Green Supply Chain Network Design Research Articles

A multi-objective optimization approach for green supply chain network design for the sea cucumber (Apostichopus japonicus) industry

In China, aquatic supply chain network design does not include the green concept or the coordination of environmental and economic performance. Sea cucumber (Apostichopus japonicus) is an aquatic product of high economic value; however, studies on sea cucumber supply chain network optimization are lacking. This study is the first to design the sea cucumber supply chain and construct an optimization model. Considering the characteristics of the sea cucumber industry, LCA for Experts software and the CML-IA-Aug. 2016-world method were used to assess each aquaculture model's global warming potential (GWP), as the environmental performance indicator. In addition, multi-objective genetic algorithm (MOGA) coupled with Modified Technique for Order of Preference by Similarity to Ideal Solution (M-TOPSIS) integrates yield production, economic benefits, and environmental performance. The results demonstrated that cage seed rearing (CSR) combined bottom sowing aquaculture (BSA) represents the best production strategy upstream of the sea cucumber supply chain. In the downstream, the best proportion of sales channels in supermarkets, boutique stores and online shops accounted for 14.79 %, 58.02 % and 27.19 % of the production, respectively. The proposed optimization scenario 4 (S4) can increase product profit by 27.88 % and reduce GWP by 56.89 %. The following improvement measures are proposed: using sea cucumber aquaculture industry standards (cleaner production and green supplier selection) to regulate the behavior of enterprises, adopting an ecological and green production strategy, eliminating high-energy consumption and high emission production practices, and promoting widespread adoption of green consumption concepts. Finally, these measures may improve the sea cucumber supply chain, achieve coordinated environmental and economic performance development in the sea cucumber industry, and provide guidance for green optimization of other aquatic product supply chains in China.

A dual-channel multi-objective green supply chain network design considering pricing and transportation mode choice under fuzzy uncertainty

A dual-channel multi-objective green supply chain network design considering pricing and transportation mode choice under fuzzy uncertainty

A multi-objective optimisation for green supply chain network design problem considering economic and environmental sustainability

A multi-objective optimisation for green supply chain network design problem considering economic and environmental sustainability

Green and reliable medical device supply chain network design under deep dynamic uncertainty: A novel approach in the context of COVID-19 outbreak

Conditions governing industrial activities during and after global shocks with societal and economic transformations such as the COVID-19 pandemic have led to the loss of effectiveness of conventional approaches to dealing with uncertainties. The occurrence of sharp fluctuations in the essential parameters has left decision-makers in an unpredictable situation. Therefore, proactive efforts should be made to develop current approaches for adapting to new conditions. This paper establishes a strategic, tactical, and operational decision-making framework under the COVID-19 outbreak by developing a new uncertainty type called deep dynamic uncertainty. In the first step, a Mixed-Integer Linear Programming (MILP) model is proposed for the green and reliable closed-loop supply chain network design. The proposed model allows the decision-maker (DM) to manage and control co2emissions and e-waste generation. In the second step, a new three-step algorithm called Augmented Adjustable Column-Wise Robust Optimization (AACWRO) is first proposed. Then, by combining the proposed column-wise uncertainty with multi-stage stochastic programming (MSSP) approach, deep dynamic uncertainty is theorized for modeling the demand uncertainty under pandemic conditions. The model's performance under deep dynamic uncertainty has been carefully investigated based on the real ventilator and infusion pump supply chain network in Iran. The model under deep dynamic uncertainty, while maintaining tractability and adjustability, provides flexibility in entering data into the problem and significantly increases the coverage of modeling uncertainties. The results clearly demonstrate the efficiency of the proposed approach. The model under deep dynamic uncertainty at all levels of conservatism has on average 42.96% lower cost and 32% higher stability than the MSSP model.

An Integrated Optimization Model of Green Supply Chain Network Design with Inventory Management

Supply chain network design and inventory management are both significant for improving the core competitiveness of enterprises. This study investigates the joint optimization problem of facility locations and inventory for assembly manufacturing enterprises’ multi-echelon supply chain networks, considering the locations of facilities, the selection of suppliers, transport mode choices, and inventory decisions simultaneously. A corresponding integrated optimization model is proposed, which aims to minimize the total cost, consisting of the fixed open cost of facilities, the inventory cost of the open plants and distribution centers, and the transportation cost of vehicles in the entire supply chain network as well as the cost of CO2 emissions. Based on the characteristics of the proposed optimization model, a hybrid genetic algorithm embedded with a local search is developed to solve the proposed model. Numerical examples and a case study are provided to illustrate the effectiveness of the proposed model and the corresponding algorithm. The findings show that the model is reasonable and applicable, and hybrid genetic algorithm (HGA) is more efficient than the standard genetic algorithm (SGA). In addition, plants’ maximum lead-time has a significant impact on the total cost of the supply chain.

