Genetic Algorithm for Disassembly Process Planning

Disassembly is part of the demanufacturing and it is meant to obtain components and materials from end-of-life products. An essential performance objective of a disassembly process is the benefits it brings, that is the revenue brought by the retrieved parts and material, diminished by the cost of their retrieval operations. A decision must be taken to balance an automatic disassembly line. A well balanced line will decrease the cost of disassembly operations. An evolutionary (genetic) algorithm is used to deal with the multi-criteria optimization problem of the disassembly scheduling.

Computing Research Repository, 2004

The authors propose the implementation of hybrid Fuzzy Logic-Genetic Algorithm (FL-GA) methodology to plan the automatic assembly and disassembly sequence of products. The GA-Fuzzy Logic approach is implemented onto two levels. The first level of hybridization consists of the development of a Fuzzy controller for the parameters of an assembly or disassembly planner based on GAs. This controller acts on mutation probability and crossover rate in order to adapt their values dynamically while the algorithm runs. The second level consists of the identification of the optimal assembly or disassembly sequence by a Fuzzy function, in order to obtain a closer control of the technological knowledge of the assembly/disassembly process. Two case studies were analyzed in order to test the efficiency of the Fuzzy-GA methodologies.

Northeast Decision Sciences Institute Conference, NEDSI 2010, Hilton Alexandria Old Town, Alexandria, VA, March 26 -- March 28, 2010.

IIE Annual Conference Proceedings, 2013

Disassembly is an important activity which directly governs the cost of products. Literature reveals that much of the research has been focused on assembly sequence planning. The disassembly planning problem, however, needs a separate consideration on account of the need for partial disassembly and other constraints. This paper reports the seamless design and implementation of a software system which automatically determines how to disassemble a product completely, given only a geometric description of the assembly. The system is conceptually divided into two stages. The first stage takes the CAD model of the product assembly as input, which generates interference matrices for principal directions (±x, ±y, ±z). A precedence relationship is determined from these interference matrices to aid in the generation of disassembly sequences. In the second stage a user defined fitness function is proposed with the objective of minimizing the number of reorientations and tool changes during disassembly. An enhanced Genetic Algorithm was designed and implemented to generate optimum solution(s) with the help of this function. A module was implemented to visually simulate the optimum disassembly sequence generated by above algorithm. The system was found to efficiently generate logical, optimum disassembly sequences.

The International Journal of Advanced Manufacturing Technology, 2006

At the end-of-life (EOL) of a product, there are several options available for its processing including reuse, remanufacturing, recycling and disposing. 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 the reverse of assembly operations. Since the complexity of determining the best disassembly sequence increases with increase in the number of parts of the product, it is extremely crucial that an efficient methodology for disassembly sequencing be developed. In this paper, we present a genetic algorithm for disassembly sequencing of EOL products. A case example is considered to demonstrate the functionality of the algorithm.

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.

JJMIE, 2011

For the past two decades, increased efforts by both governments and the general public have been enforced through stricter legislations and more awareness to make manufacturing more environmentally conscious. Product refurbishing and component re-use are being applied on a wider scale worldwide. Disassembly, hence, has attracted more attention both in academia and the industry. Concepts and methods for disassembly planning should be further developed to support this new manufacturing environment. A semi-generative macro disassembly process planning approach based on the Traveling Salesperson formulation has been developed and is reported in this paper. Precedence graphs, which depict the precedence relationships between disassembly operations, are being utilized. The problem of generating optimal macro-level process plans is combinatorial in nature and proven NP-hard. Hence, a random-based hill-climbing heuristic based on Simulated Annealing is tailored for this problem. Finally, a realistic case study is presented to illustrate the working of the proposed methodology. The presented method produced good quality suboptimal solutions and is proven efficient in terms of computation time as demonstrated by the obtained results.

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.

Environmental Science & Technology, 2003

With growing interest in recovering materials and subassemblies within consumer products at the end of their useful life, there has been an increasing interest in developing decision-making methodologies that determine how to maximize the environmental benefits of end-oflife (EOL) processing while minimizing costs under variable EOL situations. This paper describes a methodology to analyze how product designs and situational variables impact the Pareto set of optimal EOL strategies with the greatest environmental benefit for a given economic cost or profit. Since the determination of this Pareto set via enumeration of all disassembly sequences and EOL fates is prohibitively time-consuming even for relatively simple products, multi-objective genetic algorithms (GA) are utilized to rapidly approximate the Pareto set of optimal EOL tradeoffs between cost and environmentally conscious actions. Such rapid calculations of the Pareto set are critical to better understand the influence of situational variables on how disassembly and recycling decisions change under different EOL scenarios (e.g., under variable regulatory, infrastructure, or market situations). To illustrate the methodology, a case study involving the EOL treatment of a coffee maker is described. Impacts of situational variables on tradeoffs between recovered energy and cost in Aachen, Germany, and in Ann Arbor, MI, are elucidated, and a means of presenting the results in the form of a multi-situational EOL strategy graph is described. The impact of the European Union Directive regarding Waste Electric and Electronic Equipment (WEEE) on EOL trade-offs between energy recovery and cost was also considered for both locations.

Proceedings of the ASME Design Engineering Technical Conference, 2011

In this study, we propose an intelligent automated disassembly cell for online (real time) selective disassembly. The cell is composed of an industrial robotic manipulator, a camera, range-sensing and component segmentation visual algorithms. The cell prototype allows for robotic sensory-driven disassembly under uncertainty. An online genetic algorithm model for selective disassembly is also proposed for optimal and near/optimal disassembly sequencing.