Green Supply Chain Design Research Articles

Comparing resilience strategies for a multistage green supply chain to mitigate disruptions: A two-stage stochastic optimization model

Supply chain disruptions, notably exemplified by the COVID-19 pandemic, have substantial impacts, requiring effective resilience strategies. This study introduces a comprehensive multi-stage, multi-period green supply chain design model, incorporating six resilience strategies to tackle downstream and upstream disruptions. A rigorous two-stage stochastic optimization approach is employed to manage parameter uncertainties and disruptions. The objectives include minimizing disruption costs and CO2 emissions, for a chain that operates under cap-and-trade emission regulation. The findings, derived from a numerical experiment and sensitivity analysis, offer the optimal supply chain structure and effective resilience strategies to mitigate disruptions. The demand structure for essential and non-essential products during disruptions are explored, emphasizing proactive resource allocation. In particular, the impact of facility capacity reductions underscores the importance of capacity management. Sensitivity analyses reflect the trade-offs involved in capacity management strategies, emphasizing the critical need to maintain an optimal level of capacity to prevent service level degradation. Additionally, examination of carbon abatement regulations reveals the intricate balance between environmental responsibility and economic efficiency. In summary, this study addresses supply chain disruptions and offers actionable insights for supply chain managers and policy makers. The research highlights the importance of specific and data-driven resilience strategies to optimize supply chain performance in face of disruptions.

Impact of carbon emissions regulation on producer and consumer transportation decisions in green supply chain design under uncertainty

Impact of carbon emissions regulation on producer and consumer transportation decisions in green supply chain design under uncertainty

A new approach for reliability modeling in green closed-loop supply chain design under post-pandemic conditions: A case study

Climate change, pandemics, and economic crises have created complex challenges for supply chains. Managing such situations requires the development of reliable decision-making frameworks. In this paper, a multi-level, multi-product, and multi-period closed-loop supply chain is studied with environmental considerations. A bi-objective mixed-integer linear programming model is presented for facility location, flow allocation, and transportation mode determination. The objectives of the model are to minimize the total cost and maximize the reliability of suppliers to meet the needs of factories. In the area of reliability engineering, a new approach is defined for modeling the probability of supplier availability considering catastrophic failures caused by pandemics, economic sanctions, and other failure modes. Furthermore, the decision-maker can handle the emission of greenhouse gases by an upper-bound constraint. In order to face the simultaneous uncertainty of demand and the maximum CO2 emission allowed, a scenario-based two-stage stochastic programming approach is proposed. The improved version of the augmented ε-constraint method, known as AUGMECON2, is used to solve the proposed model. The efficiency of the model and the proposed solution approach are investigated through a real-world case study of a battery manufacturing company in Iran.

Factor analysis of environmental effects in circular closed‐loop supply chain network design and modelling under uncertainty in the manufacturing industry

AbstractSustainable development and competitive advantage are impacted by strategic choices that maximize resource value and reduce waste. Numerous instances of thriving OEM remanufacturing can be observed, predominantly in the business‐to‐business domain. The significance of Closed‐Loop Supply Chain (CLSC) in generating value and managing the recovery process is widely acknowledged within the supply chain industry. Manufacturing companies now have to come up with green supply chain and process design strategies in response to recent changes in environmental regulations. This study designates the specific features of circular closed‐loop supply‐chain design considering end‐of‐life products. Uncertainty in various aspects, such as acquisition, processing, and market stages, is impeding the progress of circular economies and also sustainable development in closed‐loop supply chains (CLSCs). This has led to increased complexity in remanufacturing processes and decreased efficiency. To address this issue, the study proposes a comprehensive, integrative approach for establishing a sustainable CLSC network that adapts to fluctuating demand through a questionnaire analysis. Moreover, the study introduces a multi‐objective optimization model for a dual‐channel supply chain network, aiming to enhance the flow. This model considers both economic and environmental objectives to achieve a sustainable and efficient supply chain system. To determine the ideal circular closed‐loop supply chain (CLSC) network design, the research proposes linear programming model with mixed integer, where recovery can take place in one of three ways: through material recovery, component recovery, or product recovery. Numerous findings from a thorough analysis by data backup that inform CLSC managers of ways to improve product returns in terms of quantity and quality. To address the uncertainty problem, the research also developed the fuzzy credibility constraint technique with a Simulated Annealing algorithm. Using cutting‐edge methods, it is explored and compared how sensitivity analysis results, the impact of altering the problem parameters, and the performance of the suggested model respectively.