Priority-based multi-objective algorithms for green supply chain network design with disruption consideration

Priority-based multi-objective algorithms for green supply chain network design with disruption consideration

Building a Multi-Objective Flexible Optimal Decision Model for Green Supply Chains

Due to climate change, the importance of environmental protection, and the operation of the global supply chain influenced by the pandemic, building a flexible green supply chain model can help enterprises keep operational sustainability and strengthen competitive advantages. However, it can be found from relevant literature that research on green supply chain flexibility is still insufficient. This study aims to fill the research gap, and attempts to develop a multi-objective mixed integer programming model for a flexible green supply chain network design to maximize the profit, the amicable production level. To our knowledge, this proposed model is the first effort to take economic factors, environmental factors, supply flexibility, manufacturing flexibility, distribution flexibility and reverse logistics flexibility into account simultaneously, and can be a reference for supporting effectively management of the green supply chain network design. The research result and findings can be a reference for related academic researches and also can be used to guide the development of a green supply chain model for making better decision.

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

Designing a multi-echelon closed-loop supply chain with disruption in the distribution centers under uncertainty

<p style='text-indent:20px;'>According to the need for further cost reduction and improving the process of the organization in the direction of customer demand, the concept of the supply chain has become increasingly significant and the organizations seek to expand this concept within their organizational framework. In this regard, efficient planning of distribution of products in the supply chain by considering disruption has received more attention recently. In this study a multi-objective mixed-integer linear programming model is developed for a green multi-echelon closed-loop supply chain network design under uncertainty. Moreover, a partial disruption is considered for distribution centers where has not been studied enough in previous works. The fuzzy credibility constraint approach is applied to cover uncertainty. In the following, the ε-constraint method is presented to solve and validate the model in small-sized instances. Moreover, a Non-dominated Sorting Genetic Algorithm is developed for solving the large-sized problems. Results show that uncertainty has a crucial impact on objective functions where the increase of objective functions by increasing the level of uncertainty, which was observed in all categories. Furthermore, the proposed NSGA-Ⅱ is the best tool to deal with large-size problems where the EC method lacks the necessary efficiency.</p>

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 dynamic multi-objective green supply chain network design for perishable products in uncertain environments, the coffee industry case study

ABSTRACT One of the most critical issues for any production organization to compete in the current competitive business environment is supply chain network design (SCND). Besides, by increasing greenhouse gas emissions, various organizations have imposed many restrictions to prevent the overproduction of these gases in their production planning. This study develops a mathematical model for the location-allocation-inventory problem based on a real case study to design a three-echelon coffee supply chain network. The primary purpose of the problem is to minimize supply chain costs. Minimizing the CO2 emission was also considered to address the increasing eco-friendly challenges. The problem includes various strategic and tactical decisions, such as the number and capacity of manufacturing centers (MCs) and distribution centers (DCs) to be established, how to allocate factories to distribution centers and DCs to customers, material flow throughout the system, and the number of products stored in warehouses. It should be noted the problem's parameters are considered fuzzy to get closer to reality. Finally, by performing several sensitivity analyses, valuable managerial insights were obtained. Applying the developed model to the case study of a coffee company assisted managers in making optimal strategic decisions to establish new MCs and DCs, and reduce CO2 emissions.

Bi-objective green supply chain network design under disruption risk through an extended NSGA-II algorithm

Green supply chain network design plays an important role in reducing environmental pollution and resource consumption. Furthermore, emergency events may cause disruptions and seriously reduce the performance of entire supply chain. This paper considers a three-echelon supply chain network consisting of multi-manufacturers, retailers with disruption risks and capacity constraints, and consumers with green preference. Different from traditional supply chain network design, this paper considers respectively two objectives from the manufacturers and consumers with green preference in selecting appropriate retailers under disruption risks. Moreover, we propose an extended elitist non-dominated sorting genetic algorithm (NSGA-II) to solve the bi-objective optimization model. The extended algorithm is considered to combine original NSGA-II algorithm with the migration operation of the biogeography-based optimization (BBO) algorithm, thus leading to enrich the diversity of the population and improve the ability of global searching. Compared with the traditional NSGA-II algorithm, the extended NSGA-II algorithm can obtain more Pareto solutions with higher efficiency. Furthermore, the result shows a trade-off relationship between the manufacturer cost and consumer cost. Therefore, it is necessary to separate the two objectives of manufacturers and consumers in green supply chain network design.

