Sustainable Closed-loop Supply Chain Network Research Articles

Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

Optimization modelling for a sustainable closed-loop supply chain network using IoT: multiobjective metaheuristic algorithms

A novel multi-objective robust possibilistic flexible programming to design a sustainable apparel closed-loop supply chain network

Designing a sustainable Closed-Loop Supply Chain (CLSC) network is imperative for the apparel industry, given its escalating adverse effects on economic, environmental, and social dimensions. In this study, a novel tri-objective location-allocation optimization model is specifically developed for designing a sustainable apparel CLSC, incorporating the industry's unique facilities. The aim of the model is to simultaneously minimize the costs and negative environmental impacts while maximizing social benefits under demands and returns uncertainty. A notable research contribution lies in addressing the unique challenges of treating different types of returns, including commercial, End Of Use (EOU) and End Of Life (EOL) returns due to their uncertain quality and quantity. Additionally, the model optimizes the environmental performance levels of production facilities, a novel aspect in the apparel CLSC research. Moreover, the flexibility of constraints related to the demand fulfilment is considered. To cope with such flexibility and uncertainties, a new hybrid robust possibilistic flexible programming model is developed, by extending the previous methodologies. A core innovation of this solution approach lies in the pioneering utilization of hexagonal fuzzy numbers for uncertain epistemic parameters, making a significant advancement in the field of CLSC. Comparative analysis with the similar studies demonstrates the superiority of the proposed model, incorporating hexagonal fuzzy numbers over the method using triangular fuzzy numbers. Furthermore, AUGMECON method using lexicographic optimization is applied to handle the multi-objective model. The application of the proposed model is shown focusing on Southwestern Ontario in Canada. The results reveal that commercial and EOU returns have a more detrimental impact on economic, environmental, and social sustainability aspects compared to EOL returns.

Optimization Of a Sustainable Closed Loop Supply Chain Network (SCLSCN): A Modified NSGA-III Method

For a generic closed-loop supply chain network (CLSCN), a mixed-integer Linear programing (MILP) Model was developed that simultaneously optimizes three of the fundamental elements of sustainability: cost, social impact, and environmental effect. Many real-world features of SC operations, which includes multiple products types, multiple echelons, multiple production technologies, and multiple modes of transportation, were all incorporated into the model during its development. As a result of the complexity of the model and need to provide Pareto front for decision maker, a novel hybrid NSGA III (HNSGA-III) was developed to solve the model. The Relative Gap was used result of the model was validated using the ε-constraint method. the algorithm parameters were tuned using Taguchi design then the performance of HNSGA III as compared to NSGA III, HypE and MOEA/D was assessed using RNS, SNS, IGD, CPU times and HC index. The result clearly shows the HNSGA III outperforms the other meta-heuristic algorithms. Keywords: Closed loop supply chain network, Sustainability, CO2 Emission, Multi-objective model, HNSGA III, NSGA III, MOEA/D, HypE Victor Jinn, Harold Ugochukwu Nwosu & Matthew Uzoma Shadrach (2023): Optimization Of a Sustainable Closed Loop Supply Chain Network (SCLSCN): A Modified NSGA-III Method. Journal of Advances in Mathematical & Computational Science. Vol. 10, No. 4. Pp 45-70. dx.doi.org/10.22624/AIMS/MATHS/V11N4P4. Available online at www.isteams.net/mathematics-computationaljournal.

A triple bottom line approach for designing a sustainable closed-loop supply chain network in fruit industry: A metaheuristic solution approach

In this study, the design of a sustainable closed-loop supply chain network for agricultural products with the goals of minimizing the cost and emission of greenhouse gases and maximizing the response to customer demand, and creating justice-based job opportunities, simultaneously, are aimed. The intended chain is based on the fruit supply chain study, which includes fresh fruits, concentrate, and vermicompost fertilizer. A mixed-integer linear programming (MILP) model has been developed to achieve the triple bottom line. Due to the nature of the NP-hard problem, the proposed model is solved using metaheuristic approaches consisting of two renowned algorithms, NSGA-II and NRGA, and a relatively new algorithm called NSGA-III. It is worth noting that the parameters of the algorithms are adjusted to achieve the best performance in small, medium, and large-size problems exerting the Taguchi method. After comparing the results of the three algorithms based on the well-known criteria, NRGA is introduced as the superior algorithm. Ultimately, the results of sensitivity analysis indicate that appending the possibility of using vermicompost in gardens and considering several vehicles in the proposed sustainable supply chain boosts the values of economic and environmental objective functions to about 6.4 and 8.2%, respectively.

