End-of-life Products Research Articles

An integrated approach for a new flexible multi-product disassembly line balancing problem

Flexible disassembly line design for end-of-life (EOL) products is a key issue in the remanufacturing industry. However, existing studies for disassembly line balancing have not simultaneously considered multiple EOL products, the identical parts of these products and uncertainty during disassembly, which are important characteristics of flexible disassembly lines. The present study addresses a new flexible multi-product disassembly line balancing problem in which (1) disassembly schemes need to be selected, (2) a workstation can disassemble multiple EOL products, (3) identical parts of multiple products can be treated as identical tasks, and (4) only partial probability distribution information of processing times is known. For the problem, an integrated approach is developed, which is composed of a chance-constrained program, a distribution-free model, efficient valid inequalities and an exact lifted cut-and-solve method. Numerical experiments are conducted on an illustrative example, 10 instances based on realistic products and 480 randomly generated instances with up to 20 products, 400 tasks and 86 workstations. Computational results show that the proposed valid inequalities can reduce about 75% computational time of the original model, and the lifted cut-and-solve method needs only 17.53% and 40.65% of the computational times required by the CPLEX and the classic cut-and-solve method, respectively.

Stochastic Hybrid Discrete Grey Wolf Optimizer for Multi-Objective Disassembly Sequencing and Line Balancing Planning in Disassembling Multiple Products

Recycling, reusing, and remanufacturing of end-of-life (EOL) products have been receiving increasing attention. They effectively preserve the ecological environment and promote the development of economy. Disassembly sequencing and line balancing problems are indispensable to recycling and remanufacturing EOL products. A set of subassemblies can be obtained by disassembling an EOL product. In practice, there are many different types of EOL products that can be disassembled on a disassembly line, and a high-level uncertainty exists in the disassembly process of those EOL products. Hence, this paper proposes a stochastic multi-product multi-objective disassembly-sequencing-line-balancing problem aiming at maximizing disassembly profit and minimizing energy consumption and carbon emission. A simulated annealing and multi-objective discrete grey wolf optimizer with a stochastic simulation approach is proposed. Furthermore, real cases are used to examine the efficiency and feasibility of the proposed algorithm. Comparisons with multi-objective discrete grey wolf optimization, non-dominated sorting genetic algorithm II, Multi-population multi-objective evolutionary algorithm, and multi-objective evolutionary algorithm demonstrate the superiority of the proposed approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Disassembly line balancing has been widely recognized as the most ecological way of retrieving EOL products. Through in-depth research, we present a Stochastic Multi-product Multi-objective Disassembly-sequencing-line-balancing Problem. Furthermore, we consider that the uncertainty of products might cause disassembly failure. To solve this problem effectively and quickly, we combine the simulated annealing algorithm with the Grey Wolf Optimizer. The results show that the algorithm can effectively solve the proposed problem. The disassembly scheme provided by the obtained solution set offers a variety of options for decision-makers.

Leaching characteristics of rare earth elements from coal ash using organosulphonic acids

Extraction of valuable REEs from end of life products like coal ash is a viral topic. The potency of organosulphonic acids in leaching out REEs from coal ash matrix has not been exploited till date. In the present study, the leaching potential of methanesulphonic acid and p-toluenesulphonic acid was compared towards extraction of REEs from coal ash. The optimized conditions for extraction were determined to be at a temperature of 90 °C, for duration of 60 min in a liquid to solid ratio 100 at stirring speed of 600 rpm. At 0.5 M acid concentration, LREE was leached out in about 65–70% yield whereas HREEs could be only extracted in about 15–20% yield. XRD diffraction studies revealed that the leaching with organosulphonic acids did not destroy the aluminosilicate matrix of coal ash. MSA imparted good selectivity in leaching, wherein, it leached out 70% of REE along with minimal quantities of other coal ash elements (<10%). The leaching kinetics studies under varying temperature concluded the process to be diffusion controlled, following the shrinking core model. The leaching potency of organosulphonic acid was found to be at par with mineral acids (HNO3 and HCl), and hence it could be considered as a green alternative to mineral acids which are conventionally widely used in REE extraction from coal ash wastes.

