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.     

Disassembly planning and sequencing for end-of-life products with RFID enriched information

When a product reaches its end of lifecycle, components of the product can be reused, recycled, or disposed, depending on their conditions and recovery value. In order to make an optimal disassembly plan to efficiently retrieve the reusable and recyclable items inside a product, knowing the true condition of each component is essential. Practically, the recovery value of a used product is often estimated roughly via visual inspection, and the inaccurate estimates would lead to suboptimal disassembly plans. This paper proposes the use of radio-frequency identification (RFID) technology to support disassembly decisions for end-of-life products. RFID can track pertinent data throughout a product’s lifecycle. With the enriched information, a fuzzy-based disassembly planning and sequencing model is proposed to maximize net profit. First, a Bayesian method translates the RFID data into a quality index of the components. Then, a fuzzy logic model, solved by genetic algorithm, synthesizes input variables (i.e., product usage, component usage, and component condition) into a solution of optimal disassembly sequence that maximizes profit considering recovery value and disassembly cost. This paper verifies the merits of using RFID to improve disassembly decisions that help reuse and recycle end-of-life products to reduce environmental impact.     

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.     

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.     

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.     

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.     

A systematic approach to design and operation of disassembly lines

This paper presents a systematic approach to the disassembly line (DL) design in meeting the requirement of variant orders for multiple used parts with different due dates. An extended disassembly Petri net model is proposed for the hierarchical modeling in order to derive the disassembly path with the maximal benefit in the presence of some defective components. An algorithm for balancing DLs to maximize the productivity of a disassembly system is presented. The results of simulation runs of the proposed methodology and algorithms applied to a simplified personal computer disassembly are provided. This work lays a foundation for designing efficient industrial automatic and semiautomatic disassembly systems. Note to Practitioners-Disassembly is rapidly growing in importance as manufacturers face increasing pressure to deal with obsolete products in an environmentally responsible and economically sound manner. This process can be performed at a single workstation or on a disassembly line (DL) that is organized as a sequence of workstations, each with one or more machines/operators to handle a certain type of disassembly task. Compared to a single workstation, DL provides higher productivity and greater potential for disassembly automation. However, it still faces serious scheduling and inventory problems because of a high degree of uncertainty in discarded products and disparity between demands for certain parts and their yield from disassembly. To address this challenge, this paper proposes a two-level systematic approach, aiming to maximize system throughput and system revenue by dynamically configuring the disassembly system into many DLs, while considering line balance, different process flows, and meeting different order due dates. The research results can help engineers build better disassembly systems.     

Solving large scale disassembly line balancing problem with uncertainty using reinforcement learning

Due to increasing environmental concerns, manufacturers are forced to take back their products at the end of products’ useful functional life. Manufacturers explore various options including disassembly operations to recover components and subassemblies for reuse, remanufacture, and recycle to extend the life of materials in use and cut down the disposal volume. However, disassembly operations are problematic due to high degree of uncertainty associated with the quality and configuration of product returns. In this research we address the disassembly line balancing problem (DLBP) using a Monte-Carlo based reinforcement learning technique. This reinforcement learning approach is tailored fit to the underlying dynamics of a DLBP. The research results indicate that the reinforcement learning based method is able to perform effectively, even on a complex large scale problem, within a reasonable amount of computational time. The proposed method performed on par or better than the benchmark methods for solving DLBP reported in the literature. Unlike other methods which are usually limited deterministic environments, the reinforcement learning based method is able to operate in deterministic as well as stochastic environments.     

Model review and algorithm comparison on multi-objective disassembly line balancing

As the disassembly of end-of-life products is affected by several dynamic and uncertain issues, many mathematical models and solution approaches have been established. However, with more extended objectives, constraints and different methods of disassembly, inconsistent models relating to product representations and types of disassembly lines have become the main barriers for the transfer of research to practise. In this paper, a systematic overview of recent models to summarise the input data, parameters, decision variables, constraints and objectives of disassembly line balancing are presented. After discussing the adaptation and extensibility of these models for different environments, a unified encoding scheme is designed to apply typical multi-objective evolutionary algorithms on this problem with extensive decision variables and seven significant objectives. Algorithm comparison on four typical cases is then carried out based on seven commonly used products to verify the optimisation process for the integrated version of existing models and demonstrate the overall performance of the typical multi-objective evolutionary algorithms on this problem. Experimental results can be a baseline for further algorithm design and practical algorithm selection on these disassembly line balancing scenarios.     