An in-depth metaheuristic approach to design a sustainable closed-loop agri-food supply chain network

As an essential component of human life, agricultural products play a very important role in guaranteeing that individuals get all the essential nutrition. Governments and industries spend great financial resources, define short- and long-term goals, and organize their policies to develop a steady agri-food supply chain and provide fresh, healthy products to their societies. This work proposes a new mixed-integer linear programming model to propose an agri-food supply chain network design for the coconut industry under sustainable terms. This study mainly aims to solve a multi-objective closed-loop supply chain, considering both forward and reverse product movements. The model attempts to manage the net present value of total cost for specific planning horizons while monitoring environmental pollution and job opportunities within the network. Given the NP-hard nature of the network, the solution approach embraces a set of recently developed metaheuristics to overcome its complexity effectively. To this end, six multi-objective optimizers and three hybrid algorithms are utilized, among which the multi-objective artificial rabbit optimizer is first developed and applied in this study. Hence, the model's compatibility with real conditions is investigated using fifteen practical tests. The results of interval plots and the Friedman statistical test emphasize that optimizers can solve all sizes of problems. However, the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) outperforms solving practical tests according to both the results of statistical tests and the novel hybrid Multi-Criteria Decision Making (MCDM) framework.

Multi-objective optimisation of sustainable closed-loop supply chain networks in the tire industry

As environmental concerns and social legislation continue to gain importance, supply chain decision-makers are increasingly required to consider economic and ecological objectives. A potential strategy for mitigating sustainability issues entails the utilisation of discarded tyres through the process of recycling. Nevertheless, the establishment of a closed-loop supply chain that is both sustainable and profitable presents a noteworthy challenge. This study proposes a novel multi-objective mixed-integer linear programming model to design a sustainable closed-loop supply chain network in the tire industry. The objective of the model is to optimize the overall cost of the network, taking into account the environmental consequences related to the establishment of facilities, tire processing, and transportation. While metaheuristic algorithms have been extensively employed to solve network design problems, they are not very effective in handling large-scale networks. To overcome this limitation, our study introduces six new multi-objective evolutionary algorithms based on decomposition (MOEA/D) variants. The present study introduces a prospective methodology for devising supply chain networks that are sustainable in nature, while simultaneously ensuring a harmonious equilibrium between economic and environmental considerations. The efficacy of the proposed multi-objective mixed-integer linear programming model and its MOEA/D variants in addressing large-scale networks has been demonstrated through the obtained results. As such, this study contributes to sustainable supply chain management, which is becoming increasingly important in the current environment.

Designing a dual-channel closed loop supply chain network using advertising rate and price-dependent demand: Case study in tea industry

Today, with the development of the Internet, e-commerce, and digital advertising, along with traditional advertising, have attracted the attention of many sectors and businesses, including the agricultural industry. One of the most consumed agricultural products is tea, which advertising plays a significant role in attracting more customers and improving the efficiency of its supply chain. Therefore, managing the supply chain of such product should be monitored and configured effectively considering sustainability aspects. The aim of this study is to design a dual-channel Sustainable Closed-Loop Supply Chain (SCLSC) network for tea industry for the first time. Then, a Mixed Integer Linear Programming (MILP) model is proposed for optimizing the total costs, the number of emissions and the number of created job vacancies. In addition, customer demand is dependent on traditional and digital advertising rates and product prices. For solving this model in small dimensions, LP-metric method and in high dimensions, four meta-heuristic multi-objective algorithms including Simulated Annealing Algorithm (MOSA), Particle Swarm Optimization (MOPSO), Gray Wolf Optimizer (MOGWO) and Whale Optimization Algorithm (MOWOA) were used and their results and performance were compared. Furthermore, sensitivity analysis was performed to further evaluate some parameters such as demand. The obtained results confirmed the validity of the planned model and the correctness of its solution method. Finally, practical insights are provided for the related managers and tea producer countries.

A customized multi-neighborhood search algorithm using the tabu list for a sustainable closed-loop supply chain network under uncertainty

Environmental pollution and social inequalities have prompted the agricultural industry to implement sustainable supply chain management practices. However, supply chain operations and planning in developing countries such as Iran are often not economically, environmentally, and socially sustainable. To address this challenge, this paper presents a practical optimization model for sustainable closed-loop supply chain (SCLSC) management in the agricultural industry of Iran, with a focus on the olive crop. Based on the triple bottom line concept, which considers economic, environmental, and social sustainability, the proposed model makes location, allocation, and inventory decisions under uncertainty by developing a scenario-based robust optimization model. To solve this complex network design problem, we propose a metaheuristic algorithm with a multi-neighborhood procedure that efficiently handles the complexity of the problem. Specifically, we develop a customized Simulated Annealing (SA) algorithm using a tabu list to improve the initial solution found by a constructive heuristic algorithm. Our extensive analysis and comparison of our metaheuristic algorithm against the exact solver and two other powerful metaheuristic algorithms demonstrate the applicability of our SCLSC model for the agricultural industry in Iran and the high performance of the proposed metaheuristic algorithm for solving large-scale networks.

