Closed-loop Supply Chain Network Research Articles

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.

Pricing decisions in the closed-loop supply chain network, taking into account the queuing system in production centers

In this paper, a multi-echelon closed-loop supply chain network is modeled considering pricing decisions and queuing system under uncertainty. The mathematical model's objective is to maximize the net present value (NPV) under uncertain parameters of potential demand and transportation costs. Therefore, the sensitivity of the actual demand on the pricing decisions of the final and returned products in the supply chain network. The main decisions of the mathematical model are to maximize the NPV related to the location of potential facilities, determine the amount of product inventory in the production center, reduce the length of the queue of production lines and determine the optimal amount of product transfer flow between facilities. Also, the most important decisions related to the related network are determining the optimal price of final products and returns in the supply chain network. Flexible Robust-Fuzzy optimization (FRFO) method is used to control the uncertainty parameters and the four algorithms G-HHO, PSO, ALO and GWO are used to solve the problem. The calculation results show that with the increase of uncertainty in the network, the value of NPV decreases and also with the increase of the number of production lines, due to the reduction of the queue length of the parts, the cost of waiting time decreases and the value of NPV increases. On the price of final and return products, the price in the forward and backward chain has increased and as a result, the NPV value has increased. By analyzing more tests, it was also observed that the G-HHO algorithm obtained the highest average NPV value in solving large numerical examples. While the PSO algorithm has reached the near-optimal solution in the shortest time. Also, there has been no significant difference between the averages of the comparison indices between the solution algorithms. Examination of the solution results between the solution methods shows that the maximum relative difference between the results is less than 5%, and the proposed G-HHO algorithm has the highest efficiency in solving large sample problems.

Multi-objective optimization of recycling and remanufacturing supply chain logistics network with scalable facility under uncertainty

ABSTRACT Recycling and remanufacturing logistics network affects the scale and efficiency of sustainable development of the manufacturing industry. This paper designs a multi-level closed-loop supply chain network with supplier, manufacturer, recycling centers, preprocessing centers and processing centers. An improved nonlinear grey Bernoulli-Markov model is developed to predict the recycled quantity. The capacity of recycling center and preprocessing center, the demand of manufacturer and the inventory of preprocessing center are formulated as constraints. A dynamic multi-objective model, which is based on scalable logistics facilities, takes into account the minimization of system operating costs and minimization of time costs related to the out-of-stock and inventory in each operating cycle. This model realizes the dynamic selection of the scale of facilities. Objective weighted genetic algorithm is adopted to transform multi-objective optimization problem into a single-objective. A scrap automobile products calculations are analyzed to verify the effectiveness and practicability of this model.

Resilient Supply Chain Planning for the Perishable Products under Different Uncertainty

In this research, the perishable products’ closed-loop supply chain network design problem is assessed by considering the disruption in production and distribution capacity and taking into account the uncertainty in customer demand. The main contribution of this research is modeling perishable products’ supply chain optimization and providing intelligent solution methods. In this regard, a mixed-integer mathematical model is proposed. This mathematical model consists of three objective functions. The first objective function is related to profit maximization, the second objective function is to minimize delivery time, and the third objective function is to reduce lost business days. Moreover, non-dominated sorting genetic algorithm II (NSGAII) and Multi-Objective Evolutionary Algorithm (MOEA) have been applied to optimize the proposed model. The research results show that the proposed meta-heuristic algorithm can provide a complete set of Pareto solutions in a reasonable amount of time. Moreover, based on different criteria, MOEA provides the non-dominated solutions with a higher quality, while NSGAII presents the Pareto boundary with more solutions than MOEA.

A proposed multi-objective model for cellphone closed-loop supply chain optimization based on fuzzy QFD

A Closed-Loop Supply Chain Network (CLSCN) includes forward and reverse supply chains. In this research, a dynamic cellphone closed-loop supply chain is considered, which contains multiple plants, retailers, demand markets, and collection centers. The returned cellphones are significant parts of electronic waste, and there is an increasing demand for this product due to the technology advancement and population growth. In this investigation, a Mixed-Integer Linear Programming (MILP) model is proposed and solved with the GAMS software to configure a CLSCN in the Peel Region in Ontario, Canada. The results of the optimization model show which facilities should be open for minimization of the total cost of the network. Then, the model is extended to a multi-objective model by adding two objectives considering CO2 emissions and the quality aspects of the CLSCN. To handle the qualitative factors, a fuzzy Quality Function Deployment (QFD) model is discussed in this paper. Then, weighted-sums and ε-constraint methods are utilized to detect efficient solutions for the CLSCN. Finally, the results are discussed and the conclusions are provided. Based on the results, the weights of the objective functions have significant effects on both the selection of the facilities and the flows of the products between them.

