Research on selective disassembly sequence planning for Repair

IEEE/CAA Journal of Automatica Sinica, 2021

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be handled. They contain reusable resource that can be recycled and used to generate desired economic benefits. Therefore, performing their efficient disassembly is highly important in green manufacturing and sustainable economic development. Their typical examples are electronic appliances and electromechanical/mechanical products. This paper presents a survey on the state of the art of disassembly sequence planning. It can help new researchers or decision makers to search for the right solution for optimal disassembly planning. It reviews the disassembly theory and methods that are applied for the processing, repair, and maintenance of obsolete/discarded products. This paper discusses the recent progress of disassembly sequencing planning in four major aspects: product disassembly modeling methods, mathematical programming methods, artificial intelligence methods, and uncertainty handling. This survey should stimulate readers to be engaged in the research, development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

Applications of Design and Implementation, 2012

End-of-life (EOL) processing options include reuse, remanufacturing, recycling and proper disposal. In almost all cases, a certain level of disassembly may be required due to possible changes in the original product structure. Thus, finding an optimal or near optimal disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases as the number of parts of the product grow, an efficient methodology is reuired for disassembly sequencing. In this paper, we present a Genetic Algorithm for disassembly sequencing of EOL products. A numerical example is provided to demonstrate the functionality of the algorithm.

Nigerian Journal of Technology, 2021

The life cycle of most complex engineering systems is greatly a function of maintenance. Generally, most maintenance operation usually requires the removal of failed part. Disassembly sequence planning is an optimization program that seeks to identify the optimal sequence for the removal of the failed part. Most studies in this area usually, use single constraint matrix while implementing varied complex algorithm to identify the optimal sequence that saves time associated with carrying out maintenance operation. The used of single constraint matrix typically has the drawback of computer higher storage requirement as well as time consumption. To address this problem, this study proposes Multi-Level Constraint Matrix Ant Colony Algorithm (MLCMACA). MLCMACA efficiency was validated using complex aircraft landing gear systems in comparison with genetic algorithms. The result shows MLCMACA superior performance from the perspective of reduced search time and faster tracking of optimal dis...

Journal of Engineering Design, 2007

Optimizing the disassembly sequence of mechanical systems is very useful in order to improve maintenance and recycling activities (i.e. to reduce costs, times and number of operations). A new virtual disassembly environment, based on two different algorithms, is presented. The first analyses the physical constraints that oppose the movement of the mechanical element: starting from its threedimensional computer-aided design representation, it creates the 'AND/OR' graph of a mechanical assembly that represents the space of disassembly solutions. The second algorithm, using a representation based upon binary trees, allows the automatic exploration of the set of all possible sequences. Among these, the optimal one can be identified, choosing a specific target function. The developed methodology provides the theoretical basis for the creation of a computer-aided design tool able to evaluate the ability to disassemble a mechanical complex during the early design phase.

IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 2000

The end-of-life (EOL) disassembly sequencing problem (DSP) has become increasingly important in the process of handling EOL products. This paper proposes a solution procedure for the "EOL DSP"; the procedure is based on a novel soft-computing algorithm that utilizes modified "simplified swarm optimization," and the procedure combines the precedence preservative operator, feasible solution generator, self-adaptive parameter control, and repetitive pairwise exchange procedures. By taking into consideration the non-deterministic polynomial time (NP)-complete nature of the problem, the proposed algorithm efficiently seeks the optimal disassembly sequence with a novel approach; this approach involves reducing the initial solution space and using a combination of soft-computing algorithms for achieving higher computational efficiency and solution quality. The results presented in this paper show that the proposed algorithm outperforms the existing algorithms in terms of solution quality achieved in a limited computation time.

Environmentally Conscious Manufacturing II, 2002

When a product reaches it's end-of-life, there are several options available for processing it including reuse, remanufacturing, recycling, and disposing (the least desirable option). In almost all cases, a certain level of disassembly may be necessary. Thus, finding an optimal (or near optimal) disassembly sequence is crucial to increasing the efficiency of the process. Disassembly operations are labor intensive, can be costly, have unique characteristics and cannot be considered as reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with the increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly process planning be developed. In this paper, we present a genetic algorithm for disassembly process planning. A case example is considered to demonstrate the functionality of the algorithm.

OR Spectrum, 2007

One of the steps in a reverse supply chain is to disassemble the recovered products. The design of such disassembly operations is more complex than that of classical assembly lines. A new GRASP algorithm is presented in this paper for solving Disassembly Sequence Planning, i.e. the determination of the sequence for disassembling component parts. The problem has a combinatorial structure and a large number of constraints. Moreover, checking the feasibility of each solution element is time consuming. The proposed approach is successfully tested against previous best known solutions for a set of public benchmark problems.

Computers & Industrial Engineering, 1998

Recycling and remanufacturing are important forms of product/material recovery which involve product disassembly to retrieve the desired parts and/or subassemblies. Disassembly is a systematic method for separating a product into its constituent parts, components or other groupings. Efficient disassembly requires development of disassembly sequence plans (DSPs). Generating DSPs describing the sequence of parts during disassembly is not a trivial problem since DSP generation is described to be NP-complete. Further complicating matters is the presence of a high degree of uncertainty due to upgrading/downgrading of the product during its use by the customers and defects occurring either when in use or during disassembly. In this paper, we address the uncertainty related difficulties in disassembly sequence planning. To this end, we present a methodology to develop a framework for dealing with uncertainty in DSP implementation and demonstrate it using a simple example.

Sādhanā, 2021

Disassembly is one of the essential operations in manufacturing to recover the useful parts of the product after End of Life (EOL). Moreover, by generating an optimal disassembly sequence, the time to dismantle the product will be reduced, and in turn, cost also reduces. However, achieving an optimal disassembly sequence is not an easy task as it is an NP-hard combinatorial problem. Many researchers followed different approaches like mathematical, knowledge-based and artificial intelligence (AI)-based methods to generate optimal disassembly sequences. Most of the researchers concentrate on generating the optimal disassembly sequence, but only a few of them discuss the disposal of the parts after EOL. It is very much essential to consider the type of disposal that has to follow the individual components after dismantle to reduce the effect on the environment due to parts of the EOL product. In this research work, a stability graph cut-set method is applied to generate optimal disassembly sequences by considering the minimum number of directional changes as a fitness equation. In the proposed methodology, a stability graph is formulated for the considered assembly to apply cut-set rules for generating optimal assembly sequences. Later, the reverse of the obtained optimal assembly sequences is followed to generate the optimal disassembly sequences. In this strategy, along with the generation of optimal disassembly sequences, the type of disposal (like landfill, incineration and recycling) that has to follow for the individual parts is also discussed using a SOLIDWORKS sustainable tool. The proposed stability graph cut-set method is validated using an eleven-part punching machine assembly to generate the optimal disassembly sequences; also the type of disposal that has to follow for each part after dismantle is discussed. Moreover, the proposed methodology is compared to the well-known algorithms [genetic algorithm (GA) and ant colony optimization (ACO) algorithm] in terms of the number of iterations, the number of optimal disassembly sequences generated and fitness value to check the performance of the algorithm.

Computers & Operations Research, 2008

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.