End-of-life Products Research Articles

Modeling and identification of suitable motivational mechanism in the collection system of municipal solid waste supply chain.

Many studies have identified that incentive, subsidy, and reward-penalty mechanisms improve the collection rate of recyclables and end of life products. But there is a lack of studies mathematical models and analysis of these mechanisms in the context of municipal solid waste supply chain. Therefore, in this study, models have been formulated for municipal solid waste supply chain (profit) considering government and collectors' profit under incentive, subsidy, and reward-penalty mechanisms. The study has analysed the models against the non-separation and separation scenario of waste. A numerical analysis is performed and observed that: (i) separation of waste at source along with incentive, subsidy, and reward-penalty mechanisms scenario improve the collection rate by 17%, 23%, 30%, and 45% compared to non-separated MSW. (ii) Incentive, subsidy, and reward-penalty mechanisms increases the total supply chain profit by around 9%, -36% and 18%. (iii) reward-penalty mechanism performs better than incentive and subsidy mechanism by providing the high supply chain profit (18% and 85%) and collection rate (22% and 15%) comparatively. Further, sensitivity analysis carried out to understand the behaviour of the models against the key parameters. The study also develops interesting propositions and proved for a better understanding of the models. From results, some key managerial insights have been drawn and a few future scopes of the study are presented.

Structural reuse of wind turbine blades through segmentation

Composite materials offer many advantages during the use phase, but recovery at the end of a lifecycle remains a challenge. Structural reuse, where an end of life product is segmented into construction elements, may be a promising alternative. However, composites are often used in large, complex shaped products with optimised material compositions that complicate reuse. A systematic approach is needed to address these challenges and the scale of processing. We investigated structural reuse taking wind turbine blades as a case product. A new segmentation approach was developed and applied to a reference blade model. The recovered construction elements were found to comply to geometric construction standards and to outperform conventional construction materials on specific flexural stiffness and flexural strength. Finally, we explored the reuse of these construction elements in practice. Together, the segmentation approach, structural analysis and practical application provide insights into design aspects that enable structural reuse.

Energy-Efficient Robotic Parallel Disassembly Sequence Planning for End-of-Life Products

End-of-life (EOL) products contain many valuable parts and materials. The timely disassembly and recycling of EOL products can bring considerable economic benefits and reduce environmental pollution. To reduce the cost of manual disassembly, robotic disassembly has become one of the main methods used to dismantle EOL products. In addition, parallel disassembly can shorten the makespan of EOL products. Therefore, this article focuses on a new problem involving robotic parallel disassembly sequence planning for EOL products and establishes a corresponding multiobjective model to minimize the makespan and energy consumption. To obtain high-quality disassembly schemes, a discrete artificial bee colony algorithm based on problem characteristics is proposed. The performance of the proposed algorithm is verified by solving 32 benchmark problems and a comparison with three well-known multiobjective algorithms. The proposed model and method are applied to a real-world LCD TV disassembly case, and multiple preferred disassembly schemes are obtained. The results show that the proposed model and method can effectively shorten the makespan and reduce energy consumption. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Parallel disassembly is one of the most efficient disassembly methods, and robotic disassembly will be one of the main disassembly methods in the future. Therefore, this article focuses on the robotic parallel disassembly planning to achieve an efficient and environmentally friendly treatment of end-of-life products. With the importance of production efficiency and energy saving, it is essential to simultaneously assess the makespan and energy consumption. The results demonstrate that the proposed artificial bee colony algorithm can provide multiple disassembly schemes that balance the makespan and energy consumption. The obtained disassembly schemes can provide references for the disassembly planning of the disassembly enterprise and expand the decision-making space for decision-makers.

A single-step process to leach out rare earth elements from coal ash using organic carboxylic acids

The ever increasing applications of rare earth elements and their limited primary resources has compelled researchers worldwide to find alternate methods of efficient leaching from various secondary resources, in particular from end of life products. The present research work describes such a process to extract rare earth elements from coal ash using low molecular weight carboxylic acids. Different carboxylic acids, i.e. monocarboxylic (lactic acid), dicarboxylic (malonic acid, succinic acid, tartaric acid) and tricarboxylic acid (citric acid) were used to successfully leach out significant proportion of rare earth elements (up to 62%) from coal ash at the natural pH of the carboxylic acid solutions (pH 1.6–2.3). The carboxylic acid concentration, leaching duration, temperature, and pulp density were optimized for the leaching process. Best leaching efficiency (65% for LREE, 19% for HREE & 62% for total REE) was achieved using 5% tartaric acid solution having natural solution pH 1.8 at 90 °C with a leaching duration of 60 min. The leaching efficiency of the different carboxylic acids were in the order of tartaric acid>lactic acid>citric acid>malonic acid>succinic acid. The leaching kinetics of individual rare earth elements was found to be different from each other owing to their different complex formation ability with carboxylic acids. Appreciable selectivity was observed for leaching of rare earth elements over transition metal and main group elements. XRD and electron probe micro analyzer (EPMA) studies revealed that unlike mineral acids, carboxylic acids leached out rare earth elements without damaging the minerology of coal ash, thus providing the opportunity to utilize the treated coal ash for other applications like in cement and bricks industries. Thus, the novelty of the present work lies in efficient and selective leaching of lanthanides from coal ash using only organic acids, which are considered much milder and greener options compared to mineral acids.