Novel ICT System for Recycling and Eco-Shopping

Recently, there has been a growing effort to reduce the environmental impact of products throughout their life cycle, particularly during the end-of-life (EoL) stage. To incentivise consumers’ recycling/reusing behaviours and enhance their environmental awareness, a novel ICT-based system for recycling and eco-shopping has been developed in this paper. The recycling of EoL products is conducted based on information-communication technologies to remotely monitor and manage the recycled products (such as electronics or household bio-wastes), enabling consumers’ recycling process over the Internet. Consumers are awarded the eco-credits, which can be used for various forms of eco-incentives, such as shopping discounts, tree planting donations, and exchanges for theatre and museum tickets. The eco-costs reflect the environmental impact of a product throughout its life cycle. The consumer is informed about the eco-costs through eco-shopping, which are displayed on a payment receipt. Both eco-costs and eco-credits are recorded in the consumer’s eco-account. To develop the recycling and eco-shopping system, multiple information-communication technologies are utilised, such as hardware digital monitoring/control, Internet-based communication services, traceability media (bar-code and QR code), user identity recognition and privacy protection, and multi-language supports. A case study is conducted, including online tracking of the recycling process and then implementing incentive activities with the eco-credits and eco-costs. The system has been successfully validated via illustrating recycling, eco-shopping, and eco-incentives in public places (e.g., schools, urban cultural centres), as well as promoting the consumer’s participation in recycling and enhancing their environmental awareness, which proved the successful implementation of the novel contribution of this research.

Design and qualification of a bench-scale model for municipal waste-to-energy combustion

ABSTRACT This paper reports the design and qualification of the first purpose-built, bench-scale reactor system to model the municipal waste-to-energy combustion of fluorinated polymers. Using the principle of similarity, the gas-phase combustion zone of a typical municipal waste-to-energy plant has been scaled down to the bench with a focus on chemical similarity. Chemical similarity is achieved in large part through the use of methanol as a surrogate for municipal solid waste (MSW). Review of prior research shows that methanol is one of the major volatile products expected during MSW thermal conversion in the fuel bed of waste-to-energy plants. Like full-scale waste-energy plants, the design of the bench-scale model includes a flame zone and a post-flame zone. Maintaining steady methanol vapor flow premixed with air to the model reactor system ensures stable combustion resulting in bench-scale CO emission levels comparable to those of full-scale waste-to-energy plants. Since investigation of fluorinated polymer combustion includes trace analysis of exhaust gas for perfluorooctanoic acid (PFOA), qualification testing focused on PFOA collection efficiency. High PFOA collection efficiency (>90%) demonstrated the capability of the reactor system in transporting and absorbing PFOA that may be generated during high-temperature combustion testing of fluorinated polymers. Overall, the bench-scale system is qualified for its intended use to investigate potential generation of PFOA from combustion of fluorinated polymers under conditions representative of waste-to-energy combustion. Implications: Decision makers depend on environmental researchers to provide reliable predictions of pollutant emissions from waste combustion of polymers at end of product life. Reliable predictions are especially important with regard to questions about potential PFOA emissions from municipal waste combustion of fluorinated polymers. Results from qualification testing confirm that the novel bench-scale model reactor system is capable of representing gas-phase municipal waste combustion behavior upstream of air pollution control and generating representative exhaust gas samples for off-line trace-level analysis of PFOA.