A multi-objective stochastic programming approach with untrusted suppliers for green supply chain design by uncertain demand, shortage, and transportation costs

In recent decades, people are paying more attention to protecting the environment and biological resources. One of the most important issues of environmental pollution is air pollution, with production and transportation contributing a significant number of emissions. Considering the various warnings about the excessive increase in the amount of carbon dioxide on the planet, it is necessary to investigate the solutions to reduce the emission of carbon dioxide. On the other hand, in the production cycle, providing more comprehensive models and finding more optimal designs will play a significant role in reducing energy consumption and increasing profitability. Models with the most uncertain parameters are more practical and closer to the real world. In this paper, we consider a multi-objective stochastic programming approach for green supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage, and capacity expansion costs are all considered uncertain parameters. At the same time, environmental approaches to reduce air pollution, (specifically reducing carbon dioxide emissions), are presented. Our multi-objective model includes the minimization of the sum of the total cost, the minimization of the variance of the total cost, the minimization of the financial risk or the probability of not meeting a certain budget, and the minimization of the amount of pollution consequent of production and transportation machines. In the following case study, a three-tier supply chain with four suppliers, four production centers, and three product distribution centers with uncertain demand, suppliers, processing, transportation, cost shortages, and capacity development are examined. To solve the model, two meta-heuristic algorithms (multi-objective genetics with faulty sorting and particle swarming) have been developed. The computational results and optimal designs of the supply set were obtained after the implementation of the algorithms, and finally, by performing Sensitivity Analysis and statistical tests, showed that there are two algorithms with good performance and in most cases, the Non-dominated Sorting Genetic Algorithm (NSGA-II) is superior to the Multi-Objective Particle Swarm Optimization (MOPSO).

Green Closed-Loop Supply Chain Networks’ Response to Various Carbon Policies during COVID-19

As concerns about the environment continue to increase and restrictions become tougher, professionals in business and legislators are being compelled to investigate the environmental effects of the activities associated with their supply chains. The control of carbon emissions by governments all over the world has involved the adoption of a variety of strategies to lower such emissions. This research optimizes COVID-19 pandemic logistics management as well as a green closed-loop supply chain design (GCLSCD) by basing it on carbon regulatory rules. This research looks at three of the most common types of normal CO2 restrictions. In the models that have been proposed, both costs and emissions are optimized. When it comes to supply chain (SC) activities, there is a delicate balance to strike between location selection, the many shipment alternatives, and the fees and releases. The models illustrate these tensions between competing priorities. Based on the numerical experiment, we illustrate the impact that a variety of policies have on costs in addition to the efficiency with which they reduce emissions. By analyzing the results of the models, managers can make predictions concerning how regulatory changes may affect overall emissions from SC operations.

A variable neighborhood search based algorithm and game theory models for green supply chain design