A scenario-based sustainable dual-channel closed-loop supply chain design with pickup and delivery considering social conditions in a natural disaster under uncertainty: a real-life case study.

In the context of the COVID-19 pandemic, utilizing the potential of online markets to sell products has become increasingly important for creating competitive advantages and ensuring the growth and survival of businesses. The pandemic has disrupted traditional business practices, and with social distancing measures in place, consumers have turned to online channels to meet their needs. As a result, businesses that have adapted quickly to online markets have been able to maintain their customer base and revenue streams. Thus, considering the potential of online markets is of utmost importance in the current pandemic situation. In this regard, the present research aims to provide a practical framework for creating a green and sustainable closed-loop supply chain network (SCLSCN), including the integration of online markets, to assist managers in making decisions that support economic, environmental, and social goals. Accordingly, a multi-objective mixed integer linear programming (MOMILP) optimization model was designed under uncertain demand and disruption caused by natural disasters in Iran's home appliance industry. The study also considered changes in the capacities of online and marketplace sales channels, revealing a significant reduction in costs at each stage. The results show that the increase in demand has a direct impact on the production level, warehousing, and transportation costs, leading to social impacts on the model. However, the current system cannot handle an increase in demand of more than 20%, requiring managers to make decisions to increase production capacity or build new factories. Thus, the study highlights the importance of considering online markets as a means to adapt to disruptions caused by the pandemic and maintain a competitive edge.

RETRACTED ARTICLE: Sustainable and optimal design of Chinese herbal medicine supply chain network based on risk dynamic regulation mechanism

We propose a robust fuzzy design model for a sustainable closed-loop supply chain network. The model is based on a risk dynamic regulation mechanism. In this way, we can solve the problem of sudden disruptions and uncertain demand in the supply chain of Chinese herbal medicines. We also develop a hybrid algorithm solution to solve the model and design a resilient supply chain network. The specific steps are as follows: (1) The risk dynamic regulation mechanism is created with strong risk resistance by considering the information sharing platform, facility defense, drying station scheduling, safety stock, and shared inventory. (2) Based on the dynamic risk regulation mechanism, we establish a sustainable Chinese herbal medicine supply chain network design model. Then, we use the robust fuzzy method and the epsilon constraint to deal with the uncertainty and integrate the model. (3) We introduce opposition-based learning, cosine convergence factor, and levy flight to the original Whale and Grey wolf algorithms to obtain the Hybrid algorithm, which is used to solve the processed model. The results show the model and algorithm proposed in this paper have strong applicability and advantages in designing closed-loop supply chain networks for Chinese herbal medicine and provide references for relevant decision-makers.

Designing a Scenario-Based Fuzzy Model for Sustainable Closed-Loop Supply Chain Network considering Statistical Reliability: A New Hybrid Metaheuristic Algorithm

In this study, a new nonlinear mathematical programming model of mixed integer was presented to formulate the problem of designing a sustainable closed loop supply chain, in which the three aspects of sustainability, i.e., social effect such as job creation, customer satisfaction, and distributors, environmental effects such as reducing air pollution, and economic effects such as reducing supply chain costs, increasing supply chain reliability, quality of returned products by customers, and product routing were considered. In order to solve the proposed model, a new hybrid metaheuristic algorithm based on the distinctive features of gray wolf algorithm and genetic algorithm was proposed in addition to MOPSO and NSGA-II algorithms. After tuning their parameters by the Taguchi method, their performance in problems with different dimensions was tested and evaluated by MID, DM, and SM criteria. The results of statistical analysis of indices indicated that no significant difference between the performance of the three algorithms at 5% error level. In general, GW-NS, NSGA-II and MOPSO algorithms had better performance in terms of MID index, respectively. In addition, GW-NS, NSGA-II, and MOPSO algorithms performed better in terms of DM index. NSGA-II, MOPSO, and GW-NS algorithms performed better in terms of SM index, respectively. In addition, the variability of DM index in all three algorithms was almost the same, but in MID index, GW-NS algorithm, and in SM index, MOPSO algorithm had the highest change and less sustainability.

Robust optimization of sustainable closed-loop supply chain network considering product family

Robust optimization of sustainable closed-loop supply chain network considering product family

A sustainable dynamic closed-loop supply chain network equilibrium for collectibles markets

In this paper, we present a sustainable closed-loop supply chain network equilibrium problem for collectible items in a time-dependent framework. In particular, we consider a network consisting of manufacturers, retailers, demand markets, and an online second-hand platform involved in forward and reverse logistics competition. Manufacturers as well as retailers try to control the amount of emissions generated in transaction processes. We analyze the optimal behaviors of all the decision-makers, and we give the governing closed-loop supply chain network equilibrium conditions. Then, we introduce the associated evolutionary variational inequality and the related infinite-dimensional projected dynamical system, which are convenient to solve such a problem. Finally, using a discrete-time approximation procedure, we solve a numerical example considering eBay as online platform.

Designing sustainable closed-loop supply chain network with considering spot-to-point inflation and carbon emission policies: A case study

This paper uses a fuzzy robust stochastic optimization (FRSO) approach and a new hybrid algorithm of genetic algorithm (GA) and multi-choice goal programming with Utility Function (MCGP-UF) to minimize environmental impacts (EI) while maximizing net present value (NPV) and social impacts (SI) in a sustainable closed-loop supply chain (SCLSC) in a case study has been proposed under hybrid uncertainty with considering spot-to-point inflation (STPI). The resulting mixed-integer linear programming (MILP) model has been applied to the electronic industry. The proposed model is evaluated in several experiments and discussed in different scenarios to prove the efficiency and performance comparison and validation of the proposed method. The results of this study double the share of the article (i) Effective improvement of the obtained solutions by reducing the solution time by 25% showed that the proposed method can be responsible for large-scale problems. (ii) Sensitivity analysis on parameters such as demand, inflation, costs, and capacity showed which scenario has the most impact on the strategic and tactical decisions of companies.

A neutrosophical model for optimal sustainable closed-loop supply chain network with considering inflation and carbon emission policies

In this paper, a stable CLSC problem is modeled in conditions of uncertainty and indeterminacy. The SCN is designed to maximize NPV and minimize carbon releases by maintaining environment friendly policies and accounting for the increase. To achieve a suitable model for designing a stable CLSCN and making important decisions such as selecting the right suppliers, selecting the type of transport, initialing the facility, the optimal flow between facilities, and accomplishing an efficient solution to the problem decision making, the neutrosophic optimization method is used. The results of experiments that discuss and evaluate different scenarios confirm the efficiency and validity of the proposed model. The findings also show that the effective improvement of the obtained solutions by reducing the solution time up to twenty percent can be responsible for large-scale problems in different scenarios. This paper uses a neutrosophic optimization method to solve the problem of designing a stable CLSCN under uncertainty and indeterminacy.

A Fuzzy Robust Programming Model for Sustainable Closed-Loop Supply Chain Network Design with Efficiency-Oriented Multi-Objective Optimization

Sustainable closed-loop supply chain (SCLSC) network design and decision-making is a critical problem for enterprises and organizations’ operations because of its excellent economic, environmental, and social performance. This article proposes a multi-objective mixed-integer programming model with targets for minimum total cost, reduction in environmental damage, and maximum social responsibility. In order to deal with the uncertainty caused by the dynamic business environment, a fuzzy robust programming (FRP) approach is applied. Furthermore, an efficiency-oriented optimization methodology, hybridizing meta-heuristics and efficiency evaluation, is proposed to solve the developed multi-objective model and functions as auxiliary decision-making. Data envelopment analysis is applied to evaluate the sustainability performance of feasible solutions and calculate their efficiency. The efficiency can comprehensively reflect the sustainability performance and guide the evolution process of meta-heuristic algorithms. A numerical case validates the proposed FRP model and efficiency-oriented optimization methodology. The results demonstrate that with the proposed methodology, decision-makers not only can obtain a set of efficient schemes but also can determine the optimal scheme with the best sustainability performance.