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

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

A multi-echelon fish closed-loop supply chain network problem with carbon emission and traceability

This study aims to develop a mathematical model to investigate a sustainable and traceable fish closed-loop supply chain network problem. The model considers the carbon emissions of transport, production, and warehouse activities. This study examines the fish supply chain that is supplied by the sea farms, fishponds, warehouses, wholesalers, plants, distribution centers, fish recycling centers, livestock food markets and distribution to multi-customer as well as considers multiple periods. The model uses a mixed-integer linear programming (MILP) to minimize total cost, which is included the production and traceability costs, transport costs, inventory costs, and emission costs. A numerical example is illustrated to verify the proposed model and provide managerial insight to the relevant industry. The results show that the total cost of forward distribution and reverse distribution make 79.84% and 14.65% contributions to the total cost, respectively. Finally, a sensitivity analysis shows a relationship between the total costs and emissions.

Sustainable and reliable closed-loop supply chain network design during pandemic outbreaks and disruptions

This paper aims to design a mathematical model for sustainable closed-loop supply chains during the coronavirus (COVID-19) outbreak. The proposed multi-objective model minimizes the total cost, carbon emission, and infection risk of the network and also maximizes social benefits. Some practical conditions such as stochastic demands, supplier selection, and facility disruptions are considered in the model. This stochastic model is transformed into a deterministic model by the Chance-Constrained Programming (CCP) approach. Thirty test datasets are applied to examine the effects of COVID-19 on the performance of the network. The problems are solved under two conditions (with and without COVID-19 considerations), and the results show that the total inventory of the model with COVID-19 is higher than the model without COVID-19. Moreover, the value of aggregated objectives of the model with COVID-19 is greater than the other model which indicates the greater distance from the optimal objectives. However, the computational efficiency of the model without COVID-19 is much higher than the other one. Some sensitivity analyses are also carried out, and several policy implications are proposed. According to the demand sensitivity analysis, when managers expect that the demands will increase, they should focus on the infection risk objective. On the contrary, when they expect decreased demands, they should pay more attention to environmental and economic objectives. The analysis of the objectives’ weights indicates that investing in the environmental and COVID-19 risk dimensions needs more financial resources compared to investing in the social dimension.

Resilient mixed open and closed-loop supply chain network design under operational and disruption risks considering competition: A case study

This study addresses the problem of resilient mixed open and closed-loop supply chain network design under operational and disruption risks as a fundamental domain in supply chain management. To deal with disruption risks, several resilience strategies (including multiple sourcing, facility fortification, adding extra production capacity, multi-channel distribution, and pricing) are applied. A model with the objective of maximizing the profit is proposed, which uses a two-stage stochastic programming approach. In order to cope with the problem's complexity, a constructive heuristic method and two metaheuristics, including an improved genetic algorithm (IGA) and an improved particle swarm optimization (IPSO) are developed. After tuning the parameters of metaheuristics via the Taguchi method, various numerical instances are utilized to test the validity and efficiency of the model and solution methods. Further, a real industrial case study is provided and analyzed. Results show that the constructive heuristic outperforms other solution methods and the performed analysis indicates that utilizing resilient strategies has a remarkable effect on reducing losses originating from risks and maintaining market share against competitors.

A multi-period closed-loop supply chain network design with circular route planning

A multi-period closed-loop supply chain network design with circular route planning

Pricing and advertising decisions in a direct-sales closed-loop supply chain

Remanufacturing and recycling of end-of-life products have changed the structure of supply chain networks, and the option of closed-loop supply chains gains popularity. Growing strict environmental and social legislations are expected to enhance sustainability of closed-loop supply chains. This study focuses on the pricing and advertising decisions in a closed-loop supply chain network. Although pricing decisions have been well-studied in this area, there is a little attention to advertising decisions. It is well-known that advertising plays a significant role in influencing customer behavior in returning end-of-life products for the closed-loop supply chain. Therefore, this research develops an operational and tactical plan for promoting advertising programs considering different elasticity effects. As such, the proposed optimization plan considers the pricing decisions in a more comprehensive view, where the price of similar products in the market and their substitution degree have a high impact on the profitability of manufacturers in a direct-sales closed-loop supply chain. Hence, the main novelty of this paper is to develop a new optimization model with pricing and advertising decisions in a direct-sales closed-loop supply chain. Since the proposed model is more complex than the majority of existing optimization models in the area of closed-loop supply chains, another novelty of this paper is to propose an improvement to the standard particle swarm optimization algorithm using the crowd-learning theory. The developed algorithm is validated by the exact solver and compared with the state-of-the-art algorithms in this research area. An extensive computational experiment is performed considering a number of comparative metrics. The findings show the superior performance of the proposed metaheuristic against the alternative solution approaches in terms of computational time and solution quality. Moreover, some important insights are obtained from this research, which could provide a basis for configuration of pricing schemes and advertising campaigns to improve the efficiency of closed-loop supply chains.

