基于模糊推理的不确定环境下拆卸工艺规划

回收产品因磨损等原因其结构和质量会发生改变,拆卸序列规划存在不确定性,拆卸前很难确定产品最优拆卸序列。首先建立一个模糊拆卸Petri网模型表示产品可行拆卸序列和拆卸过程存在的不确定信息,为降低产品质量和拆卸能力的不确定性对拆卸序列优化的影响,建立一个自适应的模糊推理系统,利用模糊推理和反馈学习的方法对产品各拆卸步骤的成本进行预测,然后通过计算不同拆卸序列下拆卸的收益来得到产品最优拆卸序列,最后通过算例证明了方法的有效性。     

Dynamic disassembly planning for remanufacturing of multiple types of products

With the increased need for remanufacturing of end-of-life products, achieving economic efficiency in remanufacturing is urgently needed. The purpose of this study was to devise a cost-minimisation plan for disassembly and remanufacturing of end-of-life products returned by consumers. A returned end-of-life product is disassembled into remanufacturable parts, which are supposed to be used for new products after being remanufactured. Each end-of-life product is disassembled into parts at variable levels and through variable sequences as needed, taking into account not only disassembly but also manufacturing, remanufacturing, and holding inventory of remanufacturable parts. This study proposes a mixed integer linear programming (MILP) model for derivation of the optimal disassembly plan for each returned product, under deterministically known demand and return flows. For the purposes of an illustrative example, the proposed model was applied to the formulation of an optimal disassembly and remanufacturing plan of ‘fuser assembly’ of laser printers. The solution reveals that variable-level disassembly of products saves a significant remanufacturing cost compared with full disassembly.     

拆卸与或图生成及其权重不定问题研究

为了高效地规划装配体的拆卸序列,首先利用生成完备可行子装配体的算法构造所有可行的拆卸操作,并以与或图的形式将所有可行的拆卸操作加以表达,对该与或图进行简约,从而构建能够表达所有可行拆卸序列的拆卸与或图;为了基于该拆卸与或图规划拆卸序列,利用广义权重解决了拆卸与或图中有向边上权重不确定的问题,并给出了广义权重的计算方法;最后,通过实例验证了这种拆卸与或图表达拆卸序列的高效性和用于拆卸序列规划的可行性和有效性.结果表明拆卸与或图是一种可用于拆卸序列规划的高效模型.     

On the attractiveness of sorting before disassembly in remanufacturing

We examine the attractiveness of simple sorting procedures characterized by limited accuracy just before disassembly and remanufacturing of used products. That type of quick sorting is often made possible through the installation of simple electronic devices in new products, which record basic usage data and provide information about the remanufacturability of the product without the need for its disassembly. We study a two-level reverse supply chain with remanufacturing and we concentrate on the single-period setting. There is uncertainty about the remanufacturability of used products and we derive the conditions under which quick sorting is economically justifiable. We show that the economic attractiveness of sorting depends on the costs of transportation, disposal, disassembly, the cost and accuracy of the sorting procedure and the expected quality of the returned items.     

Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing

Increasing attention is being paid to remanufacturing due to environmental protection and resource saving. Disassembly, as an essential step of remanufacturing, is always manually finished which is time-consuming while robotic disassembly can improve disassembly efficiency. Before the execution of disassembly, generating optimal disassembly sequence plays a vital role in improving disassembly efficiency. In this paper, to minimise the total disassembly time, an enhanced discrete Bees algorithm (EDBA) is proposed to solve robotic disassembly sequence planning (RDSP) problem. Firstly, the modified feasible solution generation (MFSG) method is used to build the disassembly model. After that, the evaluation criterions for RDSP are proposed to describe the total disassembly time of a disassembly sequence. Then, with the help of mutation operator, EDBA is proposed to determine the optimal disassembly sequence of RDSP. Finally, case studies based on two gear pumps are used to verify the effectiveness of the proposed method. The performance of EDBA is analysed under different parameters and compared with existing optimisation algorithms used in disassembly sequence planning (DSP). The result shows the proposed method is more suitable for robotic disassembly than the traditional method and EDBA generates better quality of solutions compared with the other optimisation algorithms.     

Disassembly line balancing using linear physical programming

Disassembly is the separation of a product into its constituent parts in a systematic way. It has gained importance recently due to its vital importance in product recovery. Cost-effective implementation of disassembly operation has a direct impact on the profitability of product recovery activities (recycling, remanufacturing etc.). Although it is possible to carry out disassembly operations in a disassembly station or in a disassembly cell, the highest productivity is achieved in a disassembly line. The output of a disassembly line can be maximised only if the line is balanced. A linear physical programming-based disassembly line balancing method is proposed in this study. This method was used to balance a mixed-model disassembly line and the effectiveness of the method was illustrated by analysing the results.     