Research on Sustainable Closed-Loop Supply Chain Synergy in Forest Industry Based on High-Quality Development: A Case Study in Northeast China

Forestry construction is a fundamental issue related to sustainable economic and social development. However, the traditional development of forestry is currently facing the dilemma of insufficient resource supply, rising labor cost, and a low return on forestry investment, which needs to be transformed into high-quality forestry development. The general view is that synergistic development is an important way to achieve high-quality development. Based on this, the strategic planning and behavioral choices of forestry enterprises under synergistic development are explored. With the objectives of minimizing cost and environmental impacts, and maximizing social benefits, a multi-objective sustainable closed-loop supply chain network planning model is developed to study a real case of a forestry supply chain in northeastern China. A robust optimization approach is used to deal with the relevant uncertain parameters, and a weighted generalized epsilon-constraint approach is applied to solve the multi-objective problem, based on which a GA genetic algorithm is used to solve it. Based on the synergistic development perspective, four different scenario assumptions and sensitivity analyses are made to examine the multi-objective calculation results in this closed-loop supply chain network, and then to analyze the strategic decisions and specific measures for forest industry enterprises to achieve high-quality development. The model results show that realizing supply chain synergy is an effective way to achieve efficient business development in the future. Managers should also establish an acceptable balance between sustainability dimensions while focusing on building a collaborative supply chain development model, where small economic benefits can be appropriately ignored to reduce the harmful effects of the production process on the environment.

Integration of sustainable closed-loop supply chain with reliability and possibility of new product development: a robust fuzzy optimisation model

ABSTRACT The development of a new product is an essential factor in changing the structure of supply chains. This paper proposes a sustainable closed-loop supply chain network with the possibility of new product development. Using a robust fuzzy optimisation model, the objectives are maximising profits, maximising the number of created jobs, and maximising reliability. The environmental emissions are considered a limitation in our optimisation model. Some parameters are uncertain to make our problem more realistic, and accordingly, a robust fuzzy optimisation method is used. To manage the complexity of the proposed model, a Multi-objective Grey Wolf Optimisation (MOGWO) algorithm was developed for solving the large-sized tests. The performance of the proposed multi-objective optimisation algorithm is shown against a traditional method called a Non-dominated Sorting Genetic Algorithm (NSGA-II). Accordingly, 15 large-scale test problems are solved, and multi-objective optimisation metrics are defined to compare the performance of the algorithms. The results showed how significantly the uncertainty reduction could affect the values of total net profit and reliability. The applicability of the proposed model is shown in a real case study in the lighting industry to approve the appropriate performance of the proposed MOGWO.

Multi-Objective Sustainable Closed-Loop Supply Chain Network Design Considering Multiple Products with Different Quality Levels

International laws and increasing consumer awareness have led to drastic changes in traditional supply chain network designs. Moreover, because of environmental and social requirements, traditional supply chain networks have changed to sustainable supply chain networks. On the other hand, reverse logistics can be effective in terms of environmental and economic aspects, so the design of the supply chain network as a closed loop is necessary. In addition, customers have a demand for different products with different quality levels. Considering different types of customers with a variety of consumption trends can be a challenging issue, and is addressed in this study. The main contributions of this research are considering different quality levels for products as well as different tendencies of customers towards environmental issues. In this study, a sustainable closed-loop supply chain model is designed that seeks to balance economic, environmental, and social responsibilities. In this paper, costs and customer demands for different types of products at different quality levels are considered under uncertain conditions using a robust possibilistic programming method. The proposed multi-objective model is solved using the Augmented Epsilon Constraint (AEC) method that provides an efficient set of solutions for all decision-making levels. The results show that the robust possibilistic programming method is more effective in dealing with uncertainties than the possibilistic programming method.

An efficient matheuristic algorithm for bi-objective sustainable closed-loop supply chain networks

Abstract This paper develops an optimization model for a sustainable closed-loop supply chain network with two conflicting objectives, namely, the minimization of the total logistic costs and the total amount of carbon emissions. The first objective relates to financial benefits, whereas the second represents the wider goal of guaranteeing cleaner air and hence a greener and healthier planet. The problem is first modelled as a mixed integer linear programming based-model. The aim is to determine the location of distribution centres and recycling centres, their respective numbers and the type of vehicles assigned to each facility. Vehicle type consideration, not commonly used in the literature, adds another dimension to this practical and challenging logistic problem. A matheuristic using compromise programming is put forward to tackle the problem. The proposed matheuristic is evaluated using a variety of newly generated datasets which produces compromise solutions that demonstrate the importance of an appropriate balance of both objective functions. The robustness analysis considering fluctuations in customer demand is assessed using Monte Carlo simulation. The results show that if the standard deviation of the demand falls within 10% of its average, the unsatisfied demand is insignificant, thus demonstrating the stability of supply chain configuration. This invaluable information is key towards helping senior management make relevant operational and strategic decisions that could impact on both the sustainability and the resilience of their supply chain networks.