With the increase in the volume of greenhouse gases and pollutants, supply chain managers have sought to design and set up networks that pay special attention to environmental factors besides the economic aspects. In this research, a three-level supply chain network is considered including one manufacturer, several retailers and several customers. Greenhouse gas emissions as an environmental issue, the dependence of demand on the selling price of products and cooperative advertising have been examined to design this network. Also, several transportation systems and production methods with different environmental effects and different costs have been considered. The problem has been modeled by both the general and advertising cost classification approaches. Each model has been linearized by McCormick and sequential linear programming methods, and in each approach, Stackelberg, Nash and cooperative games have been used to determine the relationship between members of the supply chain. Finally, an algorithm has been proposed from a combination of variable neighborhood search algorithm and mathematical modeling and after adjusting its parameters by Taguchi statistical method, Stackelberg, Nash and cooperative games have been implemented on it. The results of linearization methods and the meta-heuristic algorithm show that the profit of the manufacturers, retailers and the whole supply chain depends on the type of the game selected. The profit of the whole supply chain is greater in cooperative conditions than in non-cooperative conditions, and in non-cooperative games, the final profit of the manufacturer will be greater in Stackelberg game.

Towards sustainable development: Green supply chain design and planning using monetization methods

AbstractAddressing environmental concerns in supply chain is a challenge, where one of the greatest difficulties is quantifying the environmental impacts. This quantification in abstract units is often done, but it does not always allow decision‐makers to understand how their supply chains' influence the environment. In this regard, environmental quantification in monetary units can be an asset but, on this, not enough information exists about their application in supply chain. This work intends to develop a decision support tool for the design and planning of closed‐loop supply chains accounting for the monetization of environmental impacts, exploring the most used monetization methods. As main contributions, this paper supports the understanding of the methodological basis of the main monetization methods. This is then embedded into an optimization‐based decision supporting tool that defines the design and planning of supply chain allowing to conclude on the most appropriate methods to used when considering supply chain environmental performance. From the results is also demonstrated how the different monetization methods can influence the supply chain design and planning. A set of main recommendations to decision‐makers and researchers is provided that support the definition of supply chain strategy towards greener systems allowing the evolution of the currently practiced knowledge.

Pricing policy in green supply chain design: the impact of consumer environmental awareness and green subsidies

Pricing policy in green supply chain design: the impact of consumer environmental awareness and green subsidies

A robust-heuristic optimization approach to a green supply chain design with consideration of assorted vehicle types and carbon policies under uncertainty

Adoption of carbon regulation mechanisms facilitates an evolution toward green and sustainable supply chains followed by an increased complexity. Through the development and usage of a multi-choice goal programming model solved by an improved algorithm, this article investigates sustainability strategies for carbon regulations mechanisms. We first propose a sustainable logistics model that considers assorted vehicle types and gas emissions involved with product transportation. We then construct a bi-objective model that minimizes total cost as the first objective function and follows environmental considerations in the second one. With our novel robust-heuristic optimization approach, we seek to support the decision-makers in comparison and selection of carbon emission policies in supply chains in complex settings with assorted vehicle types, demand and economic uncertainty. We deploy our model in a case-study to evaluate and analyse two carbon reduction policies, i.e., carbon-tax and cap-and-trade policies. The results demonstrate that our robust-heuristic methodology can efficiently deal with demand and economic uncertainty, especially in large-scale problems. Our findings suggest that governmental incentives for a cap-and-trade policy would be more effective for supply chains in lowering pollution by investing in cleaner technologies and adopting greener practices.

Mapping the environmental aspect of kernel product system in complex supply chains of the West Africa cashew industry.

Global supply chains (GoSCs) have become common for industries. However, relatively little research has emphasized products' complex supply chain environmental assessments. Against this background, this paper maps key environmental issues in the GoSCs of the West Africa cashew industry. The initial-oriented green design and supply chain-wide assessment of the kernel product system was conducted through an eco-design approach based on the matrix method, life cycle design strategy wheel and analytical hierarchical process. The results reveal static/technical eco-design issues that can reduce negative environmental impacts. Additionally, it highlights the strategic opportunities for redesign related to the selection of low-impact materials and gaseous emission. The main issues that arise in mapping the complex supply chains such as the need to re-examine some environmental issues such as solid and liquid residues from production and processing stages as more strategic concerns are discussed. In conclusion, the study points out that it is important not to compromise supply chain-wide assessment of environmental impacts and to understand both technical and strategic values of environmental issues in complex supply chains of products even at the initial stage of interest in green supply chain design. Thus, the eco-design approach of the study has potential to enhance environmental sustainability.

