زنجیره تامین سبز

The purpose of the current research is to design a green supply chain pricing model with a multi-criteria decision-making approach and game theory (case study: home appliance industry). In this research, after library study and identification of key indicators in predicting green supply chain pricing, using the fuzzy Delphi method, the screening of indicators was done in three stages, and out of 20 indicators, based on the opinions of 13 experts, 13 indicators were selected and based on the priority selection index approach, company D entered the game theory as the leader company because it had the highest priority. Finally, based on the game theory, the scenario between 4 members of the supply chain was evaluated and analyzed, and the best and worst scenarios were also introduced. Also, in order to test the structural validity of the proposed model, information related to the green supply chain of 9 selected home appliance companies was implemented using MATLAB software.

Governments today are increasingly focused on managing environmental and economic challenges to safeguard the environment. Consequently, companies are striving to remain competitive in society. This paper introduces a revenue-sharing agreement between the retailer and the manufacturer. The contract is designed to alleviate the research and development costs incurred by the manufacturer in greening their products. Additionally, the paper incorporates consumer surplus as a consideration for the social dimension, which is a key pillar of sustainability.

Government interventions in this paper are modeled as subsidies that influence the final price of the product. Notably, this study, for the first time, integrates all three dimensions of sustainability—social, economic, and environmental—within a game-theoretic framework that includes government intervention and a revenue-sharing agreement between the retailer and the manufacturer.

The modeling results demonstrated that incorporating consumer surplus enhances the profitability of both the government and the manufacturer. Although consumer surplus reduces wholesale prices and revenue-sharing proportions, it leads to increased product sales. This boosts the benefits for consumers and members of the supply chain, contributing to improved greenness and higher levels of product sustainability. Consumers show a preference for purchasing products with greater environmental benefits; however, higher levels of greenness require increased production costs. Manufacturers can boost their sales through research and development efforts and by raising public awareness about green products. As a result, manufacturers, retailers, and the government adjust prices and subsidies to maximize their respective profits, altering the overall profitability of the supply chain. The findings highlight that social welfare is a crucial consideration in supply chains, as it significantly enhances the profitability of chain members and promotes higher levels of green production.

Originality/Value: 

This paper is the first to examine all dimensions of sustainability—social, economic, and environmental—within a theoretical framework that incorporates government intervention and the impact of consumer surplus on pricing policies and the greening of products. The scientific contribution of this research lies in providing new insights to managers and policymakers to enhance strategic decision-making aimed at improving sustainability and social welfare within green supply chains.

Today, sustainable development has been proposed as an alternative to industrial development, and the environmental consequences and lack of resources on the planet have become the main concern of societies. Industry managers, especially in advanced countries, are looking for ways to increase the performance of their organization while protecting the environment. In this regard, paying attention to the green supply chain has received a lot of attention. One of the solutions to this problem, which has recently attracted the attention of industries and researchers, is choosing a green supplier. That is, purchasing units in companies should pay attention to green criteria in their purchasing process from suppliers throughout the supply chain. The concept of green supplier is to provide materials that consume less resources and energy and are non-toxic and do not destroy the environment. The aim of this paper is to develop a bi-objective model based on traditional and green supplier selection criteria and the simultaneous integration of the fuzzy AHP method, multi-objective optimization along with the use of the incremental discount concept. In this research, an integrated model that combines price-related decisions and traditional criteria with green criteria for selecting a green supplier has been presented. Finally, the performance of the model has been investigated and analyzed using a real example in the Lingo software and by the LP metric method. After solving the mentioned bi-objective model, the value of the first objective function is equal to 3.547628 and the value of the second objective function is equal to 1060513.

Defense industries are considered as one of the most important pillars of a nation's national power and one of the most strategic and important sectors due to their role in creating a deterrent capability against foreign threats, and therefore they are effective in creating and promoting national security from which the whole society benefits. On the other hand, the performance of the defense sector is tied to the supply chain performance and project risk management to a large extent. Defense industries as set of project-based organization with a high-tech, always need to use new tools and methods in these projects and projects success in this sector and maintain the continuity of network operations, is highly dependent on having strong and stable supply chains and the ability to project risk management. Therefore, identifying and evaluating risks and hazards can provide better and more accurate decision-making conditions for project management and achieve the goals and success of defense projects. So, research about it is one of the important and basic issues in this field. In this study, in order to evaluate the improvement of project management based on risk management and green supply chain in products related to defense industries, data on project risk management variables, green supply chain management, project management performance and project success were collected using a questionnaire. Based on the results, according to the model hypotheses and modeling of structural equations, the partial least squares method was implemented in SmartPLS software. The analysis of the results indicates a suitable model and good effect of variables. According to the results obtained in the model fit, all variables in all domains gained a good amount of validity and reliability in the model. Also, in the overall effects of the model, it was shown that all the relationships and effects of the variables of project risk management and green supply chain management on project management performance and project success are significant. Therefore, it can be concluded that project risk management and green supply chain management are two important and fundamental factors in project management performance and project success in our country's defense industry.