Gut Microbial Composition Differs Extensively among Indian Native Chicken Breeds Originated in Different Geographical Locations and a Commercial Broiler Line, but Breed-Specific, as Well as Across-Breed Core Microbiomes, Are Found

Gut microbiota plays an important role in the health and performance of the host. Characterizations of gut microbiota, core microbiomes, and microbial networks in different chicken breeds are expected to provide clues for pathogen exclusion, improving performance or feed efficiency. Here, we characterized the gut microbiota of “finishing” chickens (at the end of production life) of indigenous Indian Nicobari, Ghagus, and Aseel breeds, originating from the Nicobari island, coastal India, and the Indian mainland, respectively, as well as a global commercial broiler line, VenCobb 400, using 16S rDNA amplicon sequencing. We found that diversity, as well as richness of microbiota, was higher in indigenous breeds than in the broiler line. Beta diversity analysis indicated the highest overlap between Ghagus and Nicobari breeds and a very low overlap between the broiler line and all indigenous breeds. Linear discriminant analysis effect size (LEfSe) revealed 82 breed- or line-specific phylotype operational taxonomic unit (OTU) level biomarkers. We confirm the presence of breed specific and across-breed core microbiomes. Additionally, we show the existence of breed specific complex microbial networks in all groups. This study provides the first (and comprehensive) insight into the gut microbiota of three indigenous breeds and one commercial broiler line of chickens reared without antimicrobials, and underscores the need to study microbial diversity in other indigenous breeds.

Reverse design for remanufacture based on failure feedback and polychromatic sets

Design for remanufacture (DfRem) plays an important role in improving remanufacture efficiency of end-of-life (EOL) products. Some studies revealed that the barriers of DfRem are mostly hidden in the failure modes of EOL product. However, one of the DfRem challenges is how to integrate failure feedback into the product’s design process. To address this problem, this study offers a novel Reverse Design for Remanufacture (RDfRem) method to offer redesign strategy for improving the remanufacturability of product based on failure feedback and polychromatic sets. An integrated model, namely, Reverse DfRem Polychromatic Model (RDPM), was developed to describe the relationship among the product’s components, failure modes, scrap modes, economic and obsolescence criteria, and EOL product recovery options based on the theory of polychromatic sets. Thus, the DfRem of product could be formally described mathematically and manipulated by using the computer system. Furthermore, a redesign proposal polychromatic reasoning method was designed to identify the proposed architecture of product. Then redesign proposals were provided to improve the remanufacturability of product. Finally, a simple example was considered to illustrate the entire framework of the proposed method and the case study of an engine validated the feasibility of the method proposed in this study.

Multiverse Optimization Algorithm for Stochastic Biobjective Disassembly Sequence Planning Subject to Operation Failures

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products’ exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.

Design of reverse supply chains under uncertainty: the lexicographic R * criterion for exploring opportunities

Reverse supply chains (RSCs) have been increasingly implemented in recent years to manage the growing flow of solid waste generated by end-of-life (EOL) products and to minimise their environmental impact. Furthermore, recent research has shown that the implementation of RSCs benefits job creation, enables savings in raw materials, and creates income from the sales of re-manufactured products. However, designing RSCs requires dealing with many sources of uncertaintydue to the reverse flow of EOL products. To model these uncertainties, we consider a set of equally possible scenarios. In previous models for scenario sets, the decision often has been influenced by negative scenarios while neglecting opportunities. We propose a new risk/opportunity approach based on the criterion to give more weight to positive scenarios in the decision-making process. This criterion is used in order to distinguish zones of risk and opportunity and guide the decision-making process accordingly to the existing zones. We develop a lexicographic approach for the consideration of existing scenarios, and propose two methods to compute the optimal solution for lexicographic () criterion: one in the form of an algorithm and another in the form of a mixed-integer program (MIP). The performance of the developed approaches is demonstrated in a case study for a reverse facility location problem.

