scholarly journals Disassembly Sequence Planning for Product Maintenance

2012 ◽  
Author(s):  
Yongtao Luo ◽  
Qingjin Peng
Keyword(s):  
Operation Time ◽  
Product Representation ◽  
Disassembly Sequence Planning ◽  
Product Operation ◽  
Disassembly Sequence ◽  
Design And Manufacturing ◽  
Selective Disassembly ◽  
The Cost ◽  
Disassembly Plan ◽  
Disassembly Planning

Environmental and sustainable issues have brought more and more attention to industries in product design and manufacturing. It is important for a product to meet its lifecycle requirements in repairing, replacing and recycling. Disassembly is required in product maintenance and recycling. An efficient disassembly plan can reduce the cost of product maintenance and minimize the product repair time. This paper introduces an efficient method for selective disassembly planning for the need of product maintenance and recycling to reduce the product operation time and cost. The method is based on an efficient product representation and effective sequence searching. It considers the product structure, removing direction of components, operation constraints and complex in the product representation and sequencing planning. An example is used to verify the proposed method. Challenges and further work are also discussed.

scholarly journals Research of Partial Destructive based Selective Disassembly Sequence Planning

2015 ◽  
Vol 9 (1) ◽  
pp. 605-612 ◽  
Author(s):  
Zhou Ziqiang ◽  
Dai Guohong ◽  
Zhang Xiangyan ◽  
Hu Chaobin ◽  
Zhang Yongjian
Keyword(s):  
Material Properties ◽  
Nondestructive Method ◽  
Target Component ◽  
Recycling Process ◽  
Disassembly Sequence Planning ◽  
Material Recycling ◽  
Disassembly Sequence ◽  
Study Case ◽  
Selective Disassembly ◽  
The Cost

In the process of recycling EOL (end of life) products, disassembly is an important stage. Moreover, there are several targets for disassembly: reuse, remanufacturing and material recycling. In order to improve the efficiency of disassembly, only the component which need to be reused or remanufactured need to be disassembled with nondestructive method. The rest components which recycled for material can be disassembled with partial destructive method. Namely, partial destructive disassembly is more practical than total disassembly within recycling process for EOL products. In this paper, the generally used partial destructive disassembly methods are analyzed. And several rules are defined for reconstructing the hybrid graph of EOL product. And then, an extended Floyd algorithm is proposed for searching optimized disassembly sequence. With this algorithm, the material properties of parts, connection properties between parts or components are taking into account. By ranking the cost of several possible disassembly path, the optimal disassembly sequence is obtained for target component. At last, a soybean milk machine is used as a study case to verify the former approach.

A hybrid method for optimizing selective disassembly sequence planning in adaptive reuse of buildings

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehran Mahmoudi Motahar ◽  
Seyed Hossein Hosseini Nourzad
Keyword(s):  
Hybrid Method ◽  
Manufacturing Industry ◽  
Adaptive Reuse ◽  
Nsga Ii ◽  
Content Type ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Selective Disassembly ◽  
Disassembly Planning

PurposeA successful adaptive reuse process relies heavily on the strong performance of disassembly sequence planning (DSP), yet the studies in the field are limited to sequential disassembly planning (SDP). Since in sequential disassembly, one component or subassembly is removed with only one manipulator at a time, it can be a relatively inefficient and lengthy process for large or complex assemblies and cannot fully utilize the DSP benefits for adaptive reuse of buildings. This study aims to present a new hybrid method for the single-target selective DSP that supports both sequential and parallel approaches.Design/methodology/approachThis study uses asynchronous parallel selective disassembly planning (aPDP) method, one of the newest and most effective parallel approaches in the manufacturing industry, to develop a parallel approach toward DSP in adaptive reuse of buildings. In the proposed method, three objectives (i.e. disassembly sequence time, cost and environmental impacts) are optimized using the Non-dominated Sorting Genetic Algorithm (NSGA-II).FindingsThe proposed method can generate feasible sequential solutions for multi-objective DSP problems as the sequence disassembly planning for buildings (SDPB) method, and parallel solutions lead to 17.6–23.4% time reduction for understudy examples. Moreover, in disassembly planning problems with more complex relations, the parallel approach generates more effective and time-efficient sequences.Originality/valueThis study introduces the parallel approach for the first time in this field. In addition, it supports both sequential and parallel approaches as a novel strategy that enables the decision-makers to select the optimum approach (i.e. either the parallel or the sequential approach) for DSP. Moreover, a metaheuristic method (i.e. NSGA-II) is adopted as the optimization tool with robust results in the field in which those heuristic methods have only been employed in the past.

Multiple-Target Selective Disassembly Sequence Planning With Disassembly Sequence Structure Graphs

2012 ◽  
Author(s):  
Shana Smith ◽  
Wei-Han Chen
Keyword(s):  
Model Complexity ◽  
Multiple Target ◽  
Sequence Structure ◽  
Specific Product ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Single Target ◽  
Disassembly Sequence ◽  
Graph Size ◽  
Selective Disassembly

Modern green products must be easy to disassemble. Selective disassembly is used to access and remove specific product components for reuse, recycling, or remanufacturing. Early related studies developed various heuristic or graph-based approaches for single-target selective disassembly. More recent research has progressed from single-target to multiple-target disassembly, but disassembly model complexity and multiple constraints, such as fastener constraints and disassembly directions, still have not been considered thoroughly. In this study, a new graph-based method using disassembly sequence structure graphs (DSSGs) was developed for multiple-target selective disassembly sequence planning. The DSSGs are built using expert rules, which eliminate unrealistic solutions and minimize graph size, which reduces searching time. Two or more DSSGs are combined into one DSSG for accessing and removing multiple target components. In addition, a genetic algorithm is used to decode graphical DSSG information into disassembly sequences and optimize the results. Using a GA to optimize results also reduces searching time and improves overall performance, with respect to finding global optimal solutions. Case studies show that the developed method can efficiently find realistic near-optimal multiple-target selective disassembly sequences for complex products.

Disassembly Sequence Planning Based on Poisoning Ant Colony Algorithm

2013 ◽  
Vol 572 ◽  
pp. 340-343 ◽  
Author(s):  
Wei Lin Wang ◽  
Xian Jie Shi
Keyword(s):  
Ant Colony Algorithm ◽  
Convergence Property ◽  
Ant Colony ◽  
Optimal Sequence ◽  
Disassembly Sequence Planning ◽  
Disassembly Sequence ◽  
Toxic Environment ◽  
The Cost ◽  
Product Disassembly ◽  
Whole Life Cycle

Disassembly of scrap product is a necessary procedure of products recycling and reusing, which is an important part of whole life cycle of the products. Scrap product disassembly process planning can reduce the cost of disassembly operation. It can also improve operation efficiency and the enterprise competitiveness. A new improved algorithm is developed based on the basic ant colony algorithm, which is poisoning ant colony algorithm. Poisoning ant colony algorithm is applied to realize the search and optimization for feasible disassembly sequence solution set with disassembly root diagram. Finally, the optimal sequence is obtained based on evaluation objective function. Toxic environment and life value are also introduced to this algorithm. The characteristics of the algorithm are preferable search and convergence property.

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