Evolutionary robust optimization for software product line scoping: An explorative study

Background: Software product line (SPL) scoping is an important phase when planning for product line adoption. An SPL scope specifies: (1) the extent of the domain supported by the product line, (2) portfolio of products in the product line and (3) list of assets to be developed for reuse across the family of products.Issue: SPL scope planning is usually based on estimates about the state of the market and the engineering capabilities of the development team. One challenge with these estimates is that there are inaccuracies due to uncertainty in the environment or accuracy of measurement. This may result in issues ranging from suboptimal plans to infeasible plans.Objective: To address the above, we propose to include uncertainty as part of the SPL scoping model. Plans developed in consideration of uncertainty would be more robust against possible fluctuations in estimates.Approach: In this paper, a method to incorporate uncertainty in scoping optimization and its application to generate robust solutions is proposed. We capture uncertainty as part of the formulation and model scoping optimization as a multi-objective problem with profit and stability as fitness functions. Profit stability and feasibility stability are considered to represent stability concerns.Results: Results show that, compared to other scope optimization approaches, both performance stability and feasibility stability are improved while maintaining near optimal performance for profit objective. Also, generated results consist of solutions with trade-offs between profit and stability, providing the decision maker with enhanced decision support.Conclusion: Multi-objective optimization with stability consideration for SPL scoping provides project managers with a robust and flexible way to address uncertainty in the process of SPL scoping.     

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.     

Disassembly line balancing under high variety of end of life states using a joint precedence graph approach

Disassembly is an important aspect of end of life product treatment, as well as having products disassembled in an efficient and responsible manner. Disassembly line balancing is a technique that enables a product to be disassembled as efficiently and economically viable as possible; however, considering all possible end of life (EOL) states of a product makes disassembly line balancing very difficult. The EOL state and the possibility of multiple recovery options of a product can alter both disassembly tasks and task times for the disassembly of the EOL product. This paper shows how generating a joint precedence graph based on the different EOL states of a product is beneficial to achieving an optimal line balance where traditional line balancing approaches are used. We use a simple example of a pen from the literature to show how a joint disassembly precedence graph is created and a laptop example for joint precedence graph generation and balancing. We run multiple scenarios where the EOL conditions have different probabilities and compare results for the case of deterministic task times. We also consider the possibility where some disassembly task times are normally distributed and show how a stochastic joint precedence graph can be created and used in a stochastic line balancing formulation.     

Platform technologies and network goods: insights on product launch and management

Entrepreneurs of technology platforms and network goods face distinctive challenges in managing customer adoption, and in trading off growth and profitability. The firm has several levers of control including managing product design and the intensity of network effects, managing the timing of product announcement versus actual product release, selecting the target market for initial product launch, and whether to sell a single version or an expanded product line. Product line expansion is especially useful under network effects. A freemium approach can help the firm manage both growth (via the free product) and profitability (via the premium higher-priced version). However, expanding the product line carries substantial fixed costs (e.g., marketing cost, cost of additional plant, managing multiple sets of inventory, increased distribution cost). Firms are deterred from incurring these fixed costs when there is uncertainty about product success. Such uncertainty is particularly relevant for multi-sided networks—where the value from joining one network (e.g., users) depends on the size of the other side (e.g., developers)—because potential participants on each side may be uncertain about participation on the other side. Despite uncertainty, product line expansion can be attractive for both startups and established firms. Established firms face lower uncertainty about developer participation, and should expand when fixed costs of expansion are low (and do so early in the product’s life cycle). In contrast, startup firms face greater uncertainty in securing participation from third-party developers, and are more likely to benefit from a “wait and see” or deferred expansion strategy.     

