A path-relinking approach for a bi-criteria disassembly sequencing problem

The first crucial step of product recovery is disassembly. Some product disassembly is almost always needed in remanufacturing, recycling, and disposal. Since disassembly tends to be expensive, disassembly sequence planning becomes important in minimizing resources (time and money) invested in disassembly and maximizing the level of automation. A disassembly sequence plan (DSP) is a sequence of disassembly tasks, which begins with a product to be disassembled and terminates in a state where all of the parts of interest are separated. The decision version of the problem of finding the optimal DSP is an NP-complete problem and therefore complex and challenging to solve. Often one has to resort to heuristic and metaheuristic techniques for solving such problems. In this paper, we seek a DSP that addresses two criteria in order. First, we look for a sequence, the cost of which is close to our cost aspiration. Second, we look for a sequence that prioritizes some selected parts to be disassembled as early as possible. We propose a greedy randomized adaptive search procedure (GRASP) and path-relinking-based heuristic methodology specifically developed to solve such bi-criteria type of disassembly problem. An example is considered to illustrate the implementation of the methodology. Conclusions drawn include the consistent generation of near-optimal solutions, the ability to preserve precedence, the superior speed of the metaheuristic, and its practicality due to its ease of implementation.     

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.     

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.     

基于中断向量表重构的固件代码反汇编技术

反汇编是固件代码逆向分析的重要研究内容,其正确性直接影响固件代码逆向分析的准确性。固件代码结构具有特殊性,针对上层应用程序的反汇编算法大都不能直接用于固件代码的反汇编。中断向量表是固件代码的重要组成部分,从中断向量开始对中断服务子程序进行反汇编,可提高固件代码反汇编的精度。通过对固件代码结构特点的研究分析,介绍了中断向量表的重构方法,提出了一种基于中断向量表重构的固件代码反汇编技术。经测试分析,与传统的静态反汇编技术相比,基于中断向量表重构的固件代码反汇编技术不仅能够对固件代码中的主函数进行反汇编,还能够对中断服务子程序进行反汇编,反汇编精度平均提高了8.72%。     

一种基于中断处理机制的动态反汇编算法

传统的反汇编是静态进行的,它难以处理逻辑陷阱、花指令、代码中的数据、动态控制流等问题,这就导致其最终的反汇编结果可能不完整,存在错误.一段特意设计的代码,可以用动态控制流替代静态控制流;一段特意设计的花指令,可以让静态控制流关系任意复杂,这都将使静态反汇编分析难以进行.让代码运行起来,动态地进行反汇编,可以解决上述静态反汇编遇到的问题.结合计算机的中断处理机制,使得代码的实际执行路径是可知的.针对实际执行到的计算机指令进行反汇编,确保了反汇编结果的准确性;反复执行代码,在时间上进行积累,可以确保反汇编结果的完整性不断增加并趋于完整.     

Disassembly sequence structure graphs: An optimal approach for multiple-target selective disassembly sequence planning

Modern green products must be easy to disassemble. Specific target components must be accessed and removed for repair, reuse, recycling, or remanufacturing. Prior studies describe various methods for removing selective targets from a product. However, solution quality, model complexity, and searching time have not been considered thoroughly. The goal of this study is to improve solution quality, minimize model complexity, and reduce searching time. To achieve the goal, this study introduces a new ‘disassembly sequence structure graph’ (DSSG) model for multiple-target selective disassembly sequence planning, an approach for creating DSSGs, and methods for searching DSSGs. The DSSG model contains a minimum set of parts that must be removed to remove selected targets, with an order and direction for removing each part. The approach uses expert rules to choose parts, part order, and part disassembly directions, based upon physical constraints. The searching methods use rules to remove all parts, in order, from the DSSG. The DSSG approach is an optimal approach. The approach creates a high quality minimum-size model, in minimum time. The approach finds high quality, practical, realistic, physically feasible solutions, in minimum time. The solutions are optimized for number of removed parts, part order, part disassembly directions, and reorientations. The solutions remove parts in practical order. The solutions remove parts in realistic directions. The solutions consider contact, motion, and fastener constraints. The study also presents eight new design rules. The study results can be used to improve the product design process, increase product life-cycle quality, and reduce product environmental impact.     

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。     

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.     

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.     

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.     

Enhancing disassembly and recycling planning using life-cycle analysis

Both the general public and governmental agencies highly prioritize resource optimization (energy and material) and environmental issues such as ozone, acid rain and global warming in the life-cycle context. Disassembly and recycling are also increasingly important in most industrial countries due to the significant increase in the quantity of used products being discarded. Disassembly of used products has been recognized as necessary to make recycling economically viable in current state-of-the-art reprocessing technology. This emerging trend requires incorporating environmental considerations into design strategies. This study presents a graph-based heuristic method for disassembly analysis of end-of-life products, which incorporates the Eco-Design concept. Product components and their assembly relationships from the bill of material BOM are adopted to split the graph into sub-graphs denoting modular sub-assemblies. The life-cycle analysis LCA is then used to analyze disassembly trees, from which a disassembly sequence can be derived. Designers can use the analytical results to evaluate the dis-assemblability and recyclability of products when they are designed.     

Linear programming in disassembly/clustering sequence generation

Based on earlier research on optimal disassembly sequence generation, notably graphically supported search methods, a method for solving general optimal disassembly sequence generation problems by linear programming has been developed and described. This method puts no serious restriction to the size of the model. It is adaptable to changes in model structures and constraints, and it is even not restricted to divergent operations, such as disassembly. This is demonstrated by solving the combined disassembly/clustering problem. Clustering is the combination of different disassembly products to categories that can be considered — and sold — as a single product, e.g. steel parts.The theoretical model is validated and clarified by its application to some standard problems from literature.     

A scatter search approach to the optimum disassembly sequence problem

One of the most difficult problems faced by OR researchers in the reverse logistics field consists in determining an efficient disassembly sequence for a product at its end of life. In this paper, a scatter search (SS) metaheuristic is presented aiming to deal with the optimum disassembly sequence problem for the case of complex products with sequence-dependent disassembly costs, and assuming that only one component can be released at each time. The algorithm was tested over a set of 48 products consisting of 25, 50, 75 and 100 right-angled parallelepiped components, connected by different kinds of joints. A feasible disassembly sequence, consisting on the reverse of the assembly sequence, is always usable. Comparing by using some statistical analysis the costs of the sequence proposed by the SS algorithm with those corresponding to the reverse order, efficiency of the metaheuristic is shown up.     

Algorithmic selection of a disassembly sequence of a component by a wave propagation method

The design of a product for its entire life-cycle is becoming more and more important. Nowadays, a product is designed with significant considerations for its manufacturability, serviceability, its functionality and even for its disassemblability. This is due to the fact that the modern consumers demand products that are not only functional, but also reliable, easy to repair and also environmentally friendly.For maintenance and re-use, the operation sequencing in disassembly process planning needs of reversible operation selecting.We present a new wave propagation disassembly algorithm of the determined component of a product; the given data are the immediate predecessors of each component.We show that the solution exists and that she is unique in the following conditions: the binary matrix of the immediate predecessors is non-symmetrical and the data does not contain a cycle. A tracked down cycle method in the data is also proposed.     

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.     

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.     

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).