Green supply chain design with emission sensitive demand: second order cone programming formulation and case study

We propose a novel modeling framework for supply chain network design that models a prevailing trend in consumer choice in which demand is impacted by carbon footprint. To date, the literature lacks models that realistically account for and accurately calculate per unit emissions, i.e., carbon footprint. We develop a profit maximizing model that accounts for emissions at the different stages of the supply chain, locates facilities and selects their technology, and decides on the flow between echelons. To calculate the carbon footprint, fixed emissions are averaged over throughput, which results in a nonlinear optimization problem with fractional terms. To solve it, we provide a mixed integer second order cone programming reformulation. We perform extensive testing of the framework on a realistic case study and carry out detailed analysis. The proposed framework succeeds in capturing the trade-off between lost demand due to a high carbon footprint and investing in environmentally-friendly technology. The framework serves as a tool to induce organizations to invest in green technology and to allow regulating authorities to assess the impact of eco-labeling.

Design of a sustainable and reliable hydrogen supply chain network under mixed uncertainties: A case study

In the recent decade, the design of green hydrogen supply chains has been highlighted by researchers. Although, nowadays, responsiveness and social responsibility of networks could also be regarded as important measures that could attract more consumers to use hydrogen. Accordingly, this paper aims to improve the reliability and social responsibility of a hydrogen supply chain along with its economic and environmental aspects. To ensure the network's responsiveness, a new objective function is extended that maximizes the reliability of products' delivery. Also, a novel reliability approach is developed to immune the network against disruptions. As a new sustainability indicator, social factors are considered in the design of the hydrogen supply chain. Finally, a mixed possibilistic flexible programming method is proposed to assure the outputs' reliability. The results illustrate that by 28.4% enhancement in cost objective, the value of environmental, social, and reliability objectives are desirably improved 39.2%, 45.6%, and 24.1%, respectively.

Green food supply chain design considering risk and post-harvest losses: a case study

The global food insecurity, malnourishment and rising world hunger are the major hindrances in accomplishing the zero hunger sustainable development goal by 2030. Due to the continuous increment of wheat production in the past few decades, India received the second rank in the global wheat production after China. However, storage capacity has not been expanded with similar extent. The administrative bodies in India are constructing several capacitated silos in major geographically widespread producing and consuming states to curtail this gap. This paper presents a multi-period single objective mathematical model to support their decision-making process. The model minimizes the silo establishment, transportation, food grain loss, inventory holding, carbon emission, and risk penalty costs. The proposed model is solved using the variant of the particle swarm optimization combined with global, local and near neighbor social structures along with traditional PSO. The solutions obtained through two metaheuristic algorithms are compared with the optimal solutions. The impact of supply, demand and capacity of silos on the model solution is investigated through sensitivity analysis. Finally, some actionable theoretical and managerial implications are discussed after analysing the obtained results.

Proposal for a Comprehensive Environmental Key Performance Index of the Green Supply Chain

The consideration of environmental objectives in the design of green supply chains creates the need to build a set of key performance indicators for monitoring and control. There is a set of generally accepted environmental indicators for monitoring environmental objectives in the supply chain. However, so far these indicators have been disconnected from the operational and economic indicators of the supply chain. It is important to consider these elements in the integral performance of a green supply chain. The present work is a proposal of a general index of environmental performance that includes operational, financial, and environmental aspects that allow monitoring the integral performance of the supply chain by applying Principal Component Analysis of mixed data.

