Disassembly sequence planning in a disassembly cell context

In this paper a two-phase approach is proposed for determining the optimal disassembly sequence when the disassembly system has a cellular configuration. Operations are first grouped into cells based on the resources they require with the goal of minimizing machine acquisition costs. The aim is to group together those operations that use similar equipment in order to achieve good utilization levels of such equipment. A maximum cell size may be imposed. Once the cells have been formed and the operations have been assigned to them, a metaheuristic algorithm (namely GRASP) is used to search for the disassembly sequence for each product that leads to the minimum number of intercellular movements. To account for uncertainty regarding the condition in which the product may arrive, each disassembly task is assumed to be required with a certain probability, regardless of the other tasks. AND/OR precedence relations among the disassembly tasks are also considered. The proposed approach is illustrated on a randomly generated disassembly problem.     

基于信息表和拆卸树的设备维修拆卸工序规划

研究了设备维修拆卸工序的规划问题。提出了拆卸优先度的概念,并给出了使用拆卸优先度表示设备维修拆卸信息的方法。对指定的拆卸目标部件,为了加快遗传算法的寻优过程,提出了由信息表生成拆卸树的方法以及拆卸树的裁剪方法,以提供合适的初始种群。应用例子表明了提出的方法的可行性和有效性。     

Disassembly information model incorporating dynamic capabilities for disassembly sequence generation

Industrial recycling and reusing is becoming more and more important due to the environmental and economic pressures. It involves disassembly activities to retrieve all the parts or selected parts. An information modeling for the disassembly and optimal disassembly sequence generation based on the information model becomes critical. Unlike the traditional graph based representation of product structure, this paper introduces an efficient and machine readable disassembly information model and then discusses a linear programming based optimization model for obtaining the optimal disassembly sequence from the proposed disassembly information model. A key feature of this approach is the incorporation and use of dynamic capabilities in its information model processing technique. Dynamic capabilities are added into the information model to handle state-dependent information such as parts' disassembly directions which may change after each disassembly operation. The overall information model is built in UML, and dynamic capabilities are represented as events in UML. The proposed method has been illustrated using an electrical–mechanical device.     

Disassembly sequence planning using a Flatworm algorithm

Disassembly Sequence Planning (DSP) refers to a disassembly sequence based on the disassembly properties and restrictions of the product parts that meets the benefit goal. This study aims to reduce the number of changes in disassembly direction and disassembly tools so as to reduce the disassembly time. This study proposes a novel Flatworm algorithm that evolves through the regenerative properties of the flatworm. It is similar to the evolutionary concept of genetic algorithms, with evolution as the main idea, but without crossover, mutation or replication mechanisms in the evolutionary processes. Instead, it is based upon the characteristics of the growth, fracture and regeneration mechanisms of the flatworm. The Flatworm algorithm features a variety of disassembly combinations and excellent mechanisms to avoid the local optimal solution. In particular, it has the advantage of keeping a good disassembly combination from being destroyed. In this study, it is compared with two genetic algorithms and two ant colony algorithms and tested in three examples of different complexity: a ceiling fan, a printer, and 150 simulated parts. The solution searching ability and execution time are compared upon the same evaluation standard. The test results demonstrate that the novel Flatworm algorithm proposed in this study is superior to the two genetic algorithms and ant colony algorithms in solution quality.     

A case-based reasoning approach to planning for disassembly

With recycling regulations, resource conservation needs and an increased awareness of the state of the environment by both the consumer and the producer, many companies are establishing disassembly plants and developing product designs that specifically facilitate disassembly. Once disassembled, the items can be reused, recycled or discarded. One can identify two distinct aspects of the disassembly problem: design for disassembly (DFD) and planning for disassembly (PFD). The goal of DFD is to design products that are easy to disassemble. On the other hand, the objective of PFD is to identify efficient sequences to disassemble products. This paper focuses on the PFD aspect of disassembly. Because there could be many ways to disassemble a given product, PFD knowledge is accumulated by experience. Such knowledge is valuable, and should be captured, saved and reused to solve similar problems that arise in the future. In this paper, we propose case-based reasoning (CBR) as an approach to solve PFD problems. CBR is based on the fundamental principle that problem solving can benefit from solutions to past problems that have been attempted. The technique and issues related to the application of CBR to PFD are presented.     

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.     

