Green Closed-loop Supply Chain Research Articles

Developing a multi-method simulation model of a green closed-loop supply chain and determining pricing and advertising policy against a competitor

Developing a multi-method simulation model of a green closed-loop supply chain and determining pricing and advertising policy against a competitor

Mathematical Formulation and Solving of Green Closed-loop Supply Chain Planning Problem with Production, Distribution and Transportation Reliability

In this paper, developed a new multi-product, multi-period, and multi-level closed-loop green supply chain planning model under uncertain conditions. The formulated model consists of five objective functions, which minimize the cost of the supply chain, minimize the CO2 emission of transportation vehicles, maximize the reliability of manufacturing and distribution centers, maximize the reliability of the transportation system, and maximize the level of service provided. Therefore, the problem of model formulated as a multi-objective mixed integer nonlinear programming. Also, since the proposed model is complex and NP-hard in large size, therefore, for the investigation of the results, we have used a Non-Dominated Sorting Genetic II Algorithm (NSGA-II). In addition, the small of size results of the problem achieved by GAMS software. Therefore, we try to solve these problems by analyzing and comparing them with the help of these algorithm. For this purpose, various size has been considered.

Risk-Averse Facility Location for Green Closed-Loop Supply Chain Networks Design under Uncertainty

With the increasing attention given to environmentalism, designing a green closed-loop supply chain network has been recognized as an important issue. In this paper, we consider the facility location problem, in order to reduce the total costs and CO2 emissions under an uncertain demand and emission rate. Particularly, we are more interested in the risk-averse method for providing more reliable solutions. To do this, we employ a coherent risk measure, conditional value-at-risk, to represent the underlying risk of uncertain demand and CO2 emission rate. The resulting optimization problem is a 0-1 mixed integer bi-objective programming, which is challenging to solve. We develop an improved reformulation-linearization technique, based on decomposed piecewise McCormick envelopes, to generate lower bounds efficiently. We show that the proposed risk-averse model can generate a more reliable solution than the risk-neutral model, both in reducing penalty costs and CO2 emissions. Moreover, the proposed algorithm outperforms and classic reformulation-linearization technique in convergence rate and gaps. Numerical experiments based on random data and a ‘real’ case are performed to demonstrate the performance of the proposed model and algorithm.

A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount

Abstract With the increasing importance of the efficient purchasing function, supplier selection decisions have become more strategic in supply chain management. In the past few years, the closed-loop green supply chain network has faced increasing attention with regard to the environmental regulations, social awareness and customer pressure. In order to cope with these two issues, this paper proposes an integrated mathematical programming model for multi-period, multi-product and capacitated closed loop green supply chain in which suppliers offer quantity discounts in order to motivate buyers to purchase more. The model objective functions are the minimization of economic cost and environmental emissions and maximization of customer satisfaction with determining best suppliers, purchasing amount, location-allocation facilities, transportation mode, technology type, carbon dioxide emissions, inventory levels and flows between facilities. Sensitivity analysis is conducted to validate the model for some test problems. Computational results show the effectiveness and applicability of the proposed model. Also, results have revealed that supply chain total costs are significantly reduced by considering quantity discount.

A Bi-Objective Green Closed Loop Supply Chain Design Problem with Uncertain Demand

With the development of e-commerce, competition among enterprises is becoming fiercer. Furthermore, environmental problems can no longer be ignored. To address these challenges, we devise a green closed loop supply chain (GCLSC) with uncertain demand. In the problem, two conflict objectives and recycling the used products are considered. To solve this problem, a mathematical model is formulated with the chance constraint, and the ϵ -constraint method is adapted to obtain the true Pareto front for small sized problems. For larger sized problems, the non-dominated sorting genetic algorithm (NSGA-II) and the multi-objective simulated annealing method (MOSA) are developed. Numerous computational experiments can help manufacturers make better production and sales plans to keep competitive advantage and protect the environment.

A robust fuzzy stochastic programming model for the design of a reliable green closed-loop supply chain network

ABSTRACTRecently, governmental legislations, limitation of natural resources and adverse effects of End-of-Life products on ecological system have spurred researchers to design closed-loop supply chains (CLSCs). Accordingly, designing green supply chains (SCs) that manage greenhouse gas emissions and prevent air pollution can be helpful for companies to heighten profitability and customer loyalty, besides, uncertainty of parameters and disruption strikes could adversely affect performance of SCs and lower quality of output decisions. Effective planning prevents great losses and increases reliability of manager's decisions against uncertainties. Therefore, this paper is proceeding to design a reliable bi-objective green CLSC that minimizes total costs of network aside with minimizing harmful gas emissions. The proposed model is capable of controlling adverse effects of disruptions via applying scenario-based stochastic programming approach. Also, an effective hybrid robust fuzzy stochastic programming method is extended to effectively control uncertainty of parameters and risk-aversion level of output decisions. Extended model analyzing is based on lead-acid battery SC case study that output results approve applicability and effectiveness of model.