An approach to evaluate the impact of the introduction of a disassembly line in traditional manufacturing systems

Purpose: The circular economy (CE) paradigm, traditionally based on the 3R (reuse, recycle, and remanufacture) principles, provides benefits for sustainability and represents a big opportunity for manufacturing enterprises to reduce costs and take economic advantages. This paper proposes an approach that can help stakeholders transition towards CE oriented business by evaluating the economic convenience of introducing a manual disassembly line to recover the components of End-of-Life (EoL) products in a traditional manufacturing system.Design/methodology/approach: The conceptual approach is generic and based on the characteristics of EoL products and on the reusability and recyclability features of every component. Then, based on the type of product and the disassembly sequence, the disassembly line is built in the virtual environment along the assembly line. The virtual environment must take into account the probabilistic parameters that characterise each real industrial context. Therefore, the assembly-disassembly lines are linked with the variables and economic functions needed to process the outputs of the approach application.Findings: Implemented in a virtual environment, the proposed approach evaluates a priori possible economic and environmental benefits coming from the integration of a disassembly line within a manufacturing context. The approach considers the variability of the EoL products’ status (their reusability and recyclability indices), provides the optimal number of operators that must be assigned to the manual disassembly line and determines the maximum reduction of the product cost that can be gained by introducing the disassembly line. Furthermore, an application example is provided to show the potential of the tool.Originality/value: Recently, the scientific literature has dealt with the issue related to the disassembly process of EoL products from several perspectives (e.g. disassembly line scheduling, planning, balancing, with and without the consideration of the quality of EoL products). However, to the best of our knowledge, no study provided an approach to evaluate the convenience of the investment in a disassembly line. Therefore, this document contributes to this research field by proposing a simple approach that supports the decision-making process of traditional manufacturing enterprises to evaluate a priori the economic return (i.e. how much the product cost decreases) and provide an estimate of the environmental benefits of integrating a manual disassembly line of EoL products with a traditional manufacturing system.

Human–robot collaboration in industrial environments: A literature review on non-destructive disassembly

Nowadays, numerous companies and industries introduce recycling processes in their production, aiming to increase the sustainable use of the planet’s natural resources. Nevertheless, these processes remain inefficient due to the high degree of complexity and variation in the products. In order to remedy this, industry stakeholders adopt the circular economy business model and introduce take-back programmes and remanufacturing processes for their End of Life products in their own supply chains. Take-back programmes enable the re-sourcing of sub-assemblies and components of previously manufactured products while remanufacturing processes encourage non-destructive disassembly. Due to the uncertain conditions of the re-sourced products, fully automated cells cannot cope with the demanding disassembly processes. Therefore, there is a need to establish hybrid disassembly robot cells where humans and robots work closely together in a process known as human–robot collaborative disassembly (HRCD). This paper examines the landscape of HRCD and reviews the progress in the field during the period 2009–2020. The analysis investigates principles and elements of human–robot collaboration in industrial environments such as safety standards and collaborative operation modes, HRI communication interfaces, and the design characteristics of a disassembly process. Additionally, the various technical challenges of HRCD are explored, and a review of existing systems supporting HRCD is presented. This review aims to support the robotics community in the future development of HRCD systems, discuss identified literature gaps, and suggest future research directions in this area. • This paper presents the first extensive review of the latest progress in the area of Industrial human–robot collaboration within the field of disassembly for the period 2009–2020. • The perspective is on how robot control schemes and human–robot interfaces enable human–robot collaborative disassembly tasks. • The outcome of this review identifies gaps in the practice of human–robot collaborative disassembly and provides the future trends and needs for robot cells focused on disassembly tasks.

Managing Uncertainties in Design Alternatives of EOL Products With Fractional Disassembly Yields

This paper proposes a model of an advanced remanufacturing to order and disassembly to order (ARTODTO) system which evaluates various design alternatives of products to satisfy the demands of retrieved products, components, and materials by disassembling these products at the end of their lives. The quality, quantity, and variety of end-of-life (EOL) products are uncertain which leads to fractional disassembly yields. Goal programming is used to determine the quantities of EOL products to be acquired in order to meet all the demands of retrieved products, components, and materials. A case example of EOL dryers is presented to demonstrate the steps and implementation of the proposed model.