Disassembly process planning algorithms for end-of-life product recovery and environmentally conscious disposal

Disassembly process planning is an act of preparing detailed operation instructions for disassembling used or an end-of-life (EOL) product to recover or dispose of its constituent parts or subassemblies. The main decisions are: (a) disassembly level; (b) disassembly sequence; and (c) EOL options such as reuse, remanufacturing, recycling, incineration, landfill, etc. This study deals with the three decision variables simultaneously in the parallel disassembly environment for the objective of maximising the profit. Unlike previous studies, we consider practical constraints, i.e., reuse probability and environmental impacts of parts or subassemblies, sequence-dependent setup costs, regulation on recovery rate, and incineration capacity. To represent and solve the problem, we develop an extended AND/OR graph, and then suggest a two-phase algorithm. To show the effectiveness of the proposed algorithm, two case studies have been carried out on an automatic pencil and a telephone. Also, test results on other general product structures are reported.     

Economical evaluation of disassembly operations for recycling,remanufacturing and reuse

This paper introduced a procedure which integrates economical factors into the scheduling of disassembly operations for Material Recovery Opportunities (MRO). MRO are defined as opportunities to reclaim post-consumer products for recycling, remanufacturing and reuse. Traditionally, recyclers have resorted to using heuristics for analysing the breakdown of products and the associated costs. In this paper, a quantitative method of disassembly analysis is developed. Its aim is to improve the efficiency of the disassembly planning process and to generate an optimal disassembly sequence which maximizes profit. Three economic indices are used to evaluate the trade-off between reclamation and disposal of individual components. A systematic procedure of generating an optimal disassembly sequence based on maximizing the profits of material recovery is presented. Three criteria are established to reduce the search space and facilitate recovery opportunities: (1) material compatibility, (2) clustering for disposal, and (3) concurrent disassembly operations. An example is provided on a product being disassembled for recycling at a local recycling plant in Canada.     

Selective disassembly sequencing with random operation times in parallel disassembly environment

Selective disassembly sequencing is the problem of determining the sequence of disassembly operations to extract one or more target components of a product. This study considers the problem with random operation times in the parallel disassembly environment in which one or more components can be removed at the same time by a single disassembly operation. After representing all possible disassembly sequences using the extended process graph, a stochastic integer programming model is developed for the objective of minimising the sum of disassembly and penalty costs, where the disassembly costs consist of sequence-dependent set-up and operation costs and the penalty cost is the expectation of the costs incurred when the total disassembly time exceeds a threshold value. A sample average approximation-based solution algorithm is proposed that incorporates an optimal algorithm to solve the sample average approximating problem under a given set of scenarios for disassembly operation times. The algorithm is illustrated with a hand-light case and a large-sized random instance, and the results are reported.     

An optimal algorithm for selective disassembly sequencing with sequence-dependent set-ups in parallel disassembly environment

This study considers selective disassembly sequencing in parallel disassembly environment in which two or more components can be removed by a single disassembly operation. The problem is to determine the sequence of disassembly operations to extract multiple target components while satisfying the precedence relations among disassembly operations. The objective is to minimise the sum of sequence-dependent set-up and operation costs. An integer programming model is developed after representing all possible disassembly sequences using an extended process graph, and then an optimal branch and bound algorithm is proposed that incorporates the methods to obtain the lower and upper bounds as well as a dominance property to reduce the search space. To show the performance of the algorithm, computational experiments are done on various random instances, and the results are reported. In particular, it is shown from the test results that the optimal algorithm requires much shorter computation times than a competitive commercial software package. Finally, a case is reported to illustrate the extended process graph and the solution algorithm.     

Disassembly sequence generation: A Petri net based heuristic approach

The generation of a set of optimal or near-optimal disassembly sequences is an important task associated with the study of disassembly planning. This task encapsulates actions at the required disassembly depth to reach maximum net revenue while satisfying economic constraints. The Petri net modelling, combined with heuristic search procedures developed in this study, offers an efficient procedure for disassembly sequence generation. The heuristic generates and searches a partial reachability graph to arrive at an optimal or near-optimal disassembly sequence based on the firing sequence of transitions of the Petri net model. The proposed methodology reduces the search space in two areas: (1) pruning the disassembly tree (DT) and, (2) selective tracking of the reachability graph. The efficacy of the proposed methodology is demonstrated by using two examples from the literature.     

