Quantifying the impact of AIDC technologies for vehicle component recovery

Recovering value from end-of-life vehicles (ELV) has become increasingly important in recent years due to legislative pressures. In this context, cannibalisation of valuable components for possible reuse in secondary markets is becoming a popular option. However, recovery processes are characterised by high levels of uncertainty regarding the quality of components returned at their end-of-life. Hence, the key to making component recovery an cost-effective option is to provide more information regarding the condition of components in the ELV to the recoverer. Emerging automated identification and data capture (AIDC) technologies such as RFID tags and sensor networks can have significant impact on the effectiveness with which product information is generated and shared among the various actors in the product lifecycle. This paper illustrates how decision-making during product recovery can be improved by collecting critical usage data during a vehicle’s lifecycle and making it readily available to key decision-makers. In particular, we use a probabilistic model to quantify the benefits of automated information capture technologies in improving product recovery processes.     

Comparison of economic benefits of sensor embedded products and conventional products in a multi-product disassembly line

Sensors embedded into products during the production process are important data acquisition tools for after-sale products’ condition monitoring. By facilitating data collection from critical components in a product, these sensors help companies predict components and/or products failures during product usage. They are also very useful at the end-of-life (EOL) of products. Using sensor information, the conditions, types and remaining lives of components in an EOL product can be determined prior to actual disassembly. In this study, we assess the use of sensors in determining the steps in EOL processing of products. In particular, we evaluate the impact of sensor embedded products (SEPs) on various performance measures of an appliance disassembly line controlled by a multi-kanban system. First, separate design of experiments studies based on orthogonal arrays are carried out for conventional products (CPs) and SEPs. In order to calculate the response values for each experiment, detailed discrete event simulation models of both cases are developed considering the precedence relationships among the components together with the routing of different appliance types through the disassembly line. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. The test results show the superiority of SEPs over CPs with respect to all costs (viz., disassembly, disposal, testing, backorder, transportation, holding) as well as revenue and profit.     

Recovery of sensor embedded washing machines using a multi-kanban controlled disassembly line

Product recovery involves the recovery of materials and components from returned or end-of-life products. Disassembly, an element of product recovery, is the systematic separation of an assembly into its components, subassemblies or other groupings. Stricter environmental regulations together with dramatic decrease in natural resources and landfills have increased the importance of disassembly as all product recovery options require some level of disassembly. Due to changes made during the lifetime of a product by customers or service personnel, the number and the version of components prior to disassembly is unknown. Customers may also discriminate between and demand different versions of components. The existence of non-functional components further adds to the uncertainty associated with disassembly yield. Sensors implanted into products during their production can address this uncertainty by providing information on the number, condition and version of components prior to disassembly. In this study, we evaluate the impact of sensor embedded products (SEPs) on the various performance measures of a washing machine (WM) disassembly line controlled by a multi-kanban system, which takes into consideration the highly stochastic behavior of the line while managing material and kanban flows. First, separate design of experiments studies based on orthogonal arrays are performed for conventional products and SEPs. In order to observe the response of each experiment, detailed discrete event simulation (DES) models for both types of products are developed considering the precedence relationships among the components of a WM. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. According to the results, SEPs provide significant reductions in all costs (viz., backorder, holding, disassembly, disposal, testing and transportation) while increasing revenue and profit.     

A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem

Value recovery from end-of-life products plays a key role in sustainability and circular economy, which starts with disassembly of products into components for reuse, remanufacturing, or recycling. As the process is often complex, a disassembly sequencing problem (DSP) studies how to optimally disassemble products considering the physical constraints between subassemblies/disassembly tasks for maximum profit. With a growing attention on energy conservation, this paper addresses a profit-oriented and energy-efficient DSP (PEDSP), whereby not only the profit is maximized, but also energy consumption is accounted as an important decision criterion. In this work, a disassembly AND/OR graph (DAOG) is used to model a disassembly diagram for a product, in which the ‘AND’ and ‘OR’ relations illustrate precedence relationships between subassemblies. Based on the DAOG, we propose a hybrid multi-objective metaheuristic that integrates an artificial bee colony algorithm, a non-dominated sorting procedure, and a variable neighborhood search approach to solve the PEDSP for Pareto solutions. The proposed method is applied to real-world cases (i.e., a simple ballpoint pen and a relatively complex radio) and compared with other multi-objective algorithms. The results indicate that our method can quickly produce a Pareto front that outperforms the alternative approaches.     