An integrated approach to selective-disassembly sequence planning

De-manufacturing (DM) is defined as a process to disassemble certain parts or components from a product. The parts or components are selected for recycling, reuse, maintenance or disposal. Selective-disassembly as the disassembly of the selected parts is a key process in DM. Allowing a partial and non-procedural disassembly sequence in DM, selective-disassembly aims to minimize the number of removals regardless of assembly indenture levels. It is necessary for selective-disassembly to have an effective and optimal sequence planning in order to reduce tremendous time and cost involved in product DM. The Wave propagation (WP) method, a dominant approach to selective-disassembly sequence planning, focuses on topological disassemblability of parts. It is inefficient to achieve the aim because of two missed considerations: tool accessibility to a fastener in non-procedural and partial disassembly, and batch removability to directly access a part for separation or replacement.This paper presents an integrated approach to selective-disassembly sequence planning. The two examples presented here demonstrate that the approach is efficient and practical for DM. The implemented approach can efficiently generate a feasible and near-optimal sequence plan for selective-disassembly, with ensuring both batch disassembly of components and tool accessibility to fasteners.     

A case-based reasoning approach for automating disassembly process planning

One of the first processes for preparing a product for reuse, remanufacture or recycle is disassembly. Disassembly is the process of systematic removal of desirable constituents from the original assembly so that there is no impairment to any useful component. As the number of components in a product increases, the time required for disassembly, as well as the complexity of planning for disassembly rises. Thus, it is important to have the capability to generate disassembly process plans quickly in order to prevent interruptions in processing especially when multiple products are involved. Case-based reasoning (CBR) approach can provide such a capability. CBR allows a process planner to rapidly retrieve, reuse, revise, and retain solutions to past disassembly problems. Once a planning problem has been solved and stored in the case memory, a planner can retrieve and reuse the product's disassembly process plan at any time. The planner can also adapt an original plan for a new product, which does not have an existing plan in case memory. Following adaptation and application, a successful plan is retained in the case memory for future use. This paper presents the procedures to initialize a case memory for different product platforms, and to operate a CBR system, which can be used to plan disassembly processes. The procedures are illustrated using examples.     

A hierarchical approach on assembly sequence planning and optimal sequences analyzing

Assembly sequence planning (ASP) is a critical technology that bridges product design and realization. Deriving and fulfilling of the assembly precedence relations (APRs) are the essential points in assembly sequences reasoning. In this paper, focusing on APRs reasoning, ASP, and optimizing, a hierarchical ASP approach is proposed and its key technologies are studied systematically. APR inferring and the optimal sequences searching algorithms are designed and realized in an integrated software prototype system. The system can find out the geometric APRs correctly and completely based on the assembly CAD model. Combined with the process APRs, the geometric and engineering feasible assembly sequences can be inferred out automatically. Furthermore, an algorithm is designed by which optimal assembly sequences can be calculated out from the immense geometric and engineering feasible assembly sequences. The case study demonstrates that the approach and its algorithms may provide significant assistance in finding the optimal ASP and improving product assembling.     

Disassembly sequence planning using discrete Bees algorithm for human-robot collaboration in remanufacturing

Remanufacturing helps to improve the resource utilization rate and reduce the manufacturing cost. Disassembly is a key step of remanufacturing and is always finished by either manual labor or robots. Manual disassembly has low efficiency and high labor cost while robotic disassembly is not flexible enough to handle complex disassembly tasks. Therefore, human-robot collaboration for disassembly (HRCD) is proposed to flexibly and efficiently finish the disassembly process in remanufacturing. Before the execution of the disassembly process, disassembly sequence planning (DSP), which is to find the optimal disassembly sequence, helps to improve the disassembly efficiency. In this paper, DSP for human-robot collaboration (HRC) is solved by the modified discrete Bees algorithm based on Pareto (MDBA-Pareto). Firstly, the disassembly model is built to generate feasible disassembly sequences. Then, the disassembly tasks are classified according to the disassembly difficulty. Afterward, the solutions of DSP for HRC are generated and evaluated. To minimize the disassembly time, disassembly cost and disassembly difficulty, MDBA-Pareto is proposed to search the optimal solutions. Based on a simplified computer case, case studies are conducted to verify the proposed method. The results show the proposed method can solve DSP for HRC in remanufacturing and outperforms the other three optimization algorithms in solution quality.     

Integrated multi-layer representation and ant colony search for product selective disassembly planning

Selective disassembly plays an important role in product life cycle to meet requirements of the product repairing, reusing and recycling. An efficient disassembly plan is essential to minimize processing time in product maintenance for cost saving. This paper introduces a method for integration of the multi-layer product representation and the optimal search in product selective disassembly planning. The multi-layer representation is based on the product structure formed in product design. The method enables an efficient search for the disassembly sequence. Unlike the existing product representation methods, the multi-layer representation is a dynamic product data model integrated with an ant colony search process for a near optimal solution. Industrial applications have proven the method effectiveness.     

