Abstract
In this study, we present a multiobjective mixed-integer nonlinear programming (MINLP) model to design a closed-loop supply chain (CLSC) from production stage to distribution as well as recycling for reproduction. The given network includes production centers, potential points for establishing of distribution centers, retrieval centers, collecting and recycling centers, and the demand points. The presented model seeks to find optimal locations for distribution centers, second-hand product collection centers, and recycling centers under the uncertainty situation alongside the factory’s fixed points. The purpose of the presented model is to minimize overall network costs including processing, establishing, and transportation of products and return flows as well as environmental impacts while maximizing social scales and network flexibility according to the presence of uncertainty parameters in the problem. To solve the proposed model with fuzzy uncertainty, first, the improved epsilon (ε)-constraints approach is used to transform a multiobjective to a single-objective problem. Afterward, the Lagrangian relaxation approach is applied to effectively solve the problem. A real-world case study is used to evaluate the performance of the proposed model. Finally, sensitivity analysis is performed to study the effects of important parameters on the optimal solution.
Highlights
Manufacturing supply chains have experienced incredible advancements in knowhow including the adoption of efficient technologies, flexibility, mass production enabled by knowledge management, computerization, and rapid reconfigurations over generations of products
We aim at demonstrating the performance and benefits of the suggested model and solution method on a real case which is a tire factory in Iran. e focus points of this industry are high quality, reliability of availability, and costeffectiveness of resources that need to be assured by the closed-loop supply chain (CLSC). e design of the CLSC aims at reducing maximum network costs while meeting customer demand, optimal locations for distribution centers (DCs), collection centers, and recycling centers need to be found
Conclusion is work presents an mixed-integer nonlinear programming (MINLP) multiobjective mathematical model designed to support selecting the best possible choice for distribution, recycling, and collection centers, so that all environmental costs and impacts are minimized while social impact and network flexibility are maximized
Summary
Manufacturing supply chains have experienced incredible advancements in knowhow including the adoption of efficient technologies, flexibility, mass production enabled by knowledge management, computerization, and rapid reconfigurations over generations of products. Sustainability can be defined as “using resources to meet the present needs without compromising the ability of future generations to meet their own Mathematical Problems in Engineering needs” [7] It bases on three pillars [8], i.e., (1) environmental, (2) economic, and (3) social, where the environmental pillar encompasses, e.g., measures such as the reduction of environmentally harmful emissions. E same is valid for mathematical models integrating these aspects We close this gap in providing a multiobjective programming model for design decisions in CLSCs including sustainability expressed by job opportunity creation in rural areas and resilience expressed by network flexibility. We use Lagrangian relaxation to solve the resulting high-dimensional model
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