scholarly journals A novel selective disassembly sequence planning method for adaptive reuse of buildings

2018 ◽  
Vol 183 ◽  
pp. 998-1010 ◽  
Author(s):  
Benjamin Sanchez ◽  
Carl Haas
Keyword(s):  
Adaptive Reuse ◽  
Planning Method ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Selective Disassembly

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.

A multi-layered disassembly sequence planning method to support decision making in de-manufacturing

Sadhana ◽  
2021 ◽  
Vol 46 (2) ◽  
Author(s):  
Gulivindala Anil Kumar ◽  
M V A Raju Bahubalendruni ◽  
V S S Prasad ◽  
K Sankaranarayanasamy
Keyword(s):  
Decision Making ◽  
Planning Method ◽  
Disassembly Sequence Planning ◽  
Sequence Planning ◽  
Disassembly Sequence ◽  
Support Decision Making

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.

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