Novel distribution-free model and method for stochastic disassembly line balancing with limited distributional information

Recycling of end-of-life (EOL) products has drawn much attention from both researchers and practitioners over the recent decades due to the environmental protection, sustainable development and economic benefits. For an EOL product recycling system, a core problem is to separate their useful and hazardous parts by an efficient disassembly line in which there exist uncertain factors, such as stochastic task processing time. The corresponding combinatorial optimization problems aim to optimally choose alternative task processes, determine the number of workstations to be opened, and assign the disassembly tasks to the opened workstations. In most existing studies, the probability distribution of task processing time is assumed to be known. However, the complete information of probability distribution is often unavailable due to various factors. In this study, we address a disassembly line balancing problem to minimize the total disassembly cost in which only limited information of probability distribution, i.e., the mean, lower and upper bounds of task processing time, is known. Based on problem analysis, some properties are derived for the construction of a new distribution-free model. Furthermore, an effective second-order cone program approximation-based method is developed to solve the proposed model. Experimental results of benchmark examples and newly generated instances demonstrate the effectiveness and efficiency of the proposed method in dealing with stochastic disassembly line balancing with limited distributional information. Finally, managerial insights and future research are discussed.

Consignment stock policy in a closed-loop supply chain

The main feature of sustainable supply Chain Management is reuse of end of life (EOL) products to reduce the environmental pollution. This paper considers the consignment stock (CS) policy for a closed-loop supply chain (CLSC). To achieve the economic goal, this study presents a multi-echelon supply chain with a single manufacturer, a single retailer and a third-party service provider. The objective is to optimize this three-level CLSC under a Stackelberg game scenario. The other objective is to identify the environmental impact of remanufacturing EOL products. In this CLSC, the manufacturer acts as the Stackelberg leader and the retailer, the third party are the followers. Under manufacturer’s leadership, the retailer and the third party acquire three different decision strategies – I, II, and III. In decision strategy I, the retailer acts as the leader and the third party acts as a follower, whereas in decision strategy II, they obtain a Nash game strategy. In the decision strategy III, they act as one unit and decide their decisions centrally. Numerical example and sensitivity analysis are used to illustrate optimal results of different decision strategies and also check the behaviour of key model-parameters. The best outcomes are found in decision strategy I. The main findings of our study show that when upper level player has more power then the supply chain gives best outcomes.

Potential for Nd and Dy Recovery from End-of-Life Products to Meet Future Electric Vehicle Demand in the U.S.

Rare earth elements (REEs) such as Neodymium (Nd) and Dysprosium (Dy) are used in Rare Earth Permanent Magnets (REPMs). Neodymium-iron-boron (NdFeB) magnets offer the strongest magnetic field per volume among all the magnets available in the market, which enables lightweight and compact product designs. NdFeB magnets have thus become indispensable in emerging clean technologies such as wind turbines and electric vehicles (EVs). Global demand for these renewables is increasing due to environmental considerations, energy security, and consumer demand. However, China’s dominance in REE production and processing technology has led to concerns pertaining to supply chain risks. This is especially the case for the automotive industry that is experiencing a major shift from traditional internal combustion engines to EVs that employ these REPMs. The number of EVs on U.S. roads is projected to reach more than 18 million in 2030 up from 1.4 million in 2019. In order to mitigate supply chain brittleness, and thereby potentially reduce price volatility, secondary REE supply sources from end-of-life (EoL) products may be a viable option. This study quantifies the recycling potential of Nd and Dy from EoL EVs, HDDs and MRIs in the U.S. Factors used in the analysis were number of units manufactured each year, NdFeB magnet mass in each unit and REE composition of magnets employed. Using EV production forecasts, we estimate how much of the future Nd and Dy demands can be met by secondary supply from EoL products.

Balancing Stochastic Mixed-Model Two-Sided Disassembly Line Using Multiobjective Genetic Flatworm Algorithm

The disassembly industry is still labor-intensive, and for the disassembly of products with similar assembly structures, achieving mixed disassembly of these products on the same disassembly line is a more economical solution and very popular in disassembly companies. When large-volume end-of-life (EOL) products are disassembled on the two-sided disassembly line, there is uncertainty in the disassembly time during the disassembly process due to the differences in the recovery quality of EOL products. Therefore, a stochastic mixed-integer programming model is formulated that considers workstation activation cost, workload smoothness index between workstations, and total hazard index considering the order of hazardous task removal. Then, a multi-objective genetic flatworm algorithm is developed in which two different mechanisms for new individual generation are embedded, namely the flatworm genetic operations and the regeneration operations. The effectiveness of the model and the efficiency of the algorithm are verified by the disassembly cases of the printer and the classical mixed-model on the two-sided disassembly line. Finally, the proposed model and algorithm are applied to the disassembly of multiple types of cars, which proves their practical industrial application value.