Disassembly sequence planning using component-joint graph and ant colony optimization

Disassembly sequence planning is an important step of mechanical maintenance.This article presents an integrated study about the generation and optimizing algorithm of the disassembly sequence.Mechanical products are divided into two categories of components and connectors.The article uses component-joint graph to represent assembly constraints,including the incidence constraints are represented by incidence matrix and the interference constraints are represented by interference constraints.The inspiring factor and pheromone matrix are calculated according to assembly constraints.Then the ant generates its own disassembly sequences one by one and updates the inspiring factor and pheromone matrix.After all iterations,the best disassembly sequence planning of components and connectors are given.Finally,an application instance of the disassembly sequence of the jack is presented to illustrate the validity of this method.     

An optimal weather condition dependent approach for emission planning in urban areas

The urban air quality strongly depends on the weather conditions. When unfavorable weather conditions are forecasted, it is desirable to reduce a certain amount of pollutant emissions to maintain a given level of air quality. In this paper, we propose and demonstrate a new approach to find the optimal reduction, and we contribute to the methodology of decision support of short-term emission control. This approach is based on weather forecasts and state-of-the-art 3-dimensional numerical air-quality prediction models by solving an optimal control problem with the emission cuts as the control variables. The objective of the optimal problem is to minimize the total cost due to the emission cuts, subject to feasibility constraints, system-governing model constraints, and target constraints. When a high-resolution numerical model is used as the state constraint, the problem can become a very high dimensional one. A practical approach to solving this problem with high dimension is proposed, based on the adjoint technique.The proposed approach is demonstrated with two computational test cases in Jinan, China for the control of sulfur dioxide. The results show the capability and computational efficiency of the method and suggest a promising potential for emission planning applications based on weather forecasts.     

Human-robot collaboration disassembly planning for end-of-life product disassembly process

The disassembly process is the main step of dealing with End-Of-Life (EOL) products. This process is carried out mostly manually so far. Manual disassembly is not efficient economically and the robotic systems are not reliable in dealing with complex disassembly operations as they have high-level uncertainty. In this research, a disassembly planning method based on human-robot collaboration is proposed. This method employs the flexibility and ability of humans to deal with complex tasks, alongside the repeatability and accuracy of the robot. Besides, to increase the efficiency of the process the components are targeted based on the remanufacturability parameters. First, human-robot collaboration tasks are classified, and using evaluation of components remanufacturability parameters, human-robot collaboration definition and characteristics are defined. To target the right components based on their remanufacturability factors, the PROMETHEE II method is employed to select the components based on Cleanability, Reparability, and Economy. Then, the disassembly process is represented using AND/OR representation and the mathematical model of the process is defined. New optimization parameters for human-robot collaboration are defined and the genetic algorithm was modified to find a near-optimal solution based on the defined parameters. To validate the task classification and allocation, a 6-DOF TECHMAN robot arm is used to test the peg-out-hole disassembly operation as a common disassembly task. The experiments confirm the task classification and allocation method. Finally, an automotive component was selected as a case study to validate the efficiency of the proposed method. The results in comparison with the Particle Swarm algorithm prove the efficiency and reliability of the method. This method produces a higher quality solution for the human-robot collaborative disassembly process.     

Heuristics for demand-driven disassembly planning

Remanufacturing of used products has become accepted as an advantageous disposition option within the field of reverse logistics. Remanufacturing, where a firm takes returned products at the end of their life and disassembles them to obtain parts which are reassembled into “good as new” products, may require so-called demand-driven- disassembly, where a specific amount of returned products must be disassembled to yield parts which are either demanded externally, or used in the remanufacturing operation. While in its simplest form, the solution can be merely calculated, more realistically complex product structures require a more powerful solution method. The first choice, integer programming (IP) can be used to arrive at an optimal solution, with the disadvantage that the time required to solve the problem explodes with increasingly complex product structures and longer time horizons. Another possible method, heuristics which were presented in a previous work on this problem [Taleb and Gupta (Computers & Industrial Engineering 1997; 32(4): 949–61], offer a faster, easier solution with the disadvantage that it is not necessarily optimal, and under certain circumstances may deliver an infeasible result. In this work, this problem was corrected and the heuristic was extended in several important ways to deal with holding costs and external procurement of items. The methodology is illustrated by an example. Another advantage of the heuristic is that it can be programmed into code and executed via spreadsheet application, which will facilitate its application. A performance study reveals that the new heuristic performs quite well for a wide spectrum of randomly generated problem test instances.     

