Disassembly Sequencing Using Technological Data

2002 ◽  
pp. 347-354 ◽  
Author(s):  
N. Rejneri ◽  
Jean-Claude Leon ◽  
G. Debarbouille
Keyword(s):  
Technological Data ◽  
Disassembly Sequencing

Disassembly Sequencing Using Tabu Search

2015 ◽  
Vol 82 (1) ◽  
pp. 69-79 ◽  
Author(s):  
Mohammad Alshibli ◽  
Ahmed El Sayed ◽  
Elif Kongar ◽  
Tarek M. Sobh ◽  
Surendra M. Gupta
Keyword(s):  
Tabu Search ◽  
Disassembly Sequencing

Disassembly Sequencing Problem

2007 ◽  
pp. 251-267 ◽  
Author(s):  
Mukul Tripathi ◽  
Shubham Agrawal ◽  
M Tiwari
Keyword(s):  
Sequencing Problem ◽  
Disassembly Sequencing

A Heuristic Approach for Disassembly Sequencing Problem for Robotic Disassembly Operations

2012 ◽  
pp. 438-447
Author(s):  
Ahmed ElSayed ◽  
Elif A. Kongar ◽  
Surendra M. Gupta
Keyword(s):  
Genetic Algorithm ◽  
Value Added ◽  
Recovery Process ◽  
Electronic Products ◽  
Sequencing Problem ◽  
Material Recovery ◽  
Disassembly Sequence ◽  
Robotic Disassembly ◽  
Efficient Recovery ◽  
Disassembly Sequencing

Electronic products enter the waste stream rapidly due to technological enhancements. Their parts and material recovery involve significant economic and environmental gain. To regain the value added to such products a certain level of disassembly may be required. Disassembly operations are often expensive and the complexity of determining the best disassembly sequence increases as the number of parts in a product grows. Therefore, it is necessary to develop methodologies for obtaining optimal or near optimal disassembly sequences to ensure efficient recovery process. To that end, this chapter introduces a Genetic Algorithm based methodology to develop disassembly sequencing for end-of-life products. A numerical example is presented to provide and demonstrate better understating and functionality of the algorithm.

Oil & Gas Production Main Technological Data Forecast

2008 ◽  
Author(s):  
T.Sh. Salavatov ◽  
A.A. Suleymanov ◽  
E.A. Panakhov ◽  
A.A. Suleymanov ◽  
E.A. Panakhov
Keyword(s):  
Gas Production ◽  
Technological Data ◽  
Oil Gas

GEN-PRODIS Approach for Determining Total Cost in Disassembly Sequencing Problem

2015 ◽  
Vol 813-814 ◽  
pp. 1165-1169
Author(s):  
B. Josephin Sajo ◽  
J. Jayaprakash
Keyword(s):  
Life Cycle ◽  
Life Cycle Cost ◽  
Product Life Cycle ◽  
Product Usage ◽  
Disassembly Sequence Planning ◽  
Total Cost ◽  
Sequencing Problem ◽  
Disassembly Sequence ◽  
Disassembly Sequencing ◽  
Product Disassembly

Disassembly sequence planning not only reduces product life cycle cost, but also greatly influences environmental impact. Industrial recycling and remanufacturing involves product disassembly to retrieve the desired parts and/or subassemblies by separating a product into its constituents. Disassembly has recently gained a great deal of attention in the literature due to its role in product recovery. Disassembly sequencing and planning is more challenging than assembly because its terminal goal is not necessarily fixed, but may depend on product usage and market demands for used parts and recycled materials. Moreover, disassembly is accompanied by more uncertainty in system structures and component conditions than is assembly. This paper presents recent methods for sequencing and process planning in disassembly and the applications to industrial products. This research is aimed at determining the optimal disassembly sequence as well as the helps to find the sequence dependent cost.

Disassembly sequencing: A survey

2003 ◽  
Vol 41 (16) ◽  
pp. 3721-3759 ◽  
Author(s):  
A. J. D. Lambert
Keyword(s):  
Disassembly Sequencing

Methods for optimum and near optimum disassembly sequencing

2008 ◽  
Vol 46 (11) ◽  
pp. 2845-2865 ◽  
Author(s):  
A. J. D. Lambert ◽  
Surendra M. Gupta
Keyword(s):  
Disassembly Sequencing

scholarly journals Approximating the Effective Protection Coefficient without Reference to Technological Data

1988 ◽  
Vol 2 (3) ◽  
pp. 349-363
Author(s):  
Patrick J. Conway ◽  
Malcolm Bale
Keyword(s):  
Effective Protection ◽  
Technological Data

scholarly journals Manufacturing Data Uncertainties Propagation Method in Burn-Up Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
Thomas Frosio ◽  
Thomas Bonaccorsi ◽  
Patrick Blaise
Keyword(s):  
Uncertainty Propagation ◽  
Plate Thickness ◽  
Nuclear Data ◽  
Monte Carlo Sampling ◽  
Uranