Green Closed-loop Supply Chain Network Research Articles

Prevention of post-pandemic crises: A green sustainable and reliable healthcare supply chain network design for emergency medical products

The COVID-19 pandemic happened exactly when no one expected it and many people died due to the lack of medical equipment. Although pulse oximeters and thermometers were only one of the virus detection equipment, the lack of that equipment could lead to people not knowing about the disease and then the death of those people. Given that these medical devices are very vital in the era of a pandemic, and much less in the later, it is required to provide the best conditions for their use in a closed-loop healthcare supply chain network in post-pandemic. In this paper, a scenario-based two-stage stochastic programming three-objective model for designing a green closed-loop supply chain network is presented. Cost minimization, reliability maximization, and critical response maximization are the objectives of the proposed model. The third objective function is considered for the critical response based on the choice of transportation method. The greenhouse gas emissions for all supply chain elements are considered uncertain and controlled in several possible scenarios. By using an accelerated Benders decomposition algorithm, the mathematical model was solved in different dimensions and the result was analyzed and evaluated in different scenarios. The results demonstrate that the acceleration of Benders bonds reduces the convergence speed of the algorithm by 41%. Our study provides managerial insights into sustainability and reliability, enhancing healthcare supply chain resilience during and after pandemics.

Risk-averse joint facility location-inventory optimisation for green closed-loop supply chain network design under demand uncertainty

Risk-averse joint facility location-inventory optimisation for green closed-loop supply chain network design under demand uncertainty

Risk-Averse Joint Facility Location-Inventory Optimization for Green Closed-Loop Supply Chain Network Design under Demand Uncertainty

Risk-Averse Joint Facility Location-Inventory Optimization for Green Closed-Loop Supply Chain Network Design under Demand Uncertainty

Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry.

This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.

A Multiobjective Model for Optimizing Green Closed-Loop Supply Chain Network under Uncertain Environment by NSGA-II Metaheuristic Algorithm

Nowadays, due to growing development and competitiveness in global markets of products, companies are forced to make significant efforts for supply procurement, production, and goods distribution in order to survive in the market and be able to respond to their customers’ needs as quickly and cost-efficiently as possible. In this regard, supply chain management is considered a crucial indicator. This study presents a multiobjective, multifacility, closed-loop supply chain under uncertain environments considering green supply chain aspects. The model is designed with multiple products, periods, plants, customer markets, collection centers, recycle centers, distribution centers, return facilities, product recovery facilities, and suppliers. After modeling the study, the model is solved by the Nondominated Sorting Genetic Algorithms (NSGA-II) in order to rank the optimum solutions. The efficiency of the research model is indicated by the results and depicted graphs in the present study. Results show that the exact value of the triple objective functions is calculated. Also, the problem is solved in small, medium, and large dimensions. Then, the accuracy of the proposed model compared to the metaheuristic method is shown. Finally, by performing sensitivity analysis, we showed that target functions are less sensitive to reducing the capacity of centers.

Robust Optimization for a Bi-Objective Green Closed-Loop Supply Chain with Heterogeneous Transportation System and Presorting Consideration

In this study, we propose a robust bi-objective optimization model of the green closed-loop supply chain network considering presorting, a heterogeneous transportation system, and carbon emissions. The proposed model is an uncertain bi-objective mixed-integer linear optimization model that maximizes profit and minimizes carbon emissions by considering uncertain costs, selling price, and carbon emissions. The robust optimization approach is implemented using the combined interval and polyhedral, “Interval+ Polyhedral,” uncertainty set to develop the robust counterpart of the proposed model. Robust Pareto optimal solutions are obtained using a lexicographic weighted Tchebycheff optimization approach of the bi-objective model. Intensive computational experiments are conducted and a robust Pareto optimal front is obtained with a probability guarantee that the constraints containing uncertain parameters are not violated (constraint satisfaction).

Designing an optimization model for green closed-loop supply chain network of heavy tire by considering economic pricing under uncertainty.