基于扩展干涉矩阵和遗传算法的拆卸序列规划

为高效求解非标准正交轴向上的装配体拆卸序列,将常用的基于全局坐标系的干涉矩阵扩展为包含基于装配体坐标系的全局干涉矩阵与基于零件坐标系的本地干涉矩阵干涉信息的扩展干涉矩阵,丰富了拆卸方向表达的多样性,并通过其检验拆卸序列的几何可拆性.在得到可行拆卸序列的基础上建立了目标函数,利用遗传算法对拆卸序列进行优化.通过一个实例验证了该方法在拆卸方向多样化方面的可行性和有效性.     

基于可拆卸性分析和图方法的目标拆卸序列规划

针对现有目标拆卸序列求解方法在拆卸并行性规划和计算效率上存在不足的情况,提出了一种目标拆卸序列优化算法。基于球面映射、非正交干涉矩阵以及复合拆卸路径判别等方法的运用与改进,研究了零部件可拆卸性分析方法;应用分层图方法,对目标零件进行定位,结合子装配体识别法,去除冗余拆卸步骤,并提高拆卸过程的并行性。通过实例对该方法的有效性进行了验证。     

Disassembly planning of mechanical systems for service and recovery: a genetic algorithms based approach

In a perspective of improving the behavior of a product in its whole life cycle, the efficient planning of the disassembly processes acquires strategic importance, as it can improve both the product’s use phase, by facilitating service operations (maintenance and repairs), and the end-oflife phase, by favoring the recycling ofmaterials and the reuse of components. The present paper proposes an approach to disassembly process planning that supports the search for the disassembly sequence best suited for both aspects, service of the product and recovery at the end of its useful life, developing two different algorithms. Notwithstanding their different purposes, the two algorithms share the typology of modeling on which they operate, and the logical structure according to which the genetic search procedure is developed. The choice of implementing genetic algorithms was prompted by the intrinsic complexity of the complete mathematical solution to the problem of generating the disassembly sequences, which suggests the use of a non-exhaustive approach. As is shown in the results of a set of simulations, both algorithms may be used not only for the purposes related to disassembly process planning but also as supporting tools during the product design phases. This is especially so for the second algorithm, that deals with the problem of a recovery-oriented disassembly through an all-encompassing approach, combining economical and environmental considerations, and extending the evaluations to the whole life cycle of the product. This formulation gives this algorithm and autonomous decisional capacity on both the disassembly level to be reached, and the definition of the optimum recovery plan (i.e., the best destination for the disassembled components, based on some significant properties of them).     

An integrated job shop scheduling and assembly sequence planning approach for discrete manufacturing

This paper proposes an integrated job shop scheduling and assembly sequence planning (IJSSASP) approach for discrete manufacturing, enabling the part processing sequence and assembly sequence to be optimized simultaneously. The optimization objectives are to minimize the total production completion time and the total inventory time of parts during production. The interaction effects between the job shop schedule and the assembly sequence plan in discrete manufacturing are analyzed, and the mathematical models including the objective functions and the constraints are established for IJSSASP. Based on the above, a non-dominated sorting genetic algorithm-II (NSGA-Ⅱ) with a hybrid chromosome coding mechanism is applied to solve the IJSSASP problem. Through the case studies and comparison tests for different scale problems, the proposed IJSSASP approach is verified to be able to improve the production efficiency and save the manufacturing cost of the discrete manufacturing enterprise more effectively.     

GlyphLink: An interactive visualization approach for semantic graphs

Graph analysis by data visualization involves achieving a series of topology-based tasks. When the graph data belongs to a data domain that contains multiple node and link types, as in the case of semantic graphs, topology-based tasks become more challenging. To reduce visual complexity in semantic graphs, we propose an approach which is based on applying relational operations such as selecting and joining nodes of different types. We use node aggregation to reflect the relational operations to the graph. We introduce glyphs for representing aggregated nodes. Using glyphs lets us encode connectivity information of multiple nodes with a single glyph. We also use visual parameters of the glyph to encode node attributes or type specific information. Rather than doing the operations in the data abstraction layer and presenting the user with the resulting visualization, we propose an interactive approach where the user can iteratively apply the relational operations directly on the visualization. We present the efficiency of our method by the results of a usability study that includes a case study on a subset of the International Movie Database. The results of the controlled experiment in our usability study indicate a statistically significant contribution in reducing the completion time of the evaluation tasks.