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 agent-based modeling framework for the design of a dynamic closed-loop supply chain network

The supply chain is a dynamic and uncertain system consisting of material, information, and fund flows between different organizations, from the acquisition of the raw materials to the delivery of the finished products to the end customers. Closed-loop supply chains do not end with the delivery of the finished products to the end customers, the process continues until economic value is obtained from the returned products or they are disposed properly in landfills. Incorporating reverse flows in supply chains increases the uncertainty and complexity, as well as complicating the management of supply chains that are already composed of different actors and have a dynamic structure. Since agent-based modeling and simulation is a more efficient method of handling the dynamic and complex nature of supply chains than the traditional analytical methods, in this study agent-based modeling methodology has been used to model a generic closed-loop supply chain network design problem with the aims of integrating customer behavior into the network, coping with the dynamism, and obtaining a more realistic structure by eliminating the required assumptions for solving the model with analytical methods. The actors in the CLSC network have been defined as agents with goals, properties and behaviors. In the proposed model dynamic customer arrivals, the changing aspects of customers' purchasing preferences for new and refurbished products and the time, quantity and quality uncertainties of returns have been handled via the proposed agent-based architecture. To observe the behavior of the supply chain in several conditions various scenarios have been developed according to different parameter settings for the supplier capacities, the rate of customers being affected by advertising, the market incentive threshold values, and the environmental awareness of customers. From the scenarios, it has been concluded that the system should be fed in the right amounts for the new and refurbished products to increase the effectiveness of factors such as advertising, incentives, and environmental awareness for achieving the desired sales amounts and cost targets.

Leveraging the circular economy with a closed-loop supply chain and a reverse omnichannel using blockchain technology and incentives

PurposeThis paper aims to analyze the benefits of the blockchain to the circular economy (CE), which is composed of both closed-loop supply chain (CLSC) systems and reverse omnichannel solutions. By ensuring transparency, traceability, visibility and security, the blockchain allows firms to acquire operational capabilities through a CLSC and service capabilities through a reverse omnichannel, which can boost business performance considerably. The related network of relationships can be reinforced by establishing incentives, which entail both smart contracts in the blockchain and active return approaches in CE.Design/methodology/approachAfter identifying the boundaries of the theoretical framework, several research hypotheses are developed according to the literature review and emerging gaps. These gaps link to the impact of the blockchain on CE systems (CLSC and reverse omnichannel), as well as the influence on business performance. The hypotheses are then tested using structural equation modeling and adopting a partial least squares-path modeling technique on a dataset composed of 157 firms. Finally, multigroup analysis is used to test the impact of incentives on the research hypotheses.FindingsThe blockchain facilitates a more efficient CE system, although reverse omnichannel solutions seldom bring any benefits to performance. The shift from a passive to an active return approach must be carefully evaluated. The CLSC network can benefit from an active return approach by developing appealing incentives for collectors and enhancing the positive effects of the blockchain. In contrast, consumer incentives can have detrimental effects on the blockchain. Various combinations of incentives can only bring a few business performance increases, while collector incentives are vital to reinforce the CE system's operational and service capabilities.Originality/valueThis paper takes a new approach toward the study of CE, which considers a dual circular system composed of a CLSC and a reverse omnichannel. The research explores whether the adoption of blockchain technology enables better return processes by improving the operations in CLSC and services in reverse omnichannel. Finally, this is the first empirical work to evaluate the benefits emerging from incentives, which can activate smart contracts in the blockchain and enable active return approaches in CE.

