Reverse Logistics Network Research Articles

Developing a circular economy framework for e‐commerce packaging materials: A study on behavioural intentions of online consumers

AbstractThe exponential growth of e‐commerce brings the need for sustainable business models to address the negative environmental impacts of packaging. This study used a bibliometric analysis, the theory of planned behaviour and stakeholder theory to explore the behavioural intentions of consumers in adopting a circular economy model for e‐commerce packaging materials. Surveying 362 respondents in the Indian market, our findings revealed e‐commerce users were likely to participate in a closed‐loop system, with environmental concern and attitude emerging as pivotal factors. The study's novelty lies in examining consumers' willingness to engage in circular economy practices, using an extended model of the theory of planned behaviour. We propose leveraging existing reverse logistics networks for e‐commerce packaging that incorporate circular economy principles for packaging sustainability. The study contributes to the theory by emphasising the primacy of environmental concerns over financial incentives in motivating consumer participation in the emerging economies context. We advance the prioritisation of environmental considerations by e‐commerce consumers, which necessitates a focus on education and knowledge development on the circular economy. Building upon our findings from the Indian market context, we propose key recommendations for practice and policy, which could be applicable to other emerging markets and contribute to their CE adoption.

Big Data Analytics and Innovation in the Sustainable Performance of Electronic Waste Reverse Logistics: An Empirical Study in India

Abstract Electronic waste (e-waste), encompassing discarded electrical and electronic devices, demands effective reverse logistics management to ensure optimal resource use and environmental preservation. Despite growing interest in Big Data Analytics within the scientific community, its slow practical implementation in e-waste management and the absence of validated measurement models hinder both industry adoption and empirical studies. To address these challenges and drawing upon the Resource-Capability-Advantage (RCA) theory, this study aims to investigate the interplay between Big Data Analytics (BDA) management capabilities, BDA talent capabilities, Reverse Logistics (RL) innovation, and sustainable RL performance. A conceptual model was tested using primary data from practitioners and managers in India’s e-waste reverse logistics network, with Structural Equation Modeling (SEM) as the primary analytical method. The results highlight the multifaceted contributions of Big Data Analytics Management and Talent Capabilities to Reverse Logistics Innovation and Sustainable Reverse Logistics Performance.

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.

Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions

This study addresses the critical issue of managing construction and demolition waste in urban environments. Effective waste management is not only essential for minimizing costs but also for enhancing sustainability and reducing environmental impact. In this context, the research introduces a green reverse logistics model designed for C&D waste management, integrating both sustainability considerations and current regulatory frameworks, such as LEED. A key innovation of this model is the incorporation of electric vehicles for waste collection, compared to traditional diesel vehicles, as part of the logistical process, as carbon emission is a significant concern. By evaluating the limitations and opportunities associated with electric vehicles, alongside robust optimization to manage uncertainties in waste collection, the model seeks to balance environmental, social, and economic objectives. It further incorporates decision-making tools like fuzzy logic to optimize multi-objective outcomes across various waste facilities, including separation labs, incineration centers, recycling centers, and landfills. A case study conducted in Tehran validates the model, highlighting the socio-economic and environmental benefits of using electric vehicles in waste collection. Sensitivity analysis indicates that hybrid and socially focused policies perform best under high-impact scenarios, although results can differ with varying data sets. Despite the complexity of managing reverse logistics networks, this research provides valuable insights for supply chain planners. It suggests potential future directions, such as the application of metaheuristic algorithms and improved stochastic planning methods.

GIS-technologies and algorithm for developing regional systems for handling medical waste on the example of three regions