Alloy information helps prioritize material criticality lists

Materials scientists employ metals and alloys that involve most of the periodic table. Nonetheless, materials scientists rarely take material criticality and reuse potential into account. In this work, we expand upon lists of “critical materials” generated by national and regional governments by showing that many materials are employed predominantly as alloying elements, which can be a deterrent to recovery and reuse at end of product life and, likely as a consequence, have low functional end-of-life recycling rates, among other problematic characteristics. We thereby single out six metals for enhanced concern: dysprosium, samarium, vanadium, niobium, tellurium, and gallium. From that perspective, the use of critical metals in low concentrations in alloys unlikely to be routinely recycled should be avoided if possible. If not, provision should be made for better identification and more efficient recycling so that materials designated as critical can have increased potential for more than a single functional use.

Two-stage stochastic program for disassembly lot-sizing under random ordering lead time

This study addresses disassembly lot-sizing that determines the ordering and disassembly schedules of end-of-life (EOL) products and subassemblies to satisfy items demands. A stochastic version with uncertain ordering lead time (OLT) is considered for the first time. Here, OLT represents the time elapsed between placing an order and receiving it (only an EOL product can be ordered). Stochastic integer programming model is developed for the objective of minimizing the expected sum of setup, purchase, inventory and backlog costs. To test the performance of the proposed model, computational experiments were done on various test instances and are reported. The results show that the performance of the proposed model depends on the size of the ordering lead times, the number of periods and components.

Multi-product capacitated disassembly lot-sizing problem with lost sales and possibility of defective disassembly components

One of the major disassembly problems is the number of defective disassembly components and uncertain return of end-of-life (EoL) products to be recovered from end user. In fact, if many disassembled components are defective or the quantity of EoL products to be recovered is inadequate, customer demand cannot be satisfied, and this generates lost sales. This paper addresses the multi-product capacitated disassembly lot-sizing problem with lost sales and possibility of defective disassembled items or components. The objective aims at determining the quantity of the EoL products to be disassembled in each period to minimize the total cost or maximize total profit earned by resale of the component recovered by disassembly operations. Hence, to maximize this total profit, a new mixed-integer linear programming (MILP) approach allowing lost sales is firstly proposed. Secondly, three properties that can be used to determine the optimal solution are studied. Finally, the proposed model is solved using well-known CPLEX solver to both show the model performances and verify the limit of exact method. Test results showed that CPLEX was unable to solve all instances in reasonable computational time and an optimization approach method such as heuristic or meta-heuristic will be crucial to solve the large instances.

Sustainable Neodymium Recovery Using an Electrochemical Approach

Neodymium is a technologically important metal in green energy due to its properties which are critical in wind turbines as permanent magnets and in rechargeable batteries for electric vehicles. However, its production and supply are concentrated in China and this, together with its high demand, has led to categorise it as a critical metal. Therefore, instead of relying exclusively on mining, neodymium recovery from end of life products is one of the most interesting ways to tackle the availability.Our research has been focused on electrolyte synthesis to attain Nd in the metallic and more expensive form via electrodeposition as an environmentally friendly approach.Electrochemical deposition of neodymium has also been attempted in several ionic liquids, but only been partially successful in phosphonium -based ILs, therefore that family of ionic liquids have been at the core of our research. The long alkyl chain trihexyltetradecylphosphonium bis(trifluoromethylsulfonyl)imide, [P66614][TFSI], have been selected due to its hydrophobic properties and electrochemical stability. The effect of concentration and water has been explored in our research concluding that, in fact, water promoted the Nd electrodeposition. A three-fold increase in current densities was observed in comparison with the analogous mixture with less water, which leads to more abundant deposits.[1] , [2] An in-depth study of the Nd3+ solvation in the bulk electrolyte indicated the presence of both bis(trifluoromethylsulfonyl)imide (TFSI) and water in the solvation sphere, as well as insight into the different mode of coordination of TFSI with Nd3+ (i.e. cis/ trans and mono/bidentate).More recently, we have reported a cleaner approach to recover Nd using a pyrrolidinium non-fluorinated ionic liquid (IL) (N-butyl-N-methylpyrrolidinium dicyanamide ([C4mpyr][DCA])) in the presence of neodymium nitrate. The main advantages of these electrolyte mixtures, is the elimination of higher risk of persistence, ecotoxicity when fluorine is decomposed. Additionally, relatively low manufacturing cost as opposed to fluorinated systems as contain only carbon and nitrogen. Our results presented eight times higher current density (-38 mA cm-2) at a lower temperature (halved to 50 °C) and less controlled environment (0.15 – 4.6 wt% H2O) compared to the literature.[3] This is translated into a more cost-effective recovery process. [1] Laura Sánchez-Cupido, Jennifer M. Pringle, Amal L. Siriwardana, Ainhoa Unzurrunzaga, Matthias Hilder, Maria Forsyth, Cristina Pozo-Gonzalo, “Water Facilitated Electrodeposition of Neodymium in a Phosphonium-based Ionic Liquid”, The Journal of Physical Chemistry Letters, 2019, 10, 2, 289-294 [2] Laura Sanchez Cupido, Jennifer M. Pringle, Amal Siriwardana, Matthias Hilder, Maria Forsyth, Cristina Pozo-Gonzalo,” Correlating electrochemical behaviour and speciation in neodymium ionic liquid electrolyte mixtures in the presence of water”, ACS Sustainable Chemistry & Engineering, 2020, 8, 14047-14057 [3] Kalani Periyapperuma, Jennifer M. Pringle, Laura Sanchez-Cupido, Maria Forsyth, and Cristina Pozo-Gonzalo,“Fluorine-Free Ionic Liquid Electrolytes for Sustainable Neodymium Recovery Using an Electrochemical Approach, Green chemistry”, 2021, DOI: 10.1039/D1GC00361E