As environmental policies are highlighted in the green supply chain, it plays an essential role in improving the supply chain capabilities. On the other hand, considering the development of technology and the Internet of Things intelligence and their potentials in goal achievement, it is important to investigate and study their applications in this field. This study aims to identify the most important criteria in selecting a green supplier and investigate the impact of the Internet of Things (IoT) on this selection. To achieve these objectives, the fuzzy cognitive map (FCM) method is used to identify the effective weights of each criterion and introduce the most effective criterion by examining the centrality. The fuzzy hierarchical analysis (FAHP) is used to investigate the role of IoT in the green supply chain. The results indicate that air pollution control is the most important criterion for evaluating the suppliers and also has the greatest impact on IoT.

One of the key steps in greening supply chains is to investigate the environmental requirements and factors in supplier selection. Today supply chain networks are known as the main pillar of economic activities. To this end, in this article a multi-period, multi-product, multi-objective closed-loop supply chain problem has been investigated. Moreover, the return of defective products to the production cycle and the general and progressive simultaneous discount policy have been considered through an integer linear mathematical model. The presented model generates Pareto solutions using two proposed meta-heuristic algorithms, multi objective particle swarm optimization and non-dominated sorting genetic algorithm and combination of the two. To compare the performance of the solution methods, five criteria are defined and the numerical results show the efficiency and quality of the proposed hybrid algorithm.

One of the key steps in greening supply chains is to investigate the environmental requirements and factors in supplier selection. Today supply chain networks are known as the main pillar of economic activities. To this end, in this article a multi-period, multi-product, multi-objective closed-loop supply chain problem has been investigated. Moreover, the return of defective products to the production cycle and the general and progressive simultaneous discount policy have been considered through an integer linear mathematical model. The presented model generates Pareto solutions using two proposed meta-heuristic algorithms, multi objective particle swarm optimization and non-dominated sorting genetic algorithm and combination of the two. To compare the performance of the solution methods, five criteria are defined and the numerical results show the efficiency and quality of the proposed hybrid algorithm.

This study examines the pricing and product greenness level determining policies in two competing supply chains. Each supply chain consists of one manufacturer and one retailer, in which the manufacturer produces a green product, and the retailer delivers it to the end consumer. The manufactured products in supply chains are substitutable. This problem is considered in two scenarios: either the manufacturer or the retailer leads the market. In both cases, the government, as a powerful party, can affect the market by establishing a subsidy/punishment system. This system sets a threshold for the product greenness level and punishes or supports the manufacturer accordingly. In this research, we present the manufacturer and retailer profit functions, taking advantage of wholesale and retail prices, the amount of subsidy and punishment, and demand functions. Next, we use a game-theoretic approach to provide the equilibrium wholesale and retail prices and the equilibrium greenness levels. Numerical results show that the retailer-Stackelberg structure provides better conditions for the supply chain members and the environment, and it is even more highlighted with the competition intensity between the members.

Incentive by the recent global trend in greenhouse gases (GHG) emission reduction strategies and considering the evolution of business models from traditional to multi-channel, in this research, we analyze a dual-channel green supply chain in which a manufacturer delivers a product through one direct channel and one retail channel whose demand are uncertain and prices in channels can effect on customer demand. Modeling this chain is carried out by accounting for the optimal price of distribution channels, total profit optimization, and reduction of transportation cost, we well as minimizing CO2 emission that the CO2 emission is investigated through transportation mode choice. The proposed model has developed first a mixed-integer nonlinear programming and a theoretical technique is applied to linearizing the problem. To solve the proposed multi-objective Possibilistic programming model, the Jimenez and TH approach has been used. The model is verified by numerical experiments and then several sensitivity analyses have performed. Computational results proved the efficiency of the model in finding a trade-off between economic and environmental objectives so that if transportation costs are equal to both markets, in order to balance the goals, the model uses vehicles that have a less environmental impact. In addition, the proposed solution method enables the decision maker to determine the importance of each objective function. The decision-maker is also able to influence the optimal price of distribution channels by changing the parameters in the demand-price equation and maximize the total profit of the supply chain.