Introduction. GIS is a modern computer technology designed for collecting, storing, analyzing and visualizing spatial data – mapping. GIS is also actively used in healthcare to solve many regional problems, including optimization and management of healthcare resources. Over last decade, many researchers have used mathematical software models for healthcare waste management. A review of the literature shows facility location, risk to the public, and economic feasibility to be among the issues that have been extensively covered in the literature. Designing a reverse logistics network using mathematical software tools is an efficient and effective way to manage healthcare waste. The objective of the study. To determine the optimal approach to creating a functional model of a system for managing medical waste of class B and C using GIS-technologies for each of the three regions, based on the characteristics of each. Materials and methods. Medical institutions in three regions (St. Petersburg, Leningrad Region and Krasnoyarsk Territory) as sources of medical waste generation were considered taking into account their capacity and specialization. The composition and volumes of waste by class were determined using a calculation method based on indicative waste generation standards. The identification of local technologies for the neutralization of medical waste existing in medical institutions was carried out. The obtained data was processed statistically and displayed on electronic maps for subsequent use in GIS technologies, as well as for the development and justification of a waste management system for medical institutions, taking into account the prospects for the development of healthcare. Results. Based on the analysis of data on medical waste of classes B and C in individual medical institutions, as well as an analysis of the power of existing technologies for neutralizing this waste using GIS technologies, proposals were developed to improve the medical waste management system in three regions. The proposals are aimed at improving the sanitary and epidemiological situation in the regions over a five-year period and take into account the features of the existing transport infrastructure and the requirements of sanitary legislation. Limitations. The model of a medical waste management system using GIS technologies was used only for managing waste of classes B and C. Conclusion. To ensure the safe and environmentally sustainable functioning of the waste management system of classes B and C in the regions, it is necessary to apply an integrated approach based on GIS technologies. This approach should take into account the regional characteristics of the management of the medical waste disposal system and its economic feasibility and take into account the regional characteristics of transport logistics and the prospects for the development of healthcare.

Design of a reverse logistics network for medical waste considering location and routing decisions

Medical waste generated in healthcare facilities is categorized as hazardous due to its infectious, toxic, or radioactive properties, posing substantial risks to human health and the environment. This research proposes a bi-objective Mixed-Integer Linear Programming model for designing reverse logistics networks that enable both economical and safe medical waste management. Said model determines optimal quantity, depot locations, and routes for transporting waste from its generating points (hospitals) to the treatment and disposal sites (depots).This research addresses a bi-objective location routing problem, with multiple depots, time windows, capacity constraints, and variated waste types, across a multi-period horizon. It is innovative because no previous works have successfully dealt with such a combination of pragmatic features. The Location Routing Problem is an already well-known NP-Hard problem, with the additional aforementioned features further adding to its complexity. Thus, a genetic algorithm to solve large-sized realistic instances within reasonable computing times had been developed.Numerical experiments considered 20 random instances contrasting exact and approximate solution approaches. Also, a medical waste collection case study of a public hospital network in Atlántico Department, Colombia, delivering a Pareto frontier solution set, was introduced. The results demonstrated the efficacy of the proposed genetic algorithm in successfully addressing small instances of the problem, delivering outcomes comparable to the Mixed-Integer Linear Programming model. Furthermore, it yields good-quality solutions in a reduced computational time for larger instances deemed unfeasible for the Mixed-Integer Linear Programming model.This article contributes to both scientific literature and practitioners by presenting a decision-making tool designed to address the medical waste reverse logistics challenge under realistic scenarios. It approaches the issue from a bi-objective standpoint, jointly considering economic factors and biomedical safety. Additionally, the article introduces realistic and pertinent assumptions that have not been explored in previous literature.

Risk-averse distributionally robust optimization for construction waste reverse logistics with a joint chance constraint

The uncertainty of the amount of construction and demolition waste (CDW) generation affects the CDW reverse logistics network service level. We investigate a CDW reverse logistics network location-routing problem considering uncertainties. To minimize the total social cost, a two-stage risk-averse distributionally robust optimization model is developed, which aims to optimize the location and number of CDW disposal facilities and the CDW transportation scheme. We introduce the mean-conditional value at risk measure and a joint chance constraint into our model to consider the government’s risk aversion. The above model is approximated as a standard second-order cone programming model (SOCP) considering a special case. To exactly solve the SOCP, we design an outer approximation algorithm. Several performance tests and a case study are proposed, and sensitivity analysis provides helpful managerial insights.

