Green Closed-loop Supply Chain Research Articles

Green- Closed Loop Supply Chain Network Design by Considering Customer Relationship Management and solving it using Multi-Objective Genetic Algorithm

Background and Objective: Increasing greenhouse gas emissions and pollutants has led organizers and researchers to seek to design and set up networks that focus on environmental factors and reduce pollutants in all sectors. Therefore, the main objective of this paper is to present a fuzzy mathematical programming model for the green closed loop supply chain network, taking into account customer relationship management.Method: This is an applied-development study implemented in Saba Battery Company. In this study, a mixed integer linear programming model is proposed for designing a closed loop supply chain network that seeks to minimize costs and minimize environmental impacts. Also, the concept of customer relationship management in the form of the third objective function has been added to maximize the amount of worn-out product collected to this model.Results: Given that the proposed model belongs to the NP-hard category, a multi-objective genetic algorithm is used to solve the model, and finally, the Pareto's solutions are determined. Based on the results, the two objectives of the economic and environmental objectives are contradictory. That is, moving each one toward the desired one requires movement of the other objective function to the undesirable.Conclusion: In this research, the proposed mathematical programming model has been solved with a multi-objective genetic algorithm, which results indicate the location and capacity of the facility, the amount of production, the amount of inventory and the amount of transportation of the products.

A multi-objective optimal decision model for a green closed-loop supply chain under uncertainty: A real industrial case study

Green closed-loop supply chain management is an important topic for business operations today because of increasing resource scarcity and environmental issues. Companies not only have to meet environmental regulations, but also must ensure high quality supply chain operation as a means to secure competitive advantages and increase profits. This study proposes a multi-objective mixed integer programming model for an integrated green closed-loop supply chain network designed to maximize profit, amicable production level (environmentally friendly materials and clean technology usage), and quality level. A scenario-based robust optimization method is used to deal with uncertain parameters such as the demand of new products, the return rates of returned products and the sale prices of remanufactured products. The proposed model is applied to a real industry case example of a manufacturing company to illustrate the applicability of the proposed model. The result shows a robust optimal resource allocation solution that considers multiple scenarios. This study can be a reference for closed-loop supply chain related academic research and also can be used to guide the development of a green closed-loop supply chain model for better decision making.

Integrated Operational Model of Green Closed-Loop Supply Chain

Due to increasing environmental awareness, companies have started embracing the green supply chain concept to reduce waste of resources. Based on this increased awareness, an integrated green closed-loop supply chain has been developed, which integrates the forward supply chain and reverse supply chain. The reverse supply chain follows the same path as the forward supply chain in the reverse direction to recycle used products. Due to the uncertain quality of used products, not all products can be selected for recycling and reproduction, as the reduced yield might decrease the overall net income in the supply chain. The study develops an evaluation model to consider government subsidy, used product recycling rate, and quality of the used products to explore their impacts on the entire system. The results show that when the reproducibility rate of used raw materials decreases, the net income would also decrease accordingly. Furthermore, when government subsidy increases, the net income of the supply chain also increases accordingly. Similarly, when the recycling rate of used products increases, the net income also increases. As government subsidy affects the net income more than the recycling rate of used products, this research concludes that government subsidy is a key factor in the green closed-loop supply chain.

Designing a Sustainable Green Closed-Loop Supply Chain under Uncertainty and Various Capacity Levels

The ever-increasing concerns of the growth in the volume of waste tires and new strict government legislations to reduce the environmental impact of the end-of-life (EOL) tires have increased interest among companies to design a sustainable and efficient closed-loop supply-chain (CLSC) network. In the real world, the CLSC network design is subject to a variety of uncertainties, such as random and fuzzy (epistemic) uncertainties. Designing a reliable and environmentally cautious CLSC with consideration of risks and the uncertainty of the parameters in the network is necessary for a successful supply-chain network. This study proposes a sustainable and environmentally cautious closed-loop supply-chain network for the tire industry, by considering several recovery options, including retreading, recycling, and energy recovery. This study aims to design and develop a robust multi-objective, multi-product, multi-echelon, multi-cycle, multi-capacity, green closed-loop supply-chain network under hybrid uncertainty. There are two types of uncertainties associated with the parameters in the network. There is an uncertainty associated with the demand, which is expressed in some future scenarios according to the probability of their occurrences, as well as fuzzy-based uncertainty associated with return rates, retreading rates, recycling rates, procurement, and production costs, which are expressed with possibilistic distributions. In order to deal with this hybrid uncertainty, a robust fuzzy stochastic programming approach has been proposed, and the proposed mixed integer programming model is applied to a case study in the tire industry to validate the model. The result indicates the applicability of the proposed model and its efficiency to control the hybrid uncertainties and the risk level in the network.