Applying data mining technique to disassembly sequence planning: a method to assess effective disassembly time of industrial products

Design for end-of-life and design for disassembly are enabling design strategies for the implementation of business models based on the circular economy paradigm. The paper presents a method for calculating the effective disassembly sequence and time for industrial products. Five steps support designers in defining liaisons and related properties and precedence among components with the aim to calculate the best disassembly sequence and time. The effective disassembly time is computed considering the actual conditions of a product and its components (e.g. deformation, rust and wear) using corrective factors. This aspect represents the main contribution to the state of the art in the field of design for disassembly. The corrective factors are derived from a specific data mining process, based on the observation of real de-manufacturing activities. The proposed approach has been used for calculating the disassembly times of target components in a washing machine and in a coffee machine. The case studies highlight the method reliability of both: definition of time-effective disassembly sequences and assessment of effective disassembly times. In particular, a comparison of experimental tests shows a maximum deviation of ?6% for the electric motor of the washing machine and ?3% for the water pump of the coffee machine.     

Evaluation model and algorithm of product disassembly process with stochastic feature

Disassembly planning is considered as the optimization of disassembly sequences with the target of the shortest disassembly time, the lowest disassembly cost, and the minimum disassembly energy consumption. However, obsolete products suffer from the influence of a variety of uncertainties, the disassembly process of products has the strong uncertain feature. Traditionally, to account for this uncertainty, each removal operation or removal task is assumed to be an activity or event with certain probability, and the determination of the optimal path of a disassembly process is merely a probabilistic planning problem based on this assumption. In this article, based on the established stochastic disassembly network graph, combined with different disassembly decision-making criterion, typical stochastic models for disassembly time analysis are developed. In addition, a two-phase approach is proposed to solve the typical stochastic models. Initially, according to different removal probability density functions, disassembly probability density functions of feasible disassembly paths are determined by a time-domain method or frequency-domain method, and additionally, after the disassembly probability density functions have been obtained, the quantitative evaluation of a product disassembly process and stochastic optimization of feasible disassembly paths are realized by a numerical solution method. Finally, a numerical example is illustrated to test the proposed concepts and the effectiveness of the proposed approach.     

Interactive AR-assisted product disassembly sequence planning (ARDIS)

This paper presents a proof-of-concept novel near real-time interactive AR-assisted product disassembly sequence planning system (ARDIS) based on product information, such as interference matrix and 3D models. The system is developed using Unity and consists of three modules, including an intelligent disassembly sequence planning module, an automatic content authoring module and an intuitive augmented reality (AR) user interface (UI) with various features, such as a virtual panel for customisation and an option panel for sequence regeneration. Given the retrieval targets specified by a user, optimised disassembly sequences are computed using an evolutionary computing algorithm. For the sequences computed, the respective AR disassembly instruction sequences, such as 2D text instructions and animated 3D models, are generated dynamically based on a taxonomy that links each disassembly step in a sequence with the corresponding Unity templates that have been created beforehand. Hence, the need for manual authoring to provide AR disassembly guidance is reduced. If necessary, the user can request for alternative disassembly sequences which can be re-computed in near real-time. Several case studies have been carried out to demonstrate and evaluate the performance of the system within the laboratory environment.     

Heuristics for solving disassemble-to-order problems with stochastic yields

Within the realm of reverse logistics, remanufacturing has become renowned as a popular option in many reverse logistics settings. In remanufacturing, firms take back products at the end of their use, disassemble them to obtain components, and reassemble these components into a “good as new” remanufactured product. As a process, remanufacturing requires parts as an input, parts which are gained mostly from recovered products. As the quality of the returned products are not known in advance, likewise the amount of good quality parts recovered from the returned product is subject to uncertainty. In this paper, we develop two heuristics of different sophistication which take into consideration that the yields of disassembly are stochastic. The methodology is further illustrated with a numerical example, and performance of the heuristics is examined through a performance study. The performance study indicates excellent performance for the more sophisticated heuristic, but also reveals under which conditions the more simple heuristic can be recommended for application.     

An improved gravitational search algorithm for profit-oriented partial disassembly line balancing problem

Disassembly is indispensable to recycle and remanufacture end-of-life products, and a disassembly line-balancing problem (DLBP) is studied frequently. Recent research on disassembly lines has focused on a complete disassembly for optimising the balancing ability of lines. However, a partial disassembly process is widely applied in the current industry practice, which aims at reusing valuable components and maximising the profit (or minimising the cost). In this paper, we consider a profit-oriented partial disassembly line-balancing problem (PPDLBP), and a mathematical model of this problem is established, which is to achieve the maximisation of profit for dismantling a product in DLBP. The PPDLBP is NP-complete since DLBP is proven to be a NP-complete problem, which is usually handled by a metaheuristics. Therefore, a novel efficient approach based on gravitational search algorithm (GSA) is proposed to solve the PPDLBP. GSA is an optimisation technique that is inspired by the Newtonian gravity and the laws of motion. Also, two different scale cases are used to test on the proposed algorithm, and some comparisons with the CPLEX method, particle swarm optimisation, differential evolution and artificial bee colony algorithms are presented to demonstrate the excellence of the proposed approach.     

