Reverse Logistics Network Design Research Articles

Multi-echelon reverse logistics network design in the context of circular economy: a Hong Kong case study

The management of construction and demolition waste (CDW) is emerging as a significant global challenge. Traditional methods such as direct landfill and incineration fail to handle the vast amount of CDW properly, resulting in serious social and environmental issues. In this context, the construction industry has increasingly recognized the importance of CDW recycling and recovery. Consequently, construction industry leaders are advocating for development of reverse logistics networks for multiple types of CDW by solving a multi-objective mixed-integer linear programming model, which aims to restore waste for new structures toward circular economy. This model reduces landfill waste and maximizes the value of CDW, while minimizing total cost and negative environmental impact by integrating with the government under uncertainties to encourage sustainable management of CDW. A real-world case study from Hong Kong is conducted to examine and validate the practicability of the proposed model. Experimental results show that transportation activities have a significant impact on the reverse logistics network design since they are costly and generate the largest share of carbon emissions. In addition, a regulatory mechanism, i.e., setting carbon emission caps, proves effective in balancing facility workload and improving the utilization of underutilized facilities. The results also indicate that the proposed reverse logistics network is capable of adapting to costly transportation scenarios by prioritizing facilities with higher operational costs but lower transportation expenses.

Design and Site Selection of Reverse Logistics Network for Iron and Steel Enterprises from the Perspective of Green Supply Chain

The iron and steel industry is not only one of the most important components of China's national economy, but also a typical resource-and-energy-intensive industry. Under the background of "Carbon Peaking and Carbon Neutrality Goals", it is of great significance for the iron and steel industry to improve the green closed-loop supply chain and develop circular economy. From the perspective of green supply chain, this paper puts forward the three-level network structure of waste steel reverse logistics based on the present situation of waste steel recycling in China, and uses k-means clustering algorithm and accurate center of gravity method to select locations for the primary and secondary nodes. The purpose of this paper is to provide a basis for iron and steel enterprises to carry out network optimization of reverse logistics.

Designing a multi-level reverse logistics network for waste batteries of electric vehicles under uncertainty—A case study in the Yangtze River Delta Urban Agglomerations of China

As a breakthrough for alleviating the impact of the energy crisis and improving the environment, the electric vehicle (EV) industry is entering a period of rapid development. The growth of this industry is expected to lead to a dramatic increase in the quantity of end-of-life EV traction batteries in the future, posing significant challenges to environmental pollution and the sustainable development of the EV industry. This study addresses the cascade utilization and uncertainties of waste batteries, by incorporating the development weight of the match degree between urban commercial environments and facility development, proposes a fuzzy chance constrained programming model for the design of multi-level reverse logistics network (RLN) for retired EV batteries. The objective is to minimize network costs and carbon emissions, determining optimal facility locations and transportation distribution schemes. The mathematical model is validated through a real case study in the Yangtze River Delta Urban Agglomerations in China, and sensitivity analysis is conducted to explore the impact of various influencing factors on network design. The results indicate that the development weight significantly influence facility location decisions, while the quantity and quality of battery recycling have a substantial impact on the operational quantities of network facilities at different levels.

Design of Reverse Logistics Network for Power Battery Considering Transportation Risk

Design of Reverse Logistics Network for Power Battery Considering Transportation Risk

A two-level decision-support framework for reverse logistics network design considering technology transformation in Industry 4.0: a case study in Norway

Reverse logistics network design is a complex decision-making problem that involves the reuse, repair, remanufacturing, and recycling of end-of-life (EOL) under the tradeoff among conflicting objectives. The cutting-edge technologies in Industry 4.0 are now leading to an unprecedented and dynamic transformation of reverse logistics systems, which, however, further complicates the initial network design. In this paper, a two-level decision-support framework combined with both optimization and dynamic simulation is proposed to balance the cost, environmental impact, and service level in smart and sustainable reverse logistics network design under a dynamically evolving and stochastic environment. The results of a real-world case study in Norway show that the method can better support robust strategic decisions, eliminate dominated/near-dominated solutions, and yield holistic performance analyses considering smart reverse logistics transformation. The proposed two-level decision-support framework can better analyze the impact of the technology transformation of Industry 4.0 on reverse logistics systems, while it also provides a fundamental structure for digital reverse logistics twin.