Designing green logistics networks under carbon tax policy: Post-COVID condition

This paper proposes a fuzzy-based method for estimating demand in forward and reverse logistics networks (F&RLNs) subject to carbon pricing in the post-COVID era. We investigate the complex relationships between carbon prices, the reverse logistics network (RLN), and the ever-changing product demand by utilizing fuzzy inference systems (FIS) capabilities. The case study from Mexico confirms the effectiveness of the proposed technique. The findings demonstrate the precision and predictive power of our proposed FIS-based method and show how well it can predict the number of items in demand and the number of goods that will be returned and subject to carbon taxation in the post-COVID era. The results illustrate the significant impact that carbon pricing has on the RLN and the associated product demand. For logistics managers seeking to make informed decisions about the establishment and operation of forward and RLNs within the carbon pricing paradigm, this empirical data provides insightful information in the post-COVID era.

Optimization of reverse logistics network for end-of-life vehicles: A Shanghai case study

With the surge in car ownership, the end-of-life vehicles recycling market has shown enormous development potential. As the reverse logistics network for recycling end-of-life vehicles suffers from high operating costs and low recycling rates, there is an urgent need to upgrade the actual recycling measures for end-of-life vehicles to an operable level. This article first uses the OGM (1, N) model to predict the number of end-of-life vehicles in the coming years. At the same time, a reverse logistics network model with the second-hand car market as the recycling centre was constructed with the goal of minimizing the total cost of the end-of-life vehicles reverse logistics network. The network model was simulated using mixed integer programming (MILP), and the optimal solution was solved through LINGO 12 programming. Through an example analysis of Shanghai, it is found that the market of end-of-life vehicles will embrace growth, and it is verified that the optimized reverse logistics network can effectively reduce the operation cost and logistics cost of recycling centres, and can effectively improve the actual recycling rate of end-of-life vehicles. Finally, the optimized site selection results are obtained, and a specific traffic distribution scheme is proposed, which is crucial for promoting cars that meet scrap standards to be recycled through formal channels and reducing logistics costs for recycling enterprises.

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.

Study on Reverse Logistics System Issues

In this information age, competition is fierce in all sectors, and the logistics industry is no exception. Numerous enterprises enjoy the great benefits brought by the advanced logistics system, and at the same time must respond to the call of the times, the sustainable development strategy and green logistics is imperative. Therefore, the establishment of a reasonable reverse logistics system is able to reduce the economic development of the ecological environment brought about by pollution, and effectively solve the rapid economic development and environmental protection issues between the special contradictions, but also appropriate to reduce the pressure on the supply of resources in the community, while easing the constraints on the contradiction of resources. The construction of low-cost, high-efficiency reverse logistics network can not only greatly enhance the efficiency of the logistics chain but also promote the synchronized and coordinated sustainable development of society, economy and environment.

Can an Industry-Led infrastructure development strategy facilitate electric truck Adoption?

Reverse logistics plays a critical role in sustainable development and circular economy by transforming waste into value. However, transporting large volumes of waste and recyclables generates significant carbon emissions. This paper explores an industry-led strategy for charging infrastructure development, which integrates the reverse logistics network designs with a network of charging stations to promote electric trucks (E-trucks) adoption and reduce carbon footprints in transportation. Our model optimizes operational costs while ensuring accessibility to charging infrastructure. A case study in Norway demonstrates that, despite higher operating costs due to additional charging stations, the adoption of E-trucks can decrease total reverse logistics costs by 0.18%—2.37% and cut carbon emissions by 16.3%—17.6%. These findings support the economic viability and environmental benefits of this strategy for advancing sustainable practices in transportation, and it further highlights the interdependencies between logistics network design and charging infrastructure planning.

Design of Reverse Logistics Network for Power Battery Considering Transportation Risk

Design of Reverse Logistics Network for Power Battery Considering Transportation Risk

Blockchain in reverse logistics for solid waste management: a proposal

Despite the claim that blockchain will revolutionize business, existing research so far is limited regarding frameworks that categorize blockchain application potentials and their implications. In particular, the academic literature is not well-defined on how to adopt this technology for reverse logistics management. In response, this article uses a brief review of the scientific literature and the fundamentals of blockchain technology to discuss about the benefits of implementation of this tool in a reverse logistics flow to obtain greater control and security between the actors of the reverse logistics network in all material collected and directed. Five actors were listed: industry, transport, traders, waste picker cooperative, and recycler. Develop and structure a RL management model, making use of blockchain technology, has potential to enable greater transparency of the tailing’s life cycle, increasing the traceability and reliability of the data obtained, and providing lower costs related to the monitoring of disposal.