Reinforcement Learning-Based Selective Disassembly Sequence Planning for the End-of-Life Products With Structure Uncertainty

Selective disassembly sequence planning (SDSP) is regarded as an efficient strategy to determine optimal disassembly sequences for extracting target parts (TP) from complex end-of-life (EOL) products. Previous research assumes that all EOL products have the same structure and the optimal selective disassembly sequences are given before the EOL products are removed. However, the products have different operation states during their use stage, which results in high structure uncertainty of EOL products. The structure uncertainty of EOL products often makes the predetermined selective disassembly sequences impractical for minimizing disassembly time and maximizing disassembly profit. This letter undertakes this challenge by integrated reinforcement learning (RL) to determine the optimal disassembly sequences adaptive to the structure uncertainty of the EOL products. Firstly, a multi-level selective disassembly hybrid graph model (MSDHGM) is developed to illustrate the contact, precedence, and level relationships among parts. Then, the SDSP is formulated as a finite Markov Decision Process and a deep Q-network based selective disassembly sequence planning (DQN-SDSP) is proposed. Finally, extensive comparative experiments are conducted to verify the proposed method compared with NSGA-II and ABC algorithms.

An integrated design method for remanufacturing process based on performance demand

Design for remanufacturing process (DFRP) plays a key role in implementing remanufacturing because it directly affects the performance recovery of the End-of-Life (EoL) products. Since the used parts of EoL products have various failure forms and defects, which make it hard to rapidly generate remanufacturing process scheme to satisfy the performance demand of the remanufactured products. Moreover, remanufacturing process parameters are prone to conflicts during remanufacturing processes, often leading to unsatisfactory remanufacturing processes. To accurately generate remanufacturing process scheme and solve the conflicts, an integrated design method for remanufacturing processes based on performance demand is proposed, which reuses the historical remanufacturing process data to generate the remanufacturing process scheme. Firstly, the Kansei Engineering (KE) and Quality Functional Development (QFD) are applied to analyze the performance demand data and map the demands to the engineering features. Then, Back Propagation Neural Network (BPNN) is applied to inversely generate the remanufacturing process scheme rapidly to satisfy the performance demands by reusing the historical remanufacturing process data. Meanwhile, Theory of Constraint (TOC) and TRIZ are used to identify and solve the conflicts of the remanufacturing process for the remanufacturing process scheme optimization. Finally, the DFRP of an EoL guide rail is taken as an example to demonstrate the effectiveness of the proposed method, the result of which shows the design method can quickly and efficiently generate the remanufacturing process scheme.