A novel multi-level reverse logistics network design optimization model for waste batteries considering facility technology types

The increasing prevalence of electric vehicles (EVs) will lead to a continuous rise in the quantity of waste batteries in the future. The topic regarding reverse logistics network design (RLND) for waste batteries has garnered extensive attention from both the academic and societal realms. However, relevant research in this area has yet to delve deeper. This research proposes a mixed-integer linear programming (MILP) model to optimize the multi-level reverse logistics network (RLN) of waste batteries from the perspective of a circular economy. The model aims to minimize costs as an economic objective and to reduce carbon emissions as an environmental objective. It introduces an expected weighting factor to balance these two conflicting goals. Various technological levels are considered for different processing facilities, thereby enhancing the sustainability of the RLN. The model is applied to a numerical case study in the Nanjing Metropolitan Area of China, obtaining optimal network facility location schemes, technological configuration plans, and transportation schemes between facilities. The study emphasizes the significance of selecting different technology types for facilities, as rational process configurations effectively reduce recycling operational costs and decrease network carbon emissions. Sensitivity analysis emphasizes the impact of uncertain factors on RLND, highlighting the imperative need for a forward-thinking approach to ensure long-term sustainable development for enterprises. Managerial insights aim to guide enterprises and government departments, encouraging the adoption of higher technology types to achieve the dual goals of increased revenue and reduced carbon emissions.

Uncertain remanufacturing reverse logistics network design in industry 5.0: Opportunities and challenges of digitalization

Remanufacturing, a crucial step of reverse logistics, focuses on restoring or enhancing the functionality of waste products. The challenge in planning an effective remanufacturing reverse logistics system lies in the uncertainties from various sources. In addition, the evolving industrial landscape in Industry 5.0 necessitates adaptability to technological advancements. This paper proposes an integrated and digitalized architecture for uncertain reverse logistics network design. A fuzzy optimization model is first formulated to identify potential network configurations under varying demand-satisfying and capacity constraints. These solutions are automatically converted and assessed in a dynamic simulation environment with practical operational logic under a set of real-world scenarios. Numerical experiments are performed to validate the method and show the advantages of integrating optimization with dynamic simulation on a digital platform for strategic network planning. The results, built upon previous research, indicate that while initial investments in technology might be substantial, they may lead to long-term reductions in both costs and emissions. Moreover, collaborative decision-making is essential to mitigate potential disruptions and cascading effects. Our research contributes to the development of a novel integrated decision-support architecture and underscores the role of digitalization and Industry 5.0 in future smart and sustainable reverse logistics planning.

Reverse Logistics Network Design for Medical Waste Disposal under the Scenario of Uncertain Proposal Demand

With the development of the healthcare industry, the demand for medical services and protective equipment is boosted, causing the generation rate of infectious medical waste to increase rapidly. Therefore, it is of utmost importance for decision makers to effectively predict the potential risks and propose corresponding solutions. This paper investigates the reverse logistics network optimization for medical waste under the conditions of an uncertain proposal demand. Firstly, a prediction model of medical waste based on the SEIR epidemiological dynamics is constructed, in which both routine and public health emergency scenarios are simultaneously considered. Secondly, a bi-objective location-routing optimization model for a medical waste reverse logistics network is proposed, by simultaneously optimizing the total economic cost and potential risk throughout the entire logistics process. Subsequently, an NSGA-II algorithm is designed for a model solution in response to the model’s characteristics. The epidemiological dynamics-based prediction model is validated by the real case to be scientifically effective in predicting the amount of generated medical waste with a mean absolute percentage error (MAPE) of 18.08%. The constructed reverse logistics network model and the NSGA II algorithm provide a medical waste process center location, transportation routing, and vehicle selection solutions for both routine and emergency public health cases of Xi’an city with large, medium, and small scales. The above results indicate that the research scheme proposed in this paper could significantly reduce the medical waste logistics-related risks and costs and provide decision makers with more safe and reliable logistical solutions.