Joint decision on product greenness strategies and pricing in a dual-channel supply chain: A robust possibilistic approach

Nowadays, by increasing the sensitivity of governments to the issue of the environment, green supply chain design has become very significant. This paper presents a mathematical model based on a dual-channel system for designing a green supply chain. The first sales channel is based on the traditional retail purchasing system, while in the second channel the customers purchase the products from the plants directly. In the proposed model, decisions on location, products transfer and pricing are determined. Furthermore, the decision on the kind of manufacturing technology in each plant to be established is considered as well. Any kind of technology leads to a specific level of the greenness of the products in each plant. A product with a higher green level has a lower greenhouse gas emission but higher costs. In order to encourage managers to produce green products, government subsidy policies are in place. Besides, because of the uncertain nature of some parameters, a robust possibilistic optimization approach has been used. As the proposed mixed integer nonlinear model is complicated to solve, an Invasive Weed Optimization Algorithm (IWO) is proposed to obtain efficient solutions. Due to the nonlinear type of the developed model and due to there is no benchmark available in the related literature to validate the results, Cultural Algorithm (CA) and Genetic Algorithm (GA) are utilized. The results indicate that IWO performs better than others.

A green and resilient supply chain design model

Green supply chain factors and practices have gained increasing attention from the customers, businesses, and academicians for improving overall business sustainability. Because customers also value reliable business performance in terms of product and services availability, supply chains must be resilient for their sustainability and business continuity. Supply chain (SC) operations are exposed to various internal disasters initiated by humans and external disasters from natural calamity and terrorist activities. This research designs a SC model that incorporates green approach-based product design, procurement, manufacturing, and distribution management. The model also includes resilience planning for these supply chain steps. The objective of the research is to improve overall sustainability performance of the supply chain by integrating contributions of green factors and resilient criteria in all phases of a supply chain. A numerical example illustrates applicability of the model, overall approach of the study, and managerial insights.

A green and resilient supply chain design model

Green supply chain factors and practices have gained increasing attention from the customers, businesses, and academicians for improving overall business sustainability. Because customers also value reliable business performance in terms of product and services availability, supply chains must be resilient for their sustainability and business continuity. Supply chain (SC) operations are exposed to various internal disasters initiated by humans and external disasters from natural calamity and terrorist activities. This research designs a SC model that incorporates green approach-based product design, procurement, manufacturing, and distribution management. The model also includes resilience planning for these supply chain steps. The objective of the research is to improve overall sustainability performance of the supply chain by integrating contributions of green factors and resilient criteria in all phases of a supply chain. A numerical example illustrates applicability of the model, overall approach of the study, and managerial insights.

The Design of Green Supply Chains under Carbon Policies: A Literature Review of Quantitative Models

Carbon footprinting of products and services is getting increasing attention due to the growing emphasis on carbon related policies in many countries. As a result, many enterprises are focusing on the design of green supply chains (GSCs) with research on supply chains (SCs) focused not only on cost efficiency, but also on its environmental consequences. The review presented in this paper focuses on the implications of carbon policies on SCs. The concept of content analysis is used to retrieve and analyze the information regarding drivers (carbon policies), actors (for example, manufacturers and retailers), methodologies (mathematical modeling techniques), decision-making contexts (such as, facility location and order quantity), and emission reduction opportunities. The review shows a lack of emissions analysis of SCs that face carbon policies in different countries. The research also focuses on the design of carbon policies for emissions reduction in different operating situations. Some possible research directions are also discussed at the end of this review.

The Green Supply Chain Design and Marketing Strategy for Perishable Food Based on Temperature Control

With the intensification of global warming and the levy of energy tax, more industries are paying attention to energy saving and reduction of carbon footprint. For the food industry, energy cost in the supply chain of perishable food is quite high because of cold-chain transport and storage. Therefore, the efficacies of cold chain management and inventory control are the key factors that increase the efficiency of food supply chain and make it more ecological. This research analyzes the degradation process of perishable food and determines the optimal temperature of the cold chain as well as the optimal price to maximize the channel profit. We prove that there is an optimal price with a certain temperature and develop an efficient search algorithm to find the optimal temperature. We also perform sensitivity analyses to test which parameters affect the channel profit significantly. Numerical experiments are conducted to illustrate the proposed models.