Multi-objective partial disassembly optimization based on sequence feasibility

A sustainable manufacturing system integrates production systems, consumer usage behavior, and End-of-Life (EoL) product value recovery activities. Facilitating multi-objective disassembly planning can be a step toward analyzing the tradeoffs between the environmental impact and profitability of value recovery. In this paper, a Genetic Algorithm (GA) heuristic is developed to optimize partial disassembly sequences based on disassembly operation costs, recovery reprocessing costs, revenues, and environmental impacts. EoL products may not warrant disassembly past a unique disassembly level due to limited recovered component market demand, minimal material recovery value, or minimal functional recovery value. The effectiveness of the proposed GA is first verified and tested using a simple disassembly problem and then applied to the traditional coffee maker disassembly case study. Analyses are disaggregated into multiple disassembly network optimization problems, one for each product subassembly, resulting in a bottom-up approach to EoL product partial disassembly sequence optimization.     

RFID-based product information in end-of-life decision making

With the increasing pressure on manufacturing companies to manage their end-of-life products, the availability of information to improve product recovery decisions is becoming critical. In fact, a fundamental obstacle in making efficient product recovery decisions is the loss of information associated with the product after the point-of-sale. We show qualitatively that the availability of product information has a positive impact on product recovery decisions, and discuss how radio-frequency identification (RFID)-based product identification technologies can be employed to provide the necessary information. The paper also investigates how recovery decisions can be modelled to represent the impact of product information on those decisions.     

Genetically optimised disassembly sequence for automotive component reuse

Environmental sustainability through end-of-life recovery has become the main items of contest in the automotive industries. Component reuse as one of the product recovery strategy is now gaining importance in view of its impact on the environment. Disassembly as one of the determinant factors for reuse is a very important and difficult process in life cycle engineering. To enable reuse, a certain level of disassembly of each component is necessary so that parts of the products that have arrived at their end-of life can be easily taken apart. Improvements to the disassembly process of products can be achieved at two levels: in the design phase, making choices that favours the ease of disassembly of the constructional system (design for disassembly) and planning at best and optimising the disassembly sequence (disassembly sequence planning). Hence, finding an optimal disassembly sequence is important to increase the reusability of the product. This paper presents the development work on an optimisation model for disassembly sequence using the genetic algorithms (GA) approach. GA is chosen to solve this optimisation model due to its capability in solving many large and complex optimisation problems compared with other heuristic methods. The fitness function of the GA in this study is dependent on the increment in disassembly time. Comparison of results using different combinatorial operators and tests with different probability factors are shown. This paper will present and discuss the disassembly sequence of an engine block, as a case example which achieves the minimum disassembly time.     

Quality management in product recovery using the Internet of Things: An optimization approach

Internet of Things, by reducing or almost eliminating uncertainty regarding existence, types, conditions and remaining lives of components in an end-of-life product (EOLP), can mitigate planning of remanufacturing operations. Remaining useful life can be taken into account as a good measure of quality. Therefore, immediate determination of remaining useful life allows optimal recovery decisions to guarantee a minimum quality level on recovered products while satisfying various system criteria.     

Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA)

Decrease in product life along with the advent of stringent regulations and environmental consciousness have led to increased concern for methodological product recovery through disassembly operations. This research proposes a fuzzy disassembly optimization model (FDOM) and is aimed at determining the optimal disassembly sequence as well as the optimal depth of disassembly to maximize the net revenue at the end-of-life (EOL) disposal of the product in the real world situations. In order to account for the uncertainty inherent in quality of the returned products, fuzzy control theory is incorporated in the problem environment for modeling the expected value of the recovered modules. Considering the computational complexity of the problem at hand, an innovative approach of Algorithm of Self-Guided Ants (ASGAs) is proposed for the same. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment techniques and analysis of variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization and Genetic Algorithm over which it always demonstrated better results thereby proving its superiority on the concerned problem.     

A Decision Maker-Centered End-of-Life Product Recovery System for Robot Task Sequencing

End-of-life (EOL) disassembly focuses on regaining the value embodied in products which are considered to have completed their useful lives due to a variety of reasons such as lack of technical functionality and/or lack of demand. Disassembly is known to possess unique characteristics due to possible changes in the EOL product structure and hence, cannot be considered as the reverse of assembly operations. With similar reasoning, obtaining a near-optimal/optimal disassembly sequence requires intelligent decision making during the disassembly when the sequence needs to be regenerated to accommodate these unforeseeable changes. That is, if one or more components which were included in the original bill-of-material (BOM) of the product is missing or if one or more joint types are different than the ones that are listed in the original BOM, the sequence needs to be able to adapt and generate a new and accurate alternative for disassembly. These considerations require disassembly sequencing to be solved by more powerful and versatile methodologies justifying the utilization of image detection technologies for online real-time disassembly while imposing search techniques which would provide more efficient solutions than their exhaustive search counterparts. Therefore, EOL disassembly sequencing literature offers a variety of heuristic techniques. As with any data driven technique, the performance of the proposed methodologies is heavily reliant on the accuracy and the flexibility of the algorithms and their abilities to accommodate several special considerations such as preserving the precedence relationships during disassembly while obtaining near-optimal or optimal solutions. This study builds on previous disassembly sequencing research and introduces an automated robotic disassembly framework for EOL electronic products. The model incorporates decision makers’ (DMs’) preferences into the problem environment for efficient material and component recovery. A numerical example is provided to demonstrate the functionality of the proposed approach.     

Interference probability matrix for disassembly sequence planning under uncertain interference

Disassembly of end-of-life products is a common step in remanufacturing and recycling. Disassembly sequence planning is the process that automatically finds the optimal sequence of components being removed. A key element of disassembly sequence planning is a suitable mathematical representation that describes the interference of any two components in a product. Previous studies on disassembly sequence planning have tended to focused on the interference that is fixed and known. However, the interference may be uncertain due to complex end-of-life conditions such as deformation, corrosion and rust. To deal with uncertain interference, this paper proposes an interference probability matrix as a new mathematical representation that uses probability to indicate uncertainty in the interference, and establishes a multi-threshold planning scheme to generate the optimal disassembly sequence plans. Three case studies are given to demonstrate the use of the proposed approach. It is also tested the performance of four multi-objective optimization algorithms that can be adopted in the proposed multi-threshold planning scheme.     

不确定环境下考虑多约束的拆卸收益概率模型研究

废旧产品拆卸过程中存在许多的不确定因素, 进行目标拆卸时需要兼顾整个拆卸活动的整体收益。在利用拆卸网络图得到目标零部件的所有可行拆卸序列之后, 将零部件质量不确定、拆卸破坏率、基本拆卸时间随机等因素进行综合考虑, 建立了不确定环境下的拆卸收益模型, 在同时满足拆卸破坏率和拆卸时间约束下, 基于拆卸收益概率进行序列优化, 并设计了基于随机模拟的求解方法。最后通过案例分析体现出决策者要求不同时序列优化结果的变化, 验证了所提出模型的可行性。     

A heuristic approach for allocation of data to RFID tags: A data allocation knapsack problem (DAKP)