The multidepot vehicle routing problem with intelligent recycling prices and transportation resource sharing

The increasing focus on environmental regulations and the economic advantages of recycling has spurred interest in the design of multidepot reverse logistics networks (MDRLNs). In these networks, the growing use of intelligent recycling bins (IRBs) has been beneficial for both product recycling and standardizing recycling product pricing. Furthermore, collaboration and resource sharing enhance the efficiency of resource utilization and recycling. This study proposes a multidepot vehicle routing problem with time windows that incorporates intelligent recycling prices (IRPs) and transportation resource sharing (MDVRPTW-IRPTRS). Initially, a linear function is developed to define the relationship between the volume of returned products and IRPs. Subsequently, the problem is expressed as a mathematical model aiming to minimize total operating costs and maximize total recycling profits. Additionally, a hybrid algorithm that combines a three-dimensional (3D) k-means clustering algorithm with a self-adapting genetic algorithm-particle swarm optimization (SGA-PSO) is devised to determine the optimal solution for MDVRPTW-IRPTRS. The 3D k-means clustering algorithm is utilized to categorize IRBs within an MDRLN. The SGA-PSO algorithm incorporates elite preservation and self-adaptive update mechanisms to enhance the solution quality and algorithm convergence. A transportation resource sharing (TRS) strategy is integrated into the SGA-PSO, facilitating the allocation of shared vehicles to alternative recycling routes. A comparative analysis of SGA-PSO against other algorithms, including a hybrid genetic algorithm, an improved particle swarm optimization algorithm, and a hybrid genetic algorithm with variable neighborhood search, demonstrates its superiority in solving the MDVRPTW-IRPTRS. The model and algorithm are applied in a real-world case study in Chongqing, China, and the study discusses the optimized results under varying TRS strategies and IRP schemes, contributing to the development of an efficient and synergistic urban reverse logistics network. Moreover, the superior performance of the proposed approach is validated through the ablation experiments. This study offers valuable decision-making support for fostering an environmentally sustainable and resource-efficient city.

Multi-period reverse logistics network design for water resource management in hydraulic fracturing

This paper addresses the effective management of water resources during the hydraulic fracturing process used for natural gas extraction. With increasing demand and scrutiny on shale gas production due to economic, environmental, and social factors, effective water management strategies are needed to address the challenges of flow-back water usage during hydraulic fracturing. This research contributes to the existing literature from a multi-period reverse logistics network design perspective with realistic modeling approaches. A mathematical formulation is developed for optimizing the design of a multi-period reverse logistics network for effective water resource management that incorporates production scheduling of a hydraulic fracturing process. This mixed integer programming model determines the optimal locations and capacities of impoundments, treatment facilities, and disposal sites while minimizing fixed and operational costs under constraints such as freshwater withdrawal limits, production schedules, inventory balance, and treatment and disposal options. A two-stage stochastic programming formulation is further developed as a means to address the uncertainty associated with water flows during the fracturing operation. The strategic location and capacity decisions are given in the first stage of this formulation, whereas operational decisions such as water flows are handled in the second stage under independently sampled scenarios. A sample average approximation algorithm is proposed to solve the stochastic formulation. The model is tested on a case study from the United States that explores potential impacts on water resource management due to cost variations, changes in operational parameters, and uncertainty in demands and supplies. The base case setup delivers a 12.8% reduction in freshwater usage. The benefit of multi-period reverse logistics network design is further demonstrated through variations in water demand and equipment degradation. The managerial insights derived through sensitivity analysis highlight the values of the proposed multi-period formulation, capacity expansions, and parameter estimations.

Design of Reverse Logistics Network for Power Battery Considering Recovery Quality and Risk Value

Design of Reverse Logistics Network for Power Battery Considering Recovery Quality and Risk Value

State of the art in determining the optimal reverse logistics network for your product

The primary focus of this study is to explore and answer the following research question: “In what ways can organizations enhance the efficiency and responsiveness of their reverse logistics network design?” The study presents a conceptual framework that the authors have built, incorporating several essential variables to guide the process of network design. The conceptual framework for reverse logistics networks has been developed by conducting an analysis of the fundamental factors that influence network design decisions. This paradigm facilitates the decision-making process for firms when considering the centralization or decentralization of their reverse logistics operations and the outsourcing or insourcing of specific operational aspects. Moreover, the authors have discovered and highlighted relevant literature that exhibits strong alignment with our proposed framework, hence offering useful insights for decision-making in the domain of reverse logistics network design. Within our framework, two crucial dimensions are considered at each stage: the choice between internal processing vs . outsourcing and the decision to centralize or decentralize operations. With consideration of the factors noted above, this paper proposes a mixed integer linear programming model for the design of a reverse logistics network. This model aims to minimize the total cost at each stage of reverse logistics operations.