The conditions of global competition and environmental sensitivities have made organizations and factories to collect returned products, in such a way that these organizations have tried to rehabilitate, recycle, or destroy these products in order to protect the environment. This paper propose a mathematical model for the green closed-loop supply chain network of heavy tire by considering the economic pricing of its products under conditions of uncertainty, which economically determines the price and leads to more profitability. In addition, the relevant model is a two-objective fuzzy model, the first objective of which is to minimize costs and maximize profits, and the second objective is to minimize environmental issues. The proposed model can also determine the optimal location of each center based on potential locations, the optimal amount of production, distribution, collection, recycling, as well as the reproduction of products. The ε-constraint method is used to solve the model with two objective functions; this method ensures strong Pareto optimal answers and prevents weak Pareto answers. Independent two-sample t-test is used to verify the results of certain and uncertain models in the studied model. In order to evaluate the effectiveness and profitability of the proposed method, a case study in the field of heavy tires is finally used, through which very useful results are obtained.

A Robust Multi-objective Fuzzy Model for a Green Closed-loop Supply Chain Network under Uncertain Demand and Reliability (A Case Study in Engine Oil Industry)

Given the importance of supply chain and environmental issues, this paper presents a new mathematical model for a green closed-loop supply chain (GCLSC) network with the objectives of maximizing profits, maximizing the number of jobs created, and maximizing reliability. Due to the uncertainty on some parameters such as demand and transportation costs, the new method of robust fuzzy programming model was utilized. Multi-objective Grey Wolf Optimizer (MOGWO) and Non-dominated Sorting Genetic Algorithm II (NSGA II) were used to tackle the problems for larger sizes. A number of instances of the problem in larger sizes were solved. The results from comparing the algorithms considering some criteria including means of objective functions, spacing index, distance index from ideal point, maximum amplitude index, Pareto response number index and computational time showed the fast convergence and high efficiency of MOGWO algorithm for this problem. Finally, the implementation of the model for a real case study in Iranian engine oil industry, showed the efficiency of the obtained solutions for this network.

Carbon-efficient closed-loop supply chain network: an integrated modeling approach under uncertainty.

Inspired by a circular economy paradigm, an evolving momentum of policies and legislations aims to close the loop of product lifecycles through improved level of recycling, remanufacturing, and reuse, with the objective of adding value to the economy while not endangering the environment. However, the trade-off between the environmental and economic sustainability of designing business processes is inevitable. To address this trade-off in the supply chain context, competing objectives regarding both cost minimization and reduction in carbon emission should be simultaneously considered and integrated into a comprehensive model. This complexity is however elevated when uncertainty of demand is taken into consideration. In this study, the design of a closed-loop supply chain is investigated where competing objectives of cost and sustainability of supply chain operations are evaluated under demand uncertainty. Augmented Weighted Tchebycheff (AWT) and ε-constraint methods are employed to address the multi-objectivity of the problem while a robust optimization approach is applied to deal with the demand uncertainty. The results confirm that the proposed approach provides efficient solutions for designing a green closed-loop supply chain network.

Green Closed-Loop Supply Chain Network under the COVID-19 Pandemic

The closed-loop supply chain considers conceptually the possibility of reverse logistics with the use of recycling, remanufacturing and disposal centers. This study contributes for the first time a green closed-loop supply chain framework for the ventilators, which are highly important in the case of the COVID-19 pandemic. The proposed model simulates a case study of Iranian medical ventilator production. The proposed model includes environmental sustainability to limit the carbon emissions as a constraint. A novel stochastic optimization model with strategic and tactical decision making is presented for this closed-loop supply chain network design problem. To make the proposed ventilator logistics network design more realistic, most of the parameters are considered to be uncertain, along with the normal probability distribution. Finally, to show the managerial dimensions under the COVID-19 pandemic for our proposed model, some sensitivity analyses are performed. Results confirm the high impact of carbon emissions and demand variations on the optimal solution in the case of COVID-19.

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.

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.

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 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.

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.

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.

Reliability optimization in a four-echelon green closed-loop supply chain network considering stochastic demand and carbon price

Reliability optimization in a four-echelon green closed-loop supply chain network considering stochastic demand and carbon price