Developing an incentive‐based model for efficient product recovery and reverse logistics

AbstractThis study presents an inventive model aligning with sustainable development goals (SDGs) 11, 12, and 9. SDG 11 emphasizes sustainable urban aspects, SDG 12 centers on responsible consumption, and SDG 9 highlights resilient infrastructure. Focused on enhancing operational profits, the model integrates a robust reverse logistics network and policy framework to ensure safe disposal and environmental preservation. Employing the CPLEX solver software, we evaluated various methodologies, including proximity‐based allocation, set covering problems, p‐median allocation, and capacity‐relaxed models, to maximize profitability and efficiency in battery return systems. Our findings underscored the limitations of conventional proximity‐based methods, emphasizing the necessity of advanced optimization. Scenario 3, utilizing the p‐median problem, emerged as the most profitable, optimizing customer allocation and reducing distance‐related costs. Additionally, our sensitivity analysis highlighted the collection rate parameter's pivotal role in influencing customer behavior and overall system profitability. The study also emphasizes the significance of accessible collection centers, revealing disparities in accessibility across customer zones. These findings call for nuanced analyses to ensure equitable access. Implications include advocating for strategic policies to enhance collection rates, optimize center accessibility, and promote responsible disposal, benefiting policymakers, industry professionals, and environmental stakeholders. Ultimately, this research contributes to sustainable practices, fostering eco‐conscious societies, and accelerating progress toward SDGs.

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.

Mixed-integer programming models for a multi-period integrated reverse logistics network with multiple recovery options and different quality levels of returns

This paper addresses an integrated reverse logistics (RL) model with a production route and multiple recovery options, including repair, refurbishing, remanufacturing, recycling, and selling spare components. We aim to use the quality grading system method to analyse returns comprehensively. In this regard, quality levels are used to classify returns and quality thresholds are utilised to categorise them into certain quality groups. The proposed model deals with different activities such as locating, purchasing, transporting, processing, inventory holding, recovering, and production in a planning horizon with multiple periods. A two-stage optimisation model is proposed to facilitate the decision-making process. In Stage 1, a mixed integer linear programming (MILP) model is proposed to minimise the costs and determine the number of returned products directed into different recovery routes. In Stage 2, another MILP is proposed to maximise the network’s profit. Furthermore, 350 scenarios are defined to examine the network’s performance and decide the optimal quality thresholds. Finally, the proposed models are applied to a real dataset and solved by the CPLEX solver through Pyhton. According to the results, scenarios that use multiple recovery options earn higher profits and lower costs as most demands are satisfied and the stock level of inventories decreases.

Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

Optimizing sustainable reverse logistic networks: a case study of medical waste using the genetic artificial bee colony algorithm

Toward a better understanding of China's e-waste reverse logistics implementation barriers: A certified disassembler's perspective

E-waste, containing valuable materials and hazardous substances, poses a significant challenge to the environment and society. One approach to addressing this challenge is the implementation of a reverse logistics network to recover e-waste, which involves a disassembler connecting the upstream collector with the downstream recycler and disposer. In China, a certified disassembler in a subsidy scheme plays a crucial role in e-waste recycling effectiveness. However, a declining recycling rate and high idle capacity have led to a ‘certified disassembler dilemma’. This study uses stakeholder analysis processes to identify barriers to e-waste reverse logistics, and the grey-based decision-making trial and evaluation laboratory approach is utilized to explore their causal relationships. The results, supported by a sensitivity analysis, indicate that the certified disassemblers' internal capability deficiencies form the most significant causal barrier, with a net cause value of 2.74. In contrast, the increased number of illegal disassemblers is the primary determinant of all other barriers, with a prominence value of 19.5. Recommendations for improving certified disassemblers' internal capabilities are provided, including exploring the deep processing of waste materials, extending the residential collection network, and reassessing multiple e-waste recovery options.

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.