Joint decision-making on automated disassembly system scheme selection and recovery route assignment using multi-objective meta-heuristic algorithm

Green treatment on Waste Electrical and Electronic Equipmenthas increasingly attracted attention due to its significant environmental benefits and potential recovery earnings. Automated disassembly has been regarded as a powerful solution to enable more efficient recovery operations. Although numerous studies have contributed to the issues of disassembly, there are few researches that focus on decision model for selecting disassembly system scheme and recovery route in automated disassembly. In this paper, we propose a two-phase joint decision-making model to address this problem with the goal of balancing disassembly profit with environmental impact. First, we establish a multi-objective optimisation model to obtain the Pareto optimal recovery routes for each automated disassembly system scheme. Both recovery profit and energy consumption are evaluated for multi-station disassembly system. We design a multi-objective hybrid particle swarm optimisation algorithm based on symbiotic evolutionary mechanism to solve the proposed model. Then, we compare the Pareto optimal solutions of all the system schemes using a fuzzy set method and identify the best scheme. Finally, we conduct real case studies on the automated disassembly of different waste electric metres. The results demonstrate the superiority of automated disassembly and validate the effectiveness of our proposed model and algorithm.     

Mathematical model and solution algorithms for selective disassembly sequencing with multiple target components and sequence-dependent setups

This study considers selective disassembly sequencing under the sequential disassembly environment in which one component is obtained at each disassembly operation. The problem is to determine the sequence of disassembly operations to obtain multiple target components of a used or end-of-life product for the purpose of repair, reuse, remanufacturing, disposal, etc. In particular, we consider sequence-dependent setups in which setup costs depend on the disassembly operation just completed and on the operation to be processed. The problem is represented as a disassembly precedence graph and then a new integer programming model is suggested for the objective of minimising the total disassembly cost. After it is proved that the problem is NP-hard, we suggest two types of heuristics: (1) branch and fathoming algorithm for small-to-medium-sized instances; and (2) priority-rule-based algorithm for large-sized instances. A series of computational experiments, i.e., effectiveness of the new integer programming model and performances of the two heuristic types, were done on various test instances, and the results are reported. In addition, to show the applicability of the mathematical model and the solution algorithms, a case study is reported on an end-of-life electronic calculator.     

Effects of an Inaccurate Sorting Procedure on Optimal Procurement and Production Decisions in a Remanufacturing System

Under the uncertainty of market demand and quality of returns, sorting prior to disassembly is effective for timely obtaining information about the remanufacturability of used products. In this article, we assume that the remanufacturable fraction of used products is a random variable and introduce an inaccurate sorting procedure of used products prior to disassembly. Then, three two-stage optimization models are formulated to maximize the expected profits of a remanufacturer in a single period with used products and/or new parts as inputs to meet the stochastic market demand. Moreover, the article provides a case study to explore the optimal decisions under different scenarios and analyzes the effects of parameters, such as the unit disassembly cost, unit sorting cost, and proportion of sorting errors. Finally, the results indicate that whether remanufacturing with sorting is more profitable than that without sorting mainly depends on the sorting accuracy and the relative value between disassembly cost and sorting cost. When considering or not considering a long lead time of new parts, the effects of sorting errors on procurement policies are different. A long lead time will result in lower expected profits. Finally, the diverse types of classification errors have different influences on procurement policies and corresponding expected profit.     

An improved co-evolutionary algorithm for green manufacturing by integration of recovery option selection and disassembly planning for end-of-life products

There is a strong need for recovery decision-making for end-of-life (EOL) products to satisfy sustainable manufacturing requirements. This paper develops and tests a profit maximisation model by simultaneously integrating recovery option selection and disassembly planning. The proposed model considers the quality of EOL components. This paper utilises an integrated method of multi-target reverse recursion and partial topological sorting to generate a feasible EOL solution that also reduces the complexity of genetic constraints handling. In order to determine recovery options, disassembly level and disassembly sequence simultaneously, this paper develops an improved co-evolutionary algorithm (ICA) to search for an optimal EOL solution. The proposed algorithm adopts the evolutionary mechanism of localised interaction and endosymbiotic competition. Further, an advanced local search operator is introduced to improve convergence performance, and a global disturbance strategy is also suggested to prevent premature convergence. Finally, this paper conducts a series of computational experiments under various scenarios to validate the meta-heuristic integrated decision-making model proposed and the superiority of the developed ICA. The results show that the proposed approach offers a strong and flexible decision support tool for intelligent recovery management in a ubiquitous information environment. We discuss the theoretical and practical contributions of this paper and implications for future research.