A simulation-based genetic algorithm approach for the simultaneous consideration of reverse logistics network design and disassembly line balancing with sequencing

Reverse logistics (RL) network design and disassembly line balancing (DLB) decisions are generally considered separately. In addition, the effect of disassembly sequencing on DLB is ignored. However, companies can take better-informed decisions by simultaneously considering RL, DLB and disassembly sequencing issues. This integrated approach results in more efficient processes which reduce cost and idle time, while maximizing throughput and customer satisfaction. In this study, we propose a simulation-based genetic algorithm approach for the joint optimization of RL network design and DLB with sequencing decisions. The use of simulation modeling allows for the consideration of stochastic aspects associated with RL and DLB such as transportation and disassembly times. A numerical example was provided to present the applicability of the proposed approach. Moreover, a sensitivity analysis was carried out to study the impact of various parameters. The results indicate the superior performance of the proposed approach with respect to total cost.

Multi-echelon sustainable reverse logistics network design with incentive mechanism for eco-packages

With the pollution caused by express packaging waste, eco-packages are being commonly used to promote a green supply chain. However, extant research has yet to address the efficacious reverse logistics network design problem while considering both economic and environmental consumption in the recycling process. In this study, a multi-echelon reverse logistics network is designed to collect eco-packages from customers, instead of door-to-door recycling by couriers. This study aims to solve the capacity location-routing problem in a reverse logistics network integrated with the incentive mechanism for customers, which is regarded as NP-hard. To avoid the combinatorial explosion, a multi-objective hybrid genetic algorithm-tabu search (MOHGATS) is developed to reduce search space and improve search speed by changing search strategies. Computational experiments are conducted to verify the proposed model and algorithm based on several test instances and a real-world case study. The impacts of the quantity of eco-packages, the incentive costs for customers, and the quantity and capacity of recycling stations on the reverse logistics network are explored from both economic and environmental perspectives, providing a reference for practical operation and promotion of eco-packages on a large scale.

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

중국 자동차 산업의 역물류 네트워크 최적화에 관한 연구

Reverse logistics network design is a strategic level of decision-making, and the recovery process of abandoned vehicles is inseparable from the support of a complete reverse logistics network. Against the backdrop of the green economy and the circular economy, this study designed a waste vehicle reverse logistics network under two constraints: carbon tax policy and carbon transaction market policy, respectively, and established a multi-purpose mixed integer linear plan model to minimize carbon emissions and costs. In addition, using a Chinese automobile manufacturer as an example, the results of the model of maximizing economic effects (i.e., minimizing costs) and maximizing environmental effects by conducting case verification and using lingo 11.0 are as follows. First, the cost of transportation using pure electric vehicles under the same carbon emission policy is lower than using fuel cars. Second, under the two carbon policies, carbon trading policies have a better emission reduction effect and lower network operating costs. Third, we found that transactional carbon constraints help companies reduce costs and reduce carbon emissions, and help shape long-term, even image-oriented, corporate behavior. By analyzing the optimization of China's waste vehicle reverse logistics network, this study promotes the sustainable and sound development of Chinese waste vehicle recovery companies and realizes the double benefits of the economic environment. There is a certain significance in the research and practice of developing reverse logistics activities in academia

A Novel Sustainable Reverse Logistics Network Design for Electric Vehicle Batteries Considering Multi-Kind and Multi-Technology