Multi-objective closed-loop supply chain network design: A novel robust stochastic, possibilistic, and flexible approach

Nowadays, the production of stone paper, in addition to its widespread utilization in various fields, does not require water consumption, cutting down trees, and stone paper products are easily recyclable and recoverable. Due to the importance of developing and using stone paper and paying attention to environmental matters, Closed-Loop Supply Chain Network Design (CLSCND) is very important for stone paper products. Moreover, due to epistemic, randomness uncertainties, the uncertainties in Objective Function (OF), and the flexible constraints for CLSCND in the real world, this paper introduces a novel Mixed Robust Stochastic, Possibilistic, and Flexible Programming (MRSPFP) approach based on credibility theory. The different attitudes of the Decision-Makers (DMs) are addressed by a more flexible measurement of the optimistic and pessimistic parameters using the criterion of credibility. In the study, a comprehensive procedure is proposed for stone paper CLSCND, to minimize costs, increase responsiveness by minimizing transit time between different facilities, and regarding environmental concerns into account through minimizing carbon emissions. The model is solved utilizing an interactive fuzzy programming solution procedure and the Best-Worst Method (BWM). The results of sensitivity analysis, the effect of changing the problem parameters, and the performance of the proposed model are investigated and compared. The results show that the MRSPFP model has a better performance compared to other previous models. The MRSPFP model performs better in strategic decisions that require high investment costs due to the minimization of the absolute deviation of OF from its mean. Also, the applied results of the study show that CLSCND has a good capability and potential for sustainable development in the field of stone paper.

The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network

The capacitated lot-sizing and energy efficient single machine scheduling problem with sequence dependent setup times and costs in a closed-loop supply chain network

Remanufacturing Strategy Choice of a Closed-Loop Supply Chain Network Considering Carbon Emission Trading, Green Innovation, and Green Consumers.

Due to increasingly serious environmental pollution problems and governments’ strengthening of environmental impact supervision, manufacturing companies are seeking green production methods, implementing carbon trading systems, and promoting the trend towards green remanufacturing. Thus, this paper introduces green factors to the existing closed-loop supply chain network models and studies the impact of carbon trading, green innovation efforts, and green consumers on the choice between two remanufacturing strategies: an in-house remanufacturing strategy and an authorized remanufacturing strategy. The results concerning the choice of the remanufacturing strategy are as follows: from the perspective of obtaining more profits, when the carbon trading price is low, the companies choose the authorized remanufacturing strategy; when the carbon trading price is high, the companies choose to remanufacture by themselves. For all the green innovation efforts and the proportions of green consumers, when the recovery rate of the used product is low, the companies choose to remanufacture by themselves; when the recovery rate of the used product is high, the companies choose the authorized remanufacturing strategy.

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.

Viable closed-loop supply chain network by considering robustness and risk as a circular economy.

The viable closed-loop supply chain network (VCLSCND) is a new concept that integrates sustainability, resiliency, and agility into a circular economy. We suggest a hybrid robust stochastic optimization by minimizing the weighted expected, maximum, and entropic value at risk (EVaR) of the cost function for this problem. This form considers robustness against demand disruption. Finally, CLSC components are located, and quantity flows are determined in the automotive industry. The results show that the VCLSCND cost is less than not considering viability and has a − 0.44% gap. We analyze essential parameters. By increasing the conservative coefficient, confidence level, and the scale of the main model, decreasing the allowed maximum energy, the cost function, time solution, and energy consumption grow. We suggested applying the Fix-and-Optimize algorithm for producing an upper bound for large-scale. As can be seen, the gap between this algorithm and the main problem for cost, energy, and time solution is approximately 6.10%, − 8.28%, and 75.01%.

A multi-objective optimization framework for a sustainable closed-loop supply chain network in the olive industry: Hybrid meta-heuristic algorithms

A closed-loop option for reusing, remanufacturing, and recycling waste products in the olive industry creates a high added value for this business network. This fact motivates the implementation of closed-loop supply chains for adopting an efficient and sustainable strategy in the olive industry in Iran. As one of the developing countries, Iran must redesign their supply chain networks to meet the standards of sustainable development goals. In this regard, the triple bottom line approach focuses on a sustainable design considering all economic, environmental, and social factors in supply chain networks. The main novelty of this paper is to merge the sustainable Closed-Loop Supply Chain Network (CLSCN) design and the olive industry. Hence, a multi-objective optimization framework is proposed to make location, allocation, and inventory decisions for the considered problem. Based on the triple bottom line approach, the objectives of the optimization model are to minimize the total cost and carbon dioxide (CO2)emissions and maximize job opportunities. In small instances, the model is solved by an epsilon-constraint method. To address the high complexity of large-scale networks, this study innovates new hybrid optimization algorithms. In this regard, a hybrid of Virus Colony Search Algorithm (VCS) and Simulated Annealing (SA) and a combination of Electromagnetism-like Algorithm (EMA) and Genetic Algorithm (GA) are proposed for the first time. To confirm their efficiency, an extensive comparison with individual algorithms is carried out by different multi-objective optimization metrics. Finally, some sensitivity analyses are performed to discuss some practical insights for the supply chain managers working in the olive industry in Iran.