Optimal production decisions for remanufacturing end-of-life products under quality uncertainty and a carbon cap-and-trade policy

Currently, the majority of available studies on remanufacturing mainly consider a single category of end-of-life (EOL) products under the uncertainty quality and carbon emissions policies, independently. However, enterprises have to refurbish multiple EOL products, consider the impact of uncertain quality on carbon emissions, and obey carbon emissions policies in real production. Therefore, this work considers both single and multiple categories of EOL products under carbon emissions uncertainty and a carbon cap-and-trade policy, and proposes three production decision models: (1) a traditional single-product remanufacturing model under fixed carbon emissions (FCE); (2) an extended single-product remanufacturing (ESPR) model under variable carbon emissions (VCE); and (3) a multi-product remanufacturing model. For these models, three strategies are developed to obtain the optimum profit, derive the minimum cost, and evaluate the effectiveness of the ESPR model. To validate the effects of the parameters on production decisions, numerical simulations of the VCE and FCE cases are applied in single- and multi-product settings. Besides, comparative studies with previous publications and real-life data analysis are carried out to illustrate the robustness and practicability of the models and strategies. The optimal decisions demonstrate that the proposed models considering the uncertainty of the quality and carbon emissions can obtain greater profit and lower carbon emissions, and these models for remanufacturing systems can be effectively applied under conditions of continuous quality and demand distributions. The remanufacturers and decision-makers can apply our models and strategies to choose better quality EOL products to be remanufactured, which can lead to better both economic and environmental benefits.

Sustainability Assessment of Reuse and Recycling Management Options for End-of-Life Computers-Korean and Japanese Case Study Analysis

The depletion of natural resources and global warming have increased in severity globally. In the industrial field, assembly products, such as electronic products, should be disassembled for recycling and reuse to deal with these problems. Reuse and recycling can contribute to reducing GreenHouse Gas (GHG) emissions and less depletion of natural resources since GHG emissions for virgin material production can be saved using reused components and recycled materials. However, each component of selling revenue and material-based GHG emissions depends on the country because of the different energy mixes of electrical power. Moreover, each collected component embedded in End-of-Life (EOL) products needs to be selected as a life cycle option based on its remaining life. The purpose of this study is to decide life cycle options such as reuse, recycling, and disposal of each component environmentally-friendly and economically in Korea and Japanese cases for computers. Firstly, selecting the life cycle option for each component was formulated by 0–1 integer programming with ε constraints. Next, GHG emissions, profits, and costs in Korea and Japan were estimated and analyzed for each component. Finally, Korean and Japanese cases were analyzed to obtain an economic value in the same material-based GHG saving rate with each component’s life cycle option selection by comparing each EOL product data. In the experiments, GHG recovery efficiency was higher in Japan 43 [g/Yen] than one in Korea 28 [g/Yen]. Therefore, it was better to retrieve and reutilize the components in Korea. However, if the maximum GHG recovery efficiency is desired, Japan is a better option.

Minimizing maximum tardiness subject to collect the EOL products in a single machine scheduling problem with capacitated batch delivery and pickup systems

This paper introduces capacitated batch delivery and pickup systems (here it is named closed-loop batch delivery systems) in an integrated production scheduling and distribution operation problem for the first time. A comprehensive and integrated model is developed for production scheduling, delivering products to the multiple customers using batch delivery system, and picking-up End of Life (EOL) products in order to recycle and reuse in the manufacturer plant. The aim of this model is to minimize the maximum tardiness subject to collect the EOL product in order to improve sustainability. First, a Mixed Integer Linear Programming (MILP) model is developed to solve small-size instances. Due to the fact that this problem is NP-hard, a branch and bound algorithm is developed by introducing effective lower bounds in order to obtain optimal solutions for the large-size instances. In order to show the application of the problem, a real case study in a motor oil supply chain is presented. The results illustrate that paying attention to the problem of collecting used motor oils through simultaneous pick-up and delivery can reduce their disposal in nature or reduce non-standard recycling of motor oil, which in turn improves sustainability. Finally, experimental results on the random generation problems show that the proposed branch and bound algorithm is especially useful to solve large-size instances.

