption, the robustness measure is used in the model. Moreover, because of the lack of information, the economic factors such as tax rate, interest rate, and inflation are considered as uncertain factors in the model. An interactive possibilistic programming approach is applied for solving the multi-objective model. To solve larger size instances, genetic algorithm is proposed. Finally numerical examples are presented to show how the model works in practice