With the expansion of the new energy vehicle market, electric vehicle batteries (EVBs) have entered a massive retirement wave. The strategic level of facility location and configuration decisions and the tactical level of multi-product flow and multi-technology selection decisions have been integrated into a sustainable reverse logistics network (SRLN). In this paper, we considered multiple kinds of waste electric vehicle batteries (WEVBs) with multiple recycling technology and constructed a multi-level SRLN model for WEVBs with the objectives of minimum economic costs and minimum carbon emissions. To solve this model, fuzzy set theory was applied to the equivalence transformation of constraints, non-interactive and interactive methods were used to solve the multi-objective planning (MOP), and interactive fuzzy programming with priority control was proposed to find the global optimal solution for this model. Finally, numerical experiments demonstrated the feasibility and effectiveness of the proposed model and solution method. The experimental results show that the SRLN model considering carbon emissions can significantly reduce carbon emissions of the network through a slight increase in the initial network construction cost, thus effectively balancing both economic and environmental objectives. In the non-interactive solution, the Lp-metric method has a lower deviation index than the weighted sum method; in the interactive solution, the priority control method proposed in this paper outperforms the TH method in terms of the number of practical solutions and CPU time and shows strong performance in searching and finding optimal solutions. The proposed model and method can provide the theoretical basis and technical support for a WEVB SRLN under the limited information uncertainty environment.

A two-stage MCDM model for reverse logistics network design of waste batteries in Turkey

Inadequate environmental resources and overpopulation reveal the need to protect and recover natural resources attentively. In this sense, the reverse logistics concept emerged as a key solution since it deals with product flow from the final user to the origin. There are various items that need to be considered in well-planned reverse logistics network designs and one of these items is batteries which include hazardous and precious materials in it. Hence, waste management of batteries via recycling becomes a very significant issue from both economic and environmental benefits. Accordingly, depending on the importance of the topic, a two-stage methodology is proposed in this study for providing a network design under multiple objectives. Within the first stage, the importance weights of objectives are obtained via Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) and they are found as 0.248 for cost minimization, 0.3 for carbon emission minimization, 0.256 for employment rate maximization and 0.196 for development rate maximization. Afterward, in the second stage, a Multi-Objective Mixed Integer Linear Programming Model (MO-MILP) is developed to design the reverse logistics network and an application is performed in Turkey for validation. The model is solved for various scenarios including different quantities to be collected. Hence, it is obtained that the satisfaction degrees for employment and development objectives are 100% in all of the scenarios. However, the satisfaction degree of carbon emission minimization is around 96% and the less satisfied objective is the cost minimization having a satisfaction degree of 75% on average.

Column-and-constraint-generation-based approach to a robust reverse logistic network design for bike sharing

Dockless bike-sharing systems have grown rapidly in China in recent years. A large number of shared bikes have been put into use, which results in a serious problem of uncollected faulty bikes. However, since the exact number of faulty shared bikes cannot be known a priori, a reverse logistic network must be designed strategically to reduce the total costs of the collection process. This paper studies a robust reverse logistic network design problem of shared bikes that integrates the locations of recycling centres and the planning of collection routes in uncertain collection demand situations. A two-stage robust location routing model is presented to capture the uncertainty of collection demands, and then a set-partition reformulation is presented to address the computationally intractable model. We develop a column-and-constraint-generation-based algorithm combining a tailored column generation algorithm to efficiently solve the proposed model. Numerical experiments and parameter analyses, which are based on real data from bike-sharing companies, are conducted to evaluate the performance of the proposed algorithm. A case study is presented to further analyse the spatial features of the reverse logistic network of shared bikes. Benefits of applying robust optimization are demonstrated experimentally in terms of total cost under uncertain collection demand.

Design of Reverse Logistics Network for Waste Tire Incineration in Cement Factories

This study focuses on recovery options for end-of-life tires (ELTs). One of the most proper recovery options for ELTs is incineration of them in cement plants. There are 49 integrated cement factories which have Environmental Permit and License Certificate in Turkey as the final processing plant. Besides benefits of recovering ELTs to environment, cost aspect of process is crucial. In this study, logistic network design of waste tires sent from collection points to these factories to minimize the costs involved is planned. There are three echelons of supply chain as waste tires collection point, contractor firms and cement factories. Model for this network considers transshipment plan of waste tires between the echelons of supply chain and routes used to gather them. Model that deal with this system are expressed as mixed integer linear programming (MILP) problem. As a case study, model verified with data’s for Ankara