Disassembly Line Balancing by Using Simulation Optimization

Increasing environmental awareness in today's society and stricter environmental regulations have forced manufacturing firms to take necessary actions for the recovery of end-of-life (EOL) products through different options (e.g., recycling, remanufacturing,). Disassembly is regarded as a critical operation in EOL treatment of used products since all product recovery options require the disassembly of EOL products at certain levels. This critical operation is generally carried out by forming disassembly lines in product recovery facilities. Miscellaneous methodologies based on heuristics, metaheuristics and mathematical programming have been proposed for the balancing of disassembly lines. Majority of those methodologies assume that disassembly line parameters are deterministic by ignoring the fact that a disassembly line involves great deal of uncertainty mainly due to uncertain conditions of arriving EOL products. Considering this high level of uncertainty, simulation modeling can be an effective tool for the modeling of disassembly lines. In this study, a simulation-based disassembly line balancing methodology is proposed for the explicit consideration of stochastic parameters. First, simulation model of a disassembly line is constructed. Since the disassembly line balancing problem has a combinatorial nature, two commonly used metaheuristics (i.e., genetic algorithms (GAs) and simulated annealing (SA)) are integrated with the simulation model in order to balance the disassembly line. The disassembly sequence and task assignments proposed by GA are compared with the sequence and task assignments proposed by SA. This comparison indicates that GA outperforms SA in four of eight performance measures while both algorithms have the same value for line efficiency measure.

A Review on the Lifecycle Strategies Enhancing Remanufacturing

Remanufacturing is a domain that has increasingly been exploited during recent years due to its numerous advantages and the increasing need for society to promote a circular economy leading to sustainability. Remanufacturing is one of the main end-of-life (EoL) options that can lead to a circular economy. There is therefore a strong need to prioritize this option over other available options at the end-of-life stage of a product because it is the only recovery option that maintains the same quality as that of a new product. This review focuses on the different lifecycle strategies that can help improve remanufacturing; in other words, the various strategies prior to, during or after the end-of-life of a product that can increase the chances of that product being remanufactured rather than being recycled or disposed of after its end-of-use. The emergence of the fourth industrial revolution, also known as industry 4.0 (I4.0), will help enhance data acquisition and sharing between different stages in the supply chain, as well boost smart remanufacturing techniques. This review examines how strategies like design for remanufacturing (DfRem), remaining useful life (RUL), product service system (PSS), closed-loop supply chain (CLSC), smart remanufacturing, EoL product collection and reverse logistics (RL) can enhance remanufacturing. We should bear in mind that not all products can be remanufactured, so other options are also considered. This review mainly focuses on products that can be remanufactured. For this review, we used 181 research papers from three databases; Science Direct, Web of Science and Scopus.

Collection strategies and pricing decisions for dual channel EOL products

This paper studies the collection strategies and pricing decisions involving End-Of-Life (EOL) products with competing dual collection channels. One channel collects the EOL products through an online platform and the other channel receives the products through a traditional channel. Allowing either channel to be the main collection channel, the collector of EOL products can be either a manufacturer or retailer. Four collection strategies are studied based on the main channel and the collector: Traditional channel & Manufacturer, Traditional channel & Retailer, Online channel & Manufacturer, and Online channel & Retailer. The optimal pricing decision of each strategy is found using two nested Stackelberg games. The manufacturer’s channel selection and the retailer’s response as the collector are discussed. A numerical study analyzes the effect of the acceptance rates of the EOL products collected, remanufacturing cost, and product collection cost on the pricing decisions, willingness to return, and the manufacturer’s and retailer’s profit. The results suggest that re-allocating the resources can enhance the value of the main collection channel and improve the retailer’s willingness to collect through a traditional channel. The optimal collection strategies for profitability are obtained and the strategies for a higher willingness to return discussed.