Green cross-dock based supply chain network design under demand uncertainty using new metaheuristic algorithms

<p style='text-indent:20px;'>This study concerns the optimization of green supply chain network design under demand uncertainty. The issue of demand uncertainty has been addressed using a scenario-based analysis approach. The main contribution of this research is to investigate the optimization of cross-dock based supply chain under uncertainty using scenario-based formulation and metaheuristic algorithms. The problem has been formulated as a two-objective mathematical model with the objectives of minimizing the costs and minimizing the environmental impact of the supply chain. Two metaheuristic algorithms, namely non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective invasive weed optimization (MOIWO), have been developed to optimize this mathematical model. This paper focuses on the use of new metaheuristic algorithms such as MOIWO in green supply chain network design, which has received less attention in previous studies. The performance of the two solution methods has been evaluated in terms of three indices, which measure the quality, spacing, and diversification of solutions. Evaluations indicate that the developed MOIWO algorithm produces more Pareto solutions and solutions of higher quality than NSGA-II. A performance test carried out with 31 problem instances of different sizes shows that the two methods perform similarly in terms of the spread of solutions on the Pareto front, but MOIWO provides higher quality solutions than NSGA-II.</p>

A Multi-Objective Optimisation for Green Supply Chain Network Design Problem Considering Economic and Environmental Sustainability

A Multi-Objective Optimisation for Green Supply Chain Network Design Problem Considering Economic and Environmental Sustainability

A multi-objective green supply chain network design with price-dependent demand

A multi-objective green supply chain network design with price-dependent demand

Green supply chain management under uncertainty: a review and content analysis

ABSTRACT This study reviews the literature in the field of green supply chain management (GSCM) under uncertainty published from 2011 to 2020. Following the systematic review process, 198 articles are analyzed and classified according to the content framework. This study contributes to the related literature in the following ways. Firstly, it focuses on the bibliometric analysis of the GSCM under uncertainty, which provides a better understanding of this research field. Second, it identifies the uncertainty factors and highlights the research directions in this domain. The proposed uncertainty factors mainly consist of internal uncertainty, external uncertainty, and hybrid uncertainty. In addition, it shows that the current research covers the following themes: green supply chain network design (GSCND), collaboration, transportation optimization, supplier evaluation and practice and performance. Third, this study draws attention to uncertainty management methods. The fuzzy method, the stochastic method and the grey theory method are the main methods to solve the uncertainties of GSCM at present. Finally, it also identifies the gaps existing in the literature and proposes research agendas that can be expanded in the future.

A Green Dual-Channel Closed-Loop Supply Chain Network Design Model

Environmental considerations have become a significant issue in the design of supply chain networks due to today's increasing globalization trends. Therefore, supply chain network design needs to be managed in an efficient way to deal with the complex networks involved. The aim of this article is to present a multi-objective optimization model for a green dual-channel supply chain network that handles economic and environmental objectives to optimize network flow. A complex mixed-integer linear programming model (MILP) has been proposed in a green dual-channel and closed-loop supply chain (CLSC) network design. The main objective of the generated MILP model is to investigate the optimal selection of echelons and the optimal selection of transportation alternatives between these echelons in a CLSC network that includes an e-commerce channel structure based on economic and environmental considerations. Environmental aims are achieved by decreasing CO2 emissions and by reducing PM (particulate matter) concentration throughout the network. In addition, economic aims are also met by minimizing the overall cost. The validity of the presented model is supported by a case study in the home appliances industry. The results indicate that this model provides valuable knowledge and various alternatives to managers and policymakers depending on the different weight combinations.

Green supply chain network design under uncertainty conditions with the mathematical model and solving it with a NSGA II algorithm

Purpose: In this paper a mathematical model for the green supply chain network problem is designed. In this research, we seek to optimize two inconsistent and conflicting goals of the problem which are as follows: 1.Minimization of costs 2.Minimization of environmental impacts, using of the economic indicator 99 method. Methodology: In this paper, two methods of Epsilon constraint and NSGA II algorithm are used to solve the two-objective model with the objective functions of minimizing network costs and minimizing emissions. Findings: The results show that the introduced NSGA II algorithm has a high efficiency in forming efficient solutions in a short time. Originality/Value: In this paper, a two-objective model for green supply chain network is modeled and solved with the aim of reducing network costs and reducing greenhouse gas emissions.

A new bi-objective green medicine supply chain network design under fuzzy environment: Hybrid metaheuristic algorithms

This paper studies the design of the green medicine supply chain network under uncertainty, which integrates allocation, location, production, distribution, routing, inventory, and purchasing problems. The main contribution of the current paper is to design a fuzzy bi-objective Mixed-Integer Linear Programming (MILP) model for a multi-period, three-echelon, multi-product, and multi-modal transportation green medicine supply chain network (GMSCN). Additionally, the main aim of this network is to consider the environmental impacts related to the establishment of pharmacies and hospitals, by focusing on the reduction of greenhouse gases and the control of environmental pollutants. Therefore, to cope with uncertain parameters, fuzzy programming is utilized to examine uncertainty parameters. To solve the GMSCN model, meta-heuristic algorithms are used, including social engineering optimization, improved kill herd, improved social spider optimization, and hybrid whale optimization and simulated annealing. In this regard, two new hybrid algorithms called hybrid Firefly Algorithm and Simulated Annealing (HFFA-SA) and Hybrid Firefly Algorithm and Social Engineering Optimization (HFFA-SEO) to solve the proposed model for the first time are developed. In order to show the applicability of our paper and the lack of benchmark functions in the literature, a set of simulated data in two sizes including small- and large-sized problems is provided. Finally, the results of the analysis and the designed problems indicate that the GMSCN model and developed solution approaches are promising.