A model for the economic assessment of disassembly-line integration in traditional manufacturing processes

Abstract Managing End-of-Life (EoL) products and reintroducing materials and components within the production loop become crucial for guaranteeing the Circular Economy business model. In such a way, the proper management of disassembly process for recovering components and materials from returned EoL products is essential as well as strategic: disassembly is the main gateway of information and can ensure economic returns. This paper aims to provide a model for the economic assessment of the introduction of a manual disassembly line in a traditional and already operating assembly line of manufacturing industries. Therefore, recovered components and materials could directly feed the assembly lines and the recycling processes. The model takes in input probabilistic factors, as products’ characteristics, and provides the operating times and component recovery indicators, as well as allows the sizing of the right number of operators needed in the new disassembly line through the optimisation of the industrial cost. An interesting natural evolution of this study is the development of a model-based simulator, with the aim of providing a user-friendly tool to industrial practitioners to estimate the economic feasibility and convenience of introducing a disassembly line.

An attempt to create the holistic flow chart of forest resources

The environmental development of a building has an important role to play in terms of the sustainable development of our society. Against this background, wood and wood-based products have lately attracted notable attention as promising construction materials due to then-unique environmental properties, such as renewability and carbon storage capacity. For the sustainable use of wood, a relevant balance between supply and demand of forest resources is significant. However, little work has been conducted on a comprehensive understanding of forest resources flow. The objective of this study was. therefore, to draw an integrated chart of forest resources flow including forest conditions, round wood production, wood product manufacturing, wood stock in buildings and waste management at the end of product life, mainly focusing on building material flow. The flow charts were drawn for five countries (Japan. Finland. Germany, Sweden and Canada) based on statistical data. Instead of directly showing numerical information, the chart is presented as an infographic in order to promote intuitive understanding of the contents. The chart is intended as a basis for the development of sustainable forest resource circulation. This paper focuses on introducing the chart as such ad briefly discusses how the chart can be utilized for further study in relation with SDGs.

Evaluating economic opportunities for product recycling via the Sherwood principle and machine learning

In a circular economy, recovering materials from end of life (EoL) products without an appropriate estimation of the cost and revenue may result in unnecessary economic loss. Although research has been carried out on the economic analysis of value recovery from EoL products, few quantitative approaches have examined generalized revenue-cost models. To fill this gap, this study proposes an empirical model to identify economic feasibility of product recycling by integrating machine learning and the Sherwood principle. A boundary that differentiates profitable and non-profitable EoL products for material recycling is quantified and visualized. It is envisioned that the proposed method can be adopted in other circular economy domains for better decision-making.

Sustainable Food Packaging: Materials and Waste Management Solutions

The current food packaging model in most cases is a linear material flow model and is far from the sustainable alternative – circular economy – approach where materials are recycled and recovered at the end of each service life. High concern is rising on packaging waste and especially plastic packaging and negative environmental impact. A number of factors, including policy and legislative changes, rising concerns on food and packaging waste, environmental contamination, and world demand for food and energy resources, undoubtedly make an impact on development of biodegradable and compostable packaging made from renewable environment friendly resources and a sustainable waste management opportunity at the end of product life. Food packaging industry already has options of compostable packaging that meets biodegradation and composting standards and does not impact environmental contamination, but a variety of existing bio-labels such as biobased, biodegradable, and compostable appear misleading for consumers, and terms biodegradable and compostable are often used as synonyms, although they are not the same.

Exploring the opportunities in establishing a closed-loop supply chain under uncertainty

ABSTRACT Reverse supply chains (RSC) may provide the benefits of reducing pollution, creating new jobs, and generating income from the recyclable materials. However, their implementation comes with risks and hardly predictable outcomes. The model presented in this paper aims to help managers to better evaluate risks and opportunities while deciding on the RSC design to manage the reverse flow of end-of-life (EOL) products in an existing supply chain. The goal is to set up the disassembly and recovery facilities and organize the flows between them while maximizing total network profit. We propose a two-stage multi-period mixed-integer program where the budget available for decisions at each period depends on the outcomes of previous periods. The demand for EOL products, the quantity of products returned and the time required to reprocess these products are considered uncertain. To incorporate this uncertainty into the decision making process, a discrete set of scenarios is defined. To take into account the decision maker's behavior in the areas of risks and opportunities, we propose to use criterion to select the final solution. To demonstrate the relevance of criterion, we conduct numerical investigations on an adapted case study from the literature and do a comparison with classic well-known criteria.

Stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling via fruit fly optimization algorithm

Remanufacturing end-of-life (EOL) products is an important approach to yield great economic and environmental benefits. A remanufacturing process usually contains three shops, i.e., disassembly, processing and assembly shops. EOL products are dissembled into multiple components in a disassembly shop. Reusable components are reprocessed in a processing shop, and reassembled into their corresponding products in an assembly shop. To realize an overall optimization, we have to integrate them together when making decisions. In practice, a decision-maker usually has to optimize multiple criteria such as cost-related and service-oriented objectives. Additionally, we cannot accurately acquire the detail of EOL products due to their various usage processes. Therefore, multi-objective and uncertainty need to be considered simultaneously in an integrated disassembly-reprocessing-reassembly scheduling process. This work investigates a stochastic multi-objective integrated disassembly-reprocessing-reassembly scheduling problem to achieve the expected makespan and total tardiness minimization. To handle this problem, this work develops a multi-objective discrete fruit fly optimization algorithm incorporating a stochastic simulation approach. Its search techniques are designed according to this problem’s features from five aspects, i.e., solution representation, heuristic decoding rules, smell-searching, vision-searching, and genetic-searching. Simulation experiments are conducted by adopting twenty-five instances to verify the performance of the proposed approach. Nondominated sorting genetic algorithm II, bi-objective multi-start simulated annealing method, and hybrid multi-objective discrete artificial bee colony are chosen for comparisons. By analyzing the results with three performance metrics, we can find that the proposed approach performs better on all the twenty-five instances than its peers. Specifically, it outperforms them by 6.45%–9.82%, 6.91%–17.64% and 1.19%–2.76% in terms of performance, respectively. The results confirm that the proposed approach can effectively and efficiently tackle the investigated problem.

Balancing of two-sided disassembly lines: Problem definition, MILP model and genetic algorithm approach

The recovery of end of life (EOL) products has become an important issue in terms of economic as well as social and environmental considerations. Recent rigid environmental regulations also contribute to the popularity of disassembly and product recovery topics among academicians and practitioners. Disassembly lines have been utilised to break EOL products into pieces and remove parts which can be reused in the manufacturing of new products. However, to the best of the authors’ knowledge, there is no research on the two-sided disassembly lines, which are used for disassembly of large-sized products. Therefore, this research contributes to literature by introducing the two-sided disassembly line balancing problem (TDLBP) and modelling it mathematically for the first time. The problem is depicted and the challenges are explored through extensive numerical examples. Secondly, a powerful genetic algorithm approach, called 2-GA, is developed for solving the introduced TDLBP considering complex AND/OR precedence relations. Computational tests are conducted to test the performance of the proposed 2-GA and the results are compared to those obtained from CPLEX and tabu search algorithm. From the comparison of the obtained solutions, it can be concluded that 2-GA has a superior performance in finding optimal (or at least near-optimal) solutions usually within less than one second.

Impact of Generational Commonality of Short Life Cycle Products in Manufacturing and Remanufacturing Processes

Abstract Short life cycle products are frequently replaced and discarded, even though they are resource-intensive products. Technological advances and rapid changes in demand have led manufacturers to develop their innovative next-generation products quickly, which not only enables multiple generations to coexist in the market but also speeds up the technological obsolescence of products. Diversity of collected end-of-life (EoL) and rapid technological obsolescence make the effective recovery of EoL products difficult. The low utilization rate of EoL products causes serious environmental problems such as e-waste and waste of natural resources. To deal with the conflict between the technical evolution of products and the promotion of social benefits in solving environmental problems, this paper focuses on the impact of generational commonality effects on the overall production process including manufacturing and remanufacturing. Generational commonality leads to an increase in the efficiency of manufacturing due to reducing related costs. Additionally, from the remanufacturing perspective, the interchangeability between generations can help collect the EoL products needed for remanufacturing. On the other hand, it causes a weakening of the level of performance and technology evolution between generations that significantly affect the demand for short life cycle products. Therefore, this study identifies these trade-offs of generational commonality levels in both manufacturing and remanufacturing based on a quantitative approach. This study finds how different pricing strategies, production plans, and recovery costs are based on the designs of a new generation with a different degree of generational commonality.