Durable products and their components are increasingly being equipped with one of several forms of automatic identification technology such as radio frequency identification (RFID). This technology enables data collection, storage, and transmission of product information throughout its life cycle. Ideally all available relevant information could be stored on RFID tags with new information being added to the tags as it becomes available. However, because of the finite memory capacity of RFID tags along with the magnitude of potential lifecycle data, users need to be more selective in data allocation. In this research, the data allocation problem is modeled as a variant of the nonlinear knapsack problem. The objective is to determine the number of items to place on the tag such that the value of the “unexplained” data left off the tag is minimized. A binary encoded genetic algorithm is proposed and an extensive computational study is performed to illustrate the effectiveness of this approach. Additionally, we discuss some properties of the optimal solution which can be effective in solving more difficult problem instances.     

Human–robot collaborative partial destruction disassembly sequence planning method for end-of-life product driven by multi-failures

Partial destructive disassembly (PDD) of large complex products often requires human–robot collaborative disassembly (HRCD). Therefore, a human–robot collaboration partial destructive disassembly sequence planning (HRCPDDSP) method of end-of-life products driven by multiple failures is proposed to obtain the optimal disassembly sequence to improve disassembly efficiency and degree of automation. Based on the product disassembly priority and part failure characteristics, an information model of the HRCPDDSP method is constructed. Furthermore, the model is expressed as a constraint matrix and recycling decision matrix. A multi-layer chromosome coding method, including HRCD, destructive constraint, and node layers, is proposed of the characteristics of the HRCPDDSP method. The approximate optimal sequence of human–robot collaboration PDD is achieved by improving evolutionary mechanisms such as selection, crossing, and mutation. Finally, the model and algorithm are applied to solve a case of HRCPDDSP, and the feasibility and effectiveness of the proposed method are further verified by comparison with other disassembly modes.     

Intelligent decision making in disassembly process based on fuzzy reasoning Petri nets

Practical disassembly process planning is extremely important for efficient material recycling and components reuse. The research work for the process planning in literature focuses on the generation of optimal sequences based on the predictive information of products. The used products, unfortunately, exhibit high uncertainty since products may experience very different conditions during their use stage. The indeterminate characteristics associated to used products often makes the predetermined plan unrealistic. Their disassembly process has to be decided dynamically adaptive to the products' specific status. To be able to deal with uncertainty in a dynamic decision making process, this paper presents a fuzzy reasoning Petri net (FRPN) model to represent related decision making rules in disassembly process. Using the proposed fuzzy reasoning algorithm based on the FRPN model, the multicriterion disassembly rules can be considered in the parallel way to make the decision automatically and quickly. Instead of producing the disassembly sequences before disassembling a whole product, the proposed method makes intelligent decisions based on dynamically updated status of components in the product at each disassembly step. Therefore, it is adaptive to the changes that arise during the process. Finally, an example is used to illustrate the application of the proposed methodology.     

A Kano model,AHP and M-TOPSIS method-based technique for disassembly line balancing under fuzzy environment

Recovery, recycling or remanufacturing of post-consumed products are viable alternatives for reducing the environmental problems resulting from the huge amounts of waste currently arriving at landfills. Disassembly operations are inevitable for product recovery therefore the disassembly line is an appropriate choice to carry out the same. A disassembly line balancing problem is how to streamline the disassembly activities, so that the total disassembly time required at each workstation is approximately the same. The assignment of jobs to workstations in a disassembly environment has been the matter of concern to researchers because the product, which has to be disassembled, have different types of materials. The main aim of a disassembly process is to reuse components and reduce undesirable impact on the environment. This paper applies a Kano model, fuzzy-AHP, and M-TOPSIS-based technique, shown to successfully find the optimal order of component removal using AND/OR precedence relation. The tasks are assigned to the disassembly workstations according to their priority rank and precedence relations. The proposed technique has been illustrated with an example and the results show improvements in the performance in comparison with other techniques.     