One of the main challenges of the governments of each country is the increase in pollution caused by the production of products by manufacturers. Increased pollution leads to a significant growth in the number of patients with heart failure, respiratory failure and other diseases. One of the ways to deal with this situation is to produce green products with the least degree of pollution caused by the production. The green supply chain is one of the necessary solutions to improve the current situation. Pollution is not only due to the production of the product by the manufacturers, but also the transportation of products by non-standard means of transport is a factor in the production of pollution. In this study, a supply chain with a manufacturer and a third-party logistics company (3PL) that the government monitors the supply chain is considered. The government monitors on the supply chain by setting tariffs for the producer. The results show that the presence of a 3PL, although it increases the selling price of products, but reduces pollution and also increases the amount of demand.

In recent years, the increasing pollution of the environment and its destructive consequences, as well as the growing concern about it, has led to new concepts and approaches to reduce these effects and preventive environmental measures. One of the most important of these approaches is the green supply chain. Green supply chain integrates supply chain management with environmental requirements in all stages of product design, selection and supply of raw materials, production and manufacturing, distribution and transfer processes, delivery to customer and finally after consumption, recycling management and Re-consumption is designed to maximize energy efficiency and resources while improving the overall performance of the supply chain. In this paper presents a multi-objective model for a green supply chain network. To solve it, the LP-metric method is used, which is coded in Gams software. The results show that the study of two important factors of cost and carbon dioxide emissions, as one of the most important and well-known pollutants, has led to the application of this model in the supply network and. The results also show that it is more important to pay attention to greenhouse gas emissions at higher demand.

One of the key steps in greening supply chains is to investigate the environmental requirements and factors in supplier selection. Today supply chain networks are known as the main pillar of economic activities. To this end, in this article a multi-period, multi-product, multi-objective closed-loop supply chain problem has been investigated. Moreover, the return of defective products to the production cycle and the general and progressive simultaneous discount policy have been considered through an integer linear mathematical model. The presented model generates Pareto solutions using two proposed meta-heuristic algorithms, multi objective particle swarm optimization and non-dominated sorting genetic algorithm and combination of the two. To compare the performance of the solution methods, five criteria are defined and the numerical results show the efficiency and quality of the proposed hybrid algorithm.

Reducing fuel consumption leads to reduced greenhouse gas emissions and customer service costs, resulting in customer satisfaction and reduced environmental degradation.. For this purpose, this paper focuses on the optimization and planning of the movement of inbound and outbound trucks and the green supply chain, with multi cross-docking and two different types of objective functions of minimizing the sequence of truck transportation and carbon dioxide emissions into the supply chain. Since the paper model is a linear programming integer of zero and since these models belong to the NP-hard class, their solving time severely increases with increasing the problem dimensions. In this paper, to solve the model meta-heuristic algorithms have been used. The algorithms used in solving thae model areNon-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Ant Colony (MOACO) Algorithm. Finally, the model has been solved using two algorithms and computational experiments reported carefully to illustrate and compare designing and computational.

Risk management is a significant issue in supply chain management. Improving the ability to control and manage the risk, enables the companies to be more successful in competing with other companies and decrease the expected long-term loss. In this manuscript, a mixed integer linear programming model for designing the green supply chain is presented. This model aims to minimize the cost, greenhouse gas emissions, and risk. Risk of supplying the raw materials and transportation in all levels of supply chain are under uncertainty. Furthermore, cost of raw materials is suggested by suppliers to producers with an incremental discount. The initial modelling is turned into a deterministic one using Bertsimas and Sim budget of uncertainty approach and consequently solved by GAMS software to manage risk. Furthermore, the uncertain parameter is analyzed and using various amounts the obtained result has been assessed and evaluated. The results show that the risk function is the most important factor in objective function, because parameters of risk function are subject to uncertainty.