Decision-making and coordination of green closed-loop supply chain with fairness concern

This paper considers a green closed-supply chain consisting of a manufacturer and a retailer. A Stackelberg game model of centralized decision-making and decentralized decision-making with manufacturer’s fairness concern was constructed based on the consideration of retailer’s sales effort. The decision-making of supply chain members under the above two situations and their reasons are analyzed in depth. According to the model, a green closed-loop supply chain with profit sharing contract coordination fairness is designed. Finally, the correctness of the model is verified by numerical simulation. We generate our findings from three aspects, as follows: when the manufacturer has fair concern behavior, it is not conducive to the environmental performance of green products, resulting in waste of resources, but also forcing retailers to reduce sales efforts and increase the retail price of products. Finally, the benefits of green closed-loop supply chain are seriously damaged. The profit-sharing contract could improve the relationship between members of the supply chain to achieve sustainable economic and environmental development.

Green closed-loop supply chain network design: a novel bi-objective chance-constraint approach

In this paper, a novel chance-constrained programming model has been proposed for handling uncertainties in green closed loop supply chain network design. In addition to locating the facilities and establishing a flow between them, the model also determines the transportation mode between facilities. The objective functions are applied to minimize the expected value and variance of the total cost CO2 released is also controlled by providing a novel chance-constraint including a stochastic upper bound of emission capacity. To solve the mathematical model using the General Algebraic Modeling System (GAMS) software, four multi-objective decision-making (MODM) methods were applied. The proposed methodology was subjected to various numerical experiments. The solutions provided by different methods were compared in terms of the expected value of cost, variance of cost, and CPU time using Pareto-based analysis and optimality-based analysis. In Pareto-based analysis, a set of preferable solutions were presented using the Pareto front; then optimality-based optimization was chosen as the best method by using a Simple Additive Weighting (SAW) method. Experimental experiments and sensitivity analysis demonstrated that the performance of the goal attainment method was 13% and 24% better that of global criteria and goal programming methods, respectively.

A multi-objective optimization of closed-loop supply chain problem with vehicle routing

Product recovery has become significant business strategies to increase a competitive edge in business and also in the society. Parts from discarded products due to rapid advancement and post-consumer products before & after end-of-life (EOL) are recovered to reduce landfill waste and to have become a part of circular economy. Product recovery is made possible with the help of Closed-loop supply chain (CLSC). This paper concentrates on multi-period, multi-product, and multi-echelon Closed Loop Green Supply Chain (CLGSC) network. A bi-objective (cost and emission) Mixed Integer Linear Programming (MILP) model has been formulated for the network and has been optimized using Goal Programming approach and Genetic Algorithm. Results are discussed for providing some managerial insights of the model.

A green closed-loop supply chain network: a bi-objective mixed integer linear programming model

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

A green closed-loop supply chain network: a bi-objective mixed integer linear programming model

The increasing level of customer awareness and application of environmental laws by governments, on the one hand, and the increasing number of competitors, on the other hand, has obliged industry owners to include green activities in the design of the supply chain network. Green activities include any type of action that reduces environmental degradation. Hence, this study seeks to develop a bi-objective, mixed integer linear programming (MILP) model for designing a green supply chain network. In the proposed model, the minimisation of costs and detrimental environmental effects are discussed. LP metric method is used to solve the bi-objective model and the proposed model is run by using simulated data in small and medium sizes in GAMS software. Finally, the model sensitivity analysis is measured in order to evaluate the validity and performance of the proposed model by using demand-driven scenarios. The results of this model indicate the effectiveness of the proposed model.

