Mathematical model and solution algorithms for selective disassembly sequencing with multiple target components and sequence-dependent setups

2013 ◽  
Vol 51 (16) ◽  
pp. 4997-5010 ◽  
Author(s):  
Hee-Jong Han ◽  
Jae-Min Yu ◽  
Dong-Ho Lee
Keyword(s):  
Mathematical Model ◽  
Multiple Target ◽  
Solution Algorithms ◽  
Sequence Dependent ◽  
Selective Disassembly ◽  
Disassembly Sequencing

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.

scholarly journals Quantitative regulation of the dynamic steady state of actin networks

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Angelika Manhart ◽  
Téa Aleksandra Icheva ◽  
Christophe Guerin ◽  
Tobbias Klar ◽  
Rajaa Boujemaa-Paterski ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Steady State ◽  
Heterogeneous Networks ◽  
In Silico ◽  
Actin Network ◽  
Network Nodes ◽  
Actin Networks ◽  
Cell Functions ◽  
Selective Disassembly

Principles of regulation of actin network dimensions are fundamentally important for cell functions, yet remain unclear. Using both in vitro and in silico approaches, we studied the effect of key parameters, such as actin density, ADF/Cofilin concentration and network width on the network length. In the presence of ADF/Cofilin, networks reached equilibrium and became treadmilling. At the trailing edge, the network disintegrated into large fragments. A mathematical model predicts the network length as a function of width, actin and ADF/Cofilin concentrations. Local depletion of ADF/Cofilin by binding to actin is significant, leading to wider networks growing longer. A single rate of breaking network nodes, proportional to ADF/Cofilin density and inversely proportional to the square of the actin density, can account for the disassembly dynamics. Selective disassembly of heterogeneous networks by ADF/Cofilin controls steering during motility. Our results establish general principles on how the dynamic steady state of actin network emerges from biochemical and structural feedbacks.

scholarly journals О постановках обратных задач

2020 ◽  
pp. 203-206
Author(s):  
Yurii Menshikov
Keyword(s):  
Mathematical Model ◽  
Inverse Problem ◽  
Approximate Solution ◽  
Inverse Problems ◽  
Solution Algorithms ◽  
The Mathematical Model

Some possible options for the formulation of inverse problems are considered. The ultimate research goals in these cases determine the algorithms for the approximate solution of the inverse problem and allow one to correctly interpret these solutions. Two main statements of inverse problems considered: inverse problems of synthesis and inverse problems of measurement. It is shown that in inverse synthesis problems one should not take into account the error of the mathematical model. In addition, it is possible in these cases to synthesize approximate solution algorithms that do not have a regularizing property. Examples of practical problems considered.

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