In the articles that have been investigated by the vendor managed inventory less attention to environmental pollution or emission of green logistics is considered. In this paper, the issue of green backorder vendor managed inventory in two-echelon supply has been considered. But innovation in green conditions of this article or reducing the amount of pollution is as a function intended purpose. In view of customer facing staff shortages in the supply chain, two-echelon warehouse and limits the number of orders is considered. The first objective is to maximize profit of vendor, which includes sales minus production and distribution costs and facing to shortage cost. The second objective function is minimizing the amount of pollution caused by the movement of goods. According to the model as a model of bi-objective, non-convex and non-linear algorithms is solved by GAMS and Epsilon-Constraint on a small scale can be used to obtain the Pareto front and compare with traditional inventory management in shortage state and for large scale non-dominated sorting genetic algorithm (NSGA-II) was used to solve the model. To adjust the algorithm parameters NSGA-II Taguchi method has been exploited in this article. The results of this research is that allowed the shortage situation, the lack of cost reduction and environmental pollution reduction, the greater the amount of shipping and orders as well as the lack of a less optimal to reduce the cost of inventory shortages.

As clients gain more awareness of environmental issues, governments codify stricter environmental regulations, while industries have to control their environmental effects on the supply chain. Nowadays, considering major investments in establishing cement manufacturing companies in Iran, as well as related job opportunities and other economic dimensions, the cement industry in the country has been following a rising trend. Despite these economic advantages, such companies inevitably contribute to environmental damages, which should be prevented considering environmental regulations. The present study is concerned with analyzing the obstacles to developing green chain supply of small and medium size enterprises in general, while it is specifically concentrated on Fars Cement Industry, Fars, Iran. To do this, primarily the obstacles to implementing GSCM are determined. Then, through the use of the Interpretive Structural Modeling (ISM) the factors are prioritized and categorized. Results of this method showed that lack of technology and new materials and processes counted as the first output of the model, while it showed a suitable force for development. At the next step, Analytic Network Process (ANP) was used to determine the obstacles to establishing GSCM. Finally, the degree of significance of each of the obstacles was determined. Following the whole analysis, results of the study indicated that the obstacle promoting suppliers’ environmental awareness followed by lack of participation of high-ranking managers in adopting green supply chain showed a higher weight. To analyze the output data, Excel, MATLAB and SUPERDECISION were used. To enhance the applicability of the results observed in the study, the weight of the factors were analyzed by ANP. Furthermore, to interpret the degree of effectiveness of each factor in establishing GSCM, ISM was also used.

Today, one of the pervasive issues is damage of ozone layer and environmental pollution due to production of hazardous wastes resulting from human activities in different industries. Hospitals, due to production of infectious and hazardous wastes have been identified as the second polluting industry. This study aims to develop standards for green management of hospitals, by using concepts of green supply chain management, to promote green activities in Bushehr hospitals. After reviewing the literature and researches related to green hospital, related factors were refined and the data collection was conducted through questionnaire. Finally, to analyze the data and responding to research questions Amos software was used. The study population was all personnel of Bushehr hospitals, who involved in the care process. After analyzing the data, the weights of factors affecting the standards of green hospital were determined by SWARA method. The weights of three main selective variables, including green materials, environment and green technology were calculated. Green management and social responsibility with (0.35186) and green materials with (0.31928), has the highest and the least effect on green hospitals respectively. Among three main variables, green management and social responsibility were identified as the most effective index of green hospital standards.

The most studies carried out in the logistics have had the same attitude towards inventory control components such as the optimal order quantity, order frequency and the amount of safety stock of inventory storage centers. For instance, they consider the number of inventory orders for all inventory storage centers as the same during the whole period of planning while according to customers of each distribution center, it may vary. In addition, we may determine the safety stock from different scenarios in each center. The present study offers a new model for location-routing-inventory under uncertainty with regard to environmental considerations. This model simultaneously determines the number and location of distribution centers, allocation of retailers to distribution centers and active routes and the order of visiting the retailers in each route, optimal order quantities, and optimal number of ordering times for each distribution center as well as the level of safety stock for each distribution center. Another aim of the study is minimizing the annual expected cost and the amount of carbon emissions from transport fleet during the delivery process in the entire network.
Since the proposed model is NP-hard problem, we recommend meta-heuristic approaches to obtain optimal solutions. Therefore, Non-dominated sorting genetic algorithm II (NSGA- II), multi-objective particle swarm optimization (MOPSO) and strength Pareto evolutionary algorithm II (SPEA-II) have been developed to solve the problem. Finally, we compare the performance of the proposed algorithms using standard measures. The results show the MOPSO is more efficient than both NSGA-II and SPEA-II.