Learning-Based Disassembly Process Planner for Uncertainty Management

As product lifecycles are getting shorter and shorter, manufacturers are facing a great deal of economic and political pressure to reclaim and recycle their obsolete products. Disassembly, as one of the natural solutions, is of increasing importance in material and product recovery. However, this process is fraught with a high level of uncertainty (e.g., variations in product structure and condition, and human factors). The development of an effective modeling and management tool for such involved factors is critical in moving disassembly toward a more efficient and automated regime. This paper builds upon our previous work to undertake this problem. More specifically, a fuzzy Petri net model is introduced to explicitly represent the dynamics inherent in disassembly. Instead of presuming the pertinent data in the model is already known, a self-adaptive disassembly process planner and associated computationally effective algorithms are designed in a way to: 1) accumulate the past experience of predicting such data and, at the same time, 2) exploit the “knowledge” captured in the data to choose the best disassembly plan and improve the overall disassembly performance. To ensure the robustness of the learning procedure, variable memory length is further introduced. The proposed methodology and algorithms are illustrated through the disassembly of a batch of personal computers in a prototypical disassembly system.      

Modular product platform configuration and co-planning of assembly lines using assembly and disassembly

Formation of products platforms is carried out during the planning stage and very often separately from the planning of corresponding assembly lines. There is a dearth of literature which considers the different aspects of fully integrating platform design, product family formation, assembly line design, delayed product differentiation, and new concepts of mass customization. A Modular Product Platform Configuration model which uses assembly and disassembly for configuring product variants and Co-Planning of products platforms (MPCC) and their assembly Lines is presented. It is used to co-plan the common platform components and the associated product families simultaneously with the planning of its corresponding mixed-model assembly line. Using both assembly and disassembly to customize the product family platform in order to generate product variants is not commonly discussed in literature. It is defined as the formation of platforms for use to derive multiple products by including many components not shared by every product. The platform is then customized by assembling or disassembling components to form different product variants. The model is formulated using mixed integer mathematical programming to minimize the number of assembly stations and cycle time. Two case studies are used for verification and demonstration. They illustrated the ability of the MPCC model to integrate the planning of product platform, product families and the number of assembly stations required to assemble and disassemble components from mass-assembled product platforms to derive new product variants.     

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。     

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.     

Multi-objective optimization of stochastic disassembly line balancing with station paralleling

One of the major activities performed in product recovery is disassembly. Disassembly line is the most suitable setting to disassemble a product. Therefore, designing and balancing efficient disassembly systems are important to optimize the product recovery process. In this study, we deal with multi-objective optimization of a stochastic disassembly line balancing problem (DLBP) with station paralleling and propose a new genetic algorithm (GA) for solving this multi-objective optimization problem. The line balance and design costs objectives are simultaneously optimized by using an AND/OR Graph (AOG) of the product. The proposed GA is designed to generate Pareto-optimal solutions considering two different fitness evaluation approaches, repair algorithms and a diversification strategy. It is tested on 96 test problems that were generated using the benchmark problem generation scheme for problems defined on AOG as developed in literature. In addition, to validate the performance of the algorithm, a goal programming approach and a heuristic approach are presented and their results are compared with those obtained by using GA. Computational results show that GA can be considered as an effective and efficient solution algorithm for solving stochastic DLBP with station paralleling in terms of the solution quality and CPU time.     

Disassembly sequence and cost analysis for electromechanical products

For companies, the improvement of the recyclability performance of their newly designed products is becoming an integral part of product development process. The concept of environmental conscious design (ECD) has been adopted to assist the environmental performance of the products at the early stage of designing. This new trend requires that the design strategies need to be modified by integrating the environmental constraints. This paper provides the disassembly sequence and cost analysis for the electromechanical products during the design stage. The disassembly planning is divided into four stages: geometric assembly representation, cut-vertex search analysis, disassembly precedence matrix analysis, and disassembly sequences and plan generation. The disassembly cost is categorized into three types: target disassembly, full disassembly, and optimal disassembly. The result of this approach shows that the electromechanical products can be disassembled systematically and economically.     

Artificial bee colony algorithm for solving sequence-dependent disassembly line balancing problem

In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) with multiple objectives that requires the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures. Since the complexity of SDDLBP increases with the number of parts of the product, an efficient methodology based on artificial bee colony (ABC) is proposed to solve the SDDLBP. ABC is an optimization technique which is inspired by the behavior of honey bees. The performance of the proposed algorithm was tested against six other algorithms. The results show that the proposed ABC algorithm performs well and is superior to the other six algorithms in terms of the objective values performance.