A novel robust fuzzy mean-UPM model for green closed-loop supply chain network design under distribution ambiguity

Green closed-loop supply chain (GCLSC) is a supply chain that encompasses forward and reverse flows of components and products in logistic networks with a focus on economic and environmental performance. In the decision-making process of GCLSC, the presence of uncertainty and risk originating from the size and complexity of network is crucial to consider, and the distribution of uncertain parameter may be ambiguous. To characterize the ambiguity caused by distributional perturbation, a novel ambiguity distribution set is proposed, and further a new upside risk: upper partial moment with power q is introduced to quantify the economic risk in the GCLSC. Subsequently, a distributionally robust fuzzy GCLSC network design model which attempts to optimize the worst-case performance of the network is developed with the perspective of a trade-off between upside risk and expectation of economic cost. To format a sustainable GCLSC paradigm, the policy of carbon cap is adopted to control carbon emissions in terms of environmental constraints. Furthermore, the tractable counterpart of the proposed model is obtained by transforming distributionally robust credibility objective and constraints into their equivalent forms under ambiguous distribution of uncertain parameter. Finally, a case study on Coca-Cola Company in Northeast China is investigated to test and verify the proposed model. The advantage of proposed model is demonstrated through comparative study on distribution ambiguity free and without environmental constraint problem. Computational results reveal that the proposed model has superior capability of immunity against the risk of distribution ambiguity.

Green closed-loop supply chain network design with stochastic demand: a new accelerated benders decomposition method

Changing the structure of supply chains to move towards less polluting industries and better performance has attracted many researchers in recent studies. Design of such networks is a process associated with uncertainties and control of the uncertainties during decision-making is of particular importance. In this paper, a two-stage stochastic programming model was presented for the design of a green closed-loop supply chain network. In order to reach the environmental goals, an upper bound of emission capability that helps governments and industries to control greenhouse gas emissions was considered. During the reverse logistics of this supply chain, waste materials are returned to the forward flow by the disassembly centers. To control the uncertainty of strategic decisions, demand and the upper bound of emission capacity with three possible scenarios is considered. To solve the model, a new accelerated Benders decomposition algorithm along with Pareto-Optimal-Cut was used. The efficiency of the proposed algorithm was compared with the regular Benders algorithm. The effect of different numerical values of parameters and probabilities of scenarios on the total cost was also examined.

Retailers’ competition and cooperation in a closed-loop green supply chain under governmental intervention and cap-and-trade policy

This paper investigates retailers’ competition and cooperation in a closed-loop green supply chain consisting of one common manufacturer and two competing retailers under governmental intervention and cap-and-trade policy. Considering a consistent pricing strategy of the manufacturer, this study develops one centralized policy and three manufacturer-led decentralized policies viz. Collusion, Cournot (Nash), and Stackelberg depending on different competitive behaviors of the retailers. Optimal decisions are compared analytically through a special case where the retailers face the same basic market, and numerically where they face both the same basic market and different basic markets. A transfer payment mechanism is developed so that all the channel members achieve Pareto improvement. Numerical results indicate that (1) among the three decentralized scenarios, Nash behavior is profitable to the manufacturer, customers, and the whole supply chain, but Collusion behavior is profitable to the retailers only when the difference of their basic markets is small, (2) when the retailers face the same basic market and play Stackelberg game, it is beneficial for the retailers to be follower rather than leader, and (3) occurrence of both the government subsidy and cap-and-trade policy is profitable to all the channel members.

Research on green closed-loop supply chain with the consideration of double subsidy in e-commerce environment

Issues such as shortage of resources, degradation of environment, enhancement of consumers’ environmental awareness and their preference for green products, continuous improvement of environmental protection laws and regulations of government, together with increasing competition among enterprises, make the construction of green supply chain, such as production, recycling and remanufacturing of environment-friendly products particularly important. However, there are still many constraints for enterprises to effectively implement green supply chain management, such as lack of research and development (R&D) funds and technical capacity. Moreover, government is also eager to know how to formulate effective financial subsidy policies to encourage manufacturers to actively build green supply chain. In the context of above issues, the maximum total profit model of green closed-loop supply chain system is constructed under the consideration of online and offline sales modes and consumers’ green preference in this paper. Considering excessive computational time required when solving this non-linear optimization task with mixed continuous/integer variables via traditional optimization methods, meta-heuristic approaches, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are adopted to try to find and compare calculating example’s approximate optimal solution. The calculating results indicated that enterprises could arrange the ratio of manufacturing and remanufacturing lots properly to achieve system optimal outcome; and government could adopt a suitable subsidy policy to encourage recycling and remanufacturing of used products. Sensitivity analysis of green degree of the recycled products proved that government could adopt the second subsidy strategy for low-green products to further promote the recycling and remanufacturing of enterprises. This research provides theoretical foundation for initiating reasonable government subsidy policy, formulating practical production and operation plan of enterprises as well as maximizing profits in E-commerce environment.