Fuzzy multi-objective sustainable and green closed-loop supply chain network design

This article addresses a design problem of a closed loop supply chain, including suppliers, manufacturers, distribution centers, customers, warehouse centers, return centers, and recycling centers. The problem entails three choices regarding recycling, namely, product recycling, and components recycling raw material recycling. Modeling this chain is carried out by accounting for environmental considerations, total profit optimization, and reduction of lost working days due to occupational accidents, we well as maximizing responsiveness to customer demand. In order to solve the model, genetic algorithm has been used and multiple scenarios with different aspects have been studied. Solving this model provides decisions regarding opening or closing of each of the components of the network and the optimal product flow among them. The results prove the feasibility of the presented model and the applicability of the developed solution methodology.

Developing an ant colony approach for green closed-loop supply chain network design: a case study in gold industry

The forward/reverse logistics network design is an important and strategic issue due to its effects on efficiency and responsiveness of a supply chain. In practice, it is needed to formulate and solve real problems through efficient algorithms in a reasonable time. Hence, this paper tries to cover real case problem with a multi-objective model and an integrated forward/reverse logistics network design. Further, the model is customized and implemented for a case study in gold industry where the reverse logistics play crucial role. A new solution approach is applied for the proposed 7-layer network of the case study and the solutions are achieved in order solve the current difficulties of the investigated supply chain. This paper seeks to address how a multi objective logistics model in the gold industry can be created and solved through an efficient meta-heuristic algorithm. A green approach based on the CO2 emission is considered in the network design approach. The developed model includes four echelons in the forward direction and three echelons in the reverse. First, an integer linear programming model is developed to minimize costs and emissions. Then, in order to solve the model, an algorithm based on ant colony optimization is developed. The performance of the proposed algorithm has been compared with the optimum solutions of the LINGO software through various numerical examples based on the random data and real-world instances. The evaluation studies demonstrate that the proposed model is practical and applicable and the developed algorithm is reliable and efficient. The results prove the managerial implications of the model and the solution approach in terms of presenting appropriate modifications to the mangers of the selected supply chain. Further, a Taguchi-based parameter setting is undertaken to ensure using the appropriate parameters for the algorithm.

A Hybrid Territory Defined evolutionary algorithm approach for closed loop green supply chain network design

The Closed loop Supply chain network distribution is one of the most important problems with much real world application in supply chain management area. Presently climate change problem is one of the major concerns for Researchers. Closed loop green supply chain (GCLSC) problem is the extension of closed loop supply chain problem. Semiconductor industries are one of the major industries and a number of waste products in semiconductor industries are quite high. We have considered reducing the waste in semiconductor by recycling the useful waste electronic equipment. In GCLSC, we consider to maximize the profit in forward supply chain whereas we attempt to minimize the Carbon footprints at the same time. In this paper we used a hybrid of Estimation of distribution algorithm (EDA) and Territory Defined multi-objective algorithm to select the optimum number of facilities in the closed loop supply chain network. To examine the effectiveness of our Hybrid Territory Defined algorithm (EDATDEA), we compare the results with those obtained by NSGA II on a same GCLSC problem with different problem sizes and the same data sets.

A robust fuzzy optimization model for carbon-efficient closed-loop supply chain network design problem: a numerical illustration in electronics industry

Environmentalism has become an important global issue in the present century. During the recent years, the closed-loop green supply chain management has been increasingly a center of attention as a result of governmental laws and legislations (regarding the environmental effects of any activity) and the soaring consumers' expectations. In this study, effort has been made to investigate a facility location/allocation model for a multi-product closed-loop green supply chain network consisting of manufacturing/remanufacturing and collection/inspection centers as well as disposal center and markets. To design the network, we have proposed a mixed-integer linear programming model capable of reducing the network total costs. The model has been so developed as to consider such environmental objectives as reducing the rate of carbon dioxide emission in the environment throughout the network in question. Moreover, the model has been developed using a robust fuzzy programming approach to investigate the effects of uncertainties of the variable costs, as well as the demand rate, on the network design. To solve the bi-objective programming model, use has been made of the ε-constraint approach and a numerical illustration of Copiers Industry is used to show the applicability of the proposed optimization model. Results have revealed that the model is capable of controlling the network uncertainties as a result of which a robustness price will be imposed on the system.

A Revenue Sharing Model for Closed-Loop Supply Chain of Green Agricultural Products

Revenue sharing is the key to the steady operation of green agricultural products closed-loop supply chain. In addressing issues of revenue sharing, the previously Shapley value model only took the added value of each partner into consideration, regardless of the differences of the members inputs. So it is necessary to be modified. Considering the input factors, strive factors and risk factors respectively, a comprehensive modified Shapley value model is built for the profits allocation of green agriculture products closed-up supply chain.

The Text Mining and Classification Analyses on The Relationship between Green Supply Chain Management and Closed-Loop Supply Chain Research Trends (2000-2010)

In recent years, many researches have been devoted to the topics of the green supply chain management (GSCM) and the closed-loop supply chain (CLSC); however, few have deployed the study of the relationship between the research trend of GSCM and CLSC researches. In fact, grasping the relationship of the research trend between the GSCM and CLSC is imperative for enterprises and researchers. The study aimed at analyzing the trend of the GSCM and CLSC researches from 2000 to 2010. The contribution of the study included the following two points. First, the study found a new way of literature survey by using the text mining method in order to explore the research trend. Second, the study applied the SVM (Support Vector Machine) and random forests classification analysis to explore the relationship between the GSCM and CLSC research trends.