Remanufacturability evaluation method and application for used engineering machinery parts based on fuzzy-EAHP

Remanufacturing is an important mode to promote the sustainable development of manufacturing industry. Remanufacturing mechanical components at the best recycling time can effectively reduce the remanufacturing cost and improve the remanufacturing efficiency. Therefore, the decision-making model of optimal recycling time based on full lifecycle assessment and full lifecycle cost method was firstly established to analyze and model the energy consumption, cost and emission of components in the original manufacturing, usage, remanufacturing and reuse stages. Then, a remanufacturing evaluation method based on fuzzy extension analytic hierarchy process was proposed. Considering a variety of uncertainties of waste components, a qualitative and quantitative evaluation index system was established, and the weight of each evaluation index was determined by expert evaluation and fuzzy comprehensive evaluation method. Finally, a boom cylinder of a concrete pump truck was studied. The boom cylinder recovered by the method proposed has a high recovery value, and its comprehensive score reaches 70, which verifies the effectiveness and superiority of the model.     

Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: The impact of parts reliability and product greenness on the recovery network

Closed-loop supply chain network (CLSCN) design aims to incorporate environmental considerations into the traditional supply chain design by including recycling, disassembly and reuse activities. A CLSCN incorporates the use and reuse of environmentally friendly products and materials supported by the design of an appropriate recovery, disassembly, and refurbishing network. In the design process, a trade-off must often be made between the need to maximize profit and maximize greenness. The latter is considered for several reasons including regulatory requirements, corporate responsibility and corporate image. In this paper, a bi-objective mixed integer programming model is developed and solved for a forward/reverse logistic network including three echelons in the forward direction (suppliers, assembly centers and customer zones) and two echelons in the reverse direction (disassembly and recycling center). A set of Pareto optimal solutions is obtained to show the trade-off between the profit and the greenness objectives. Some useful managerial insights are developed through various computational experiments.     

Selective disassembly for virtual prototyping as applied to de-manufacturing

Selective disassembly involves separating a selected set of components from an assembly. Applications for selective disassembly include de-manufacturing (maintenance and recycling), and assembling. This paper presents a new methodology for performing design for selective disassembly analysis on the CAD model of an assembly. The methodology involves the following three steps: (i) identifying the components to be selectively disassembled for de-manufacturing by a software program or designer, (ii) determining an optimal (e.g. minimal cost) disassembly sequence for the selected components that involves a computationally efficient two-level reduction procedure: (a) the determination of a set of sequences with an objective of minimal component removals via a wave propagation approach that topologically order components in an assembly for selective disassembly, and (b) the evaluation of resulting sequences based on an objective function (e.g. minimal cost) to identify an optimal sequence, and (iii) Performing disassembly design decisions based on the evaluated optimal sequence. Preliminary implementation results of the selective disassembly methodology in sequencing and disassembly cost evaluation, and application of the selective disassembly technique for de-manufacturing assessment are presented.     

The construction of a collaborative-design platform to support waste electrical and electronic equipment recycling

Recycling of waste electrical and electronic equipment (WEEE) is a very important subject not only from the viewpoint of waste treatment but also from the viewpoint of recovery of valuable materials. In the past, some obstacles make recycling challenging for today's manufactured products. First, it is difficult to gain all the information necessary to plan for the recycling evaluation, as most design information is owned and kept by suppliers. Another problem in recycling end-of-life (EOL) products is a lack of technologies to handle the very complex products that are being discarded today, because the knowledge of how to do so is owned by the recycler.This research demonstrates how to support WEEE recycling analysis by environmental information with the part of bill of material. A collaborative-design platform is further constructed and collected all the needed information using computer-aided design (CAD), enterprise resource planning (ERP), and product life-cycle management (PLM) systems. Through this platform, suppliers are required to provide component information to enable the manufacturer's design for disassembly and recycling analysis. The results demonstrate that designers can obtain disassembly and recycling information through the model, so that desirable changes can be made in the early stages of a design. An industrial case study from Taiwan is also provided to demonstrate the use of this model.