Robust optimization model for sustainable supply chain for production and distribution of polyethylene pipe

PurposeIn this study, the authors consider the key decisions in the design of the green closed-loop supply chain (CSLC) network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, in this paper is the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.Design/methodology/approachIn this study, the author consider the key decisions in the design of the green CLSC network. These decisions include considering the optimal location of suppliers, production facilities, distribution, customers, recycling centers and disposal of non-recyclable goods. In the proposed model, the level of technology used in recycling and production centers is taken into account. Moreover, the environmental impacts of production and distribution of products based on the eco-indicator 99 are considered.FindingsThe results indicate that the results obtained from the colonial competition algorithm have higher quality than the genetic algorithm. This quality of results includes relative percentage deviation and computational time of the algorithm and it is shown that the computational time of the colonial competition algorithm is significantly lower than the computational time of the genetic algorithm. Furthermore, the limit test and sensitivity analysis results show that the proposed model has sufficient accuracy.Originality/valueSolid modeling of the green supply chain of the closed loop using the solid optimized method by Bertsimas and Sim. Development of models that considered environmental impacts to the closed loop supply chain. Considering the impact of the technology type in the manufacture of products and the recycling of waste that will reduce emissions of environmental pollutants. Another innovation of the model is the multi-cycle modeling of the closed loop of supply chain by considering the uncertainty and the fixed and variable cost of transport.

Robust optimization and modified genetic algorithm for a closed loop green supply chain under uncertainty: Case study in melting industry

Today, due to the increasing environmental hazards and governmental regulations, as well as the limitation of sources of production, researchers have paid special attention to the design of closed-loop green supply chain networks. The closed-loop supply chain networks (CLSCN) include the returns processes and the producers aim to capturing additional value considering further integration of all supply chain activities. Therefore, all return processes need to be optimized as well as considering environmental impacts leading to form a closed-loop green supply chain network (CLGSCN). For decision making purposes, operational and tactical decision making levels are integrated to configure a coordinated supply chain network aiming to maximize profit while keeping environmental-friendly policies. The case is more sophisticated in melting industries where the collection and categorization in return process and different environmental challenges should be considered at the same time. Thus, in this paper, a CLGSCN of a melting industry is modeled with respect to environmental hazards to optimiza overall profits. Since real-world demand in melting industry under study is uncertain, the robust optimization has been employed, and while the optimization of the proposed mathematical model is time consuming, an improved version of the genetic algorithm has been implemented as a solution method. This study has been carried out at Melting Imen Tabarestan (MIT) company in Iran. The proposed model along with the solution method are investigated in the case study. The results imply the effectiveness and applicability of the model and provide tactical considerations for the managers and practitioners.

Pricing and used product collection strategies in a two-period closed-loop supply chain under greening level and effort dependent demand

This article presents a two-period closed-loop green supply chain model with a single manufacturer and a single retailer to investigate the impact of green innovation, marketing effort and collection rate of used products on the supply chain decisions. The market demand of the green product is assumed to be dependent on the selling price, greening level and marketing effort. In the first period, the manufacturer produces new product from fresh raw materials while in the second period, besides manufacturing new products, he also collects and remanufactures used products. The centralized model and three decentralized models (depending on the manufacturer’s collection option of used products) are considered. A cost sharing contract is also employed to address the coordination issue. The optimal results are obtained, and the effect of key model-parameters on the optimal decisions are investigated through sensitivity analysis. It is observed that the supply chain gives better response when both the manufacturer and the retailer collect used products simultaneously, and the performance of the supply chain can be improved by instigating either green innovation or marketing effort or both.

A robust mixed flexible-possibilistic programming approach for multi-objective closed-loop green supply chain network design

In recent years, due to governmental legislation, environmental groups’ pressures, customer green expectation, etc., closed-loop green supply chains have gained paramount consideration. Accordingly, this study develops a novel multi-objective mixed integer nonlinear programming model for a closed-loop green supply chain network design problem. The proposed model aims to minimize the total costs, total CO2 emissions, and robustness costs in both forward and reverse directions, simultaneously. To cope with flexible constraints and epistemic uncertainty in the model’s parameters, a robust flexible-possibilistic programming approach is tailored. The model is solved using an efficient interactive solution approach, in which, the presented model is analyzed under various carbon emission mechanisms to assess the influence of these mechanisms on the achieved solution. An illustrative example in the copier industry is also provided to validate the applicability of the presented optimization model. Numerical results indicate the superiority of the carbon cap-and-trade policy in most of the cases.

ارائه مدل جامع چند‌هدفه زنجیره تأمین حلقه‌بسته سبز چندسطحی ـ چند‌محصولی با رویکرد کلاسیک مجموع وزن‌دار: تولید جبهه پارتو (موردمطالعه: شرکت کفش شهپر ممتاز)

Planning and inventory control are considered the main factors affecting multi-level closed loop supply chain processes in the process of returning product from consumers to manufacturers. The integrated network is called a Green Closed-Loop Supply Chain (GCLSC). By introducing a mathematical multi-objective model for linear mixed-integer programming and optimizing it through appropriate technical, engineering and management methods, this research aims to eliminate or control the workplace pathogens with a view to minimizing the employees’ limit of exposure and total cost. Thus, a set of criteria titled “Occupational Exposure Limits” are defined to ensure the safety of the workforce in all production centers. Based on the concept of the Pareto Front, a multi-objective algorithm is proposed which uses the proposed mechanism of variable weight in the weighted-sum method to change the direction in the objective space. Comparison of the performance of the algorithm based on the objective functions: minimizing both the emission of hazardous chemical substances and the costs in the workplace, demonstrates the proper performance of the weighted sum method in solving production planning and inventory control issues. The results show that staff exposure to chemical agents is within the permissible range, so that other costs are kept to a minimum.

Green closed-loop supply chain network design considering cost control and CO2 emission

PurposeEnvironmental issues have become an important concern in modern supply chain management. The structure of closed-loop supply chain (CLSC) networks, which considers both forward and reverse logistics, can greatly improve the utilization of materials and enhance the performance of the supply chain in coping with environmental impacts and cost control.Design/methodology/approachA biobjective mixed-integer programming model is developed to achieve the balance between environmental impact control and operational cost reduction. Various factors regarding the capacity level and the environmental level of facilities are incorporated in this study. The scenario-based method and the Epsilon method are employed to solve the stochastic programming model under uncertain demand.FindingsThe proposed stochastic mixed-integer programming (MIP) model is an effective way of formulating and solving the CLSC network design problem. The reliability and precision of the Epsilon method are verified based on the numerical experiments. Conversion efficiency calculation can achieve the trade-off between cost control and CO2 emissions. Managers should pay more attention to activities about facility operation. These nodes might be the main factors of costs and environmental impacts in the CLSC network. Both costs and CO2 emissions are influenced by return rate especially costs. Managers should be discreet in coping with cost control for CO2 emissions barely affected by return rate. It is advisable to convert the double target into a single target by the idea of “Efficiency of CO2 Emissions Control Reduction.” It can provide managers with a way to double-target conversion.Originality/valueWe proposed a biobjective optimization problem in the CLSC network considering environmental impact control and operational cost reduction. The scenario-based method and the Epsilon method are employed to solve the mixed-integer programming model under uncertain demand.

A robust optimization approach for multi-objective, multi-product, multi-period, closed-loop green supply chain network designs under uncertainty and discount

ABSTRACTOne of the basic requirements of the companies to survive in real-world competitive environments is to make their supply chains as efficient as possible. Due to recent governmental regulations, environmental issues, and the development of the concept of social responsibility, the closed-loop supply chain management has been focused by many researchers. A closed-loop supply chain includes both forward and reverse supply chain networks with the purpose of combining environmental considerations with the traditional supply chain network designs through the collection of used products and activities related to their reuse. In this paper, a bi-objective, multi-period, multi-product, closed-loop supply chain network is designed under environmental considerations, discounts, and uncertainties. The deterministic model of the chain is first solved by three multi-objective decision-making methods. Then, based on real-world uncertainties involved in some of the parameters, a robust optimization model is proposed and solved using decision-making methods. At the end, the best deterministic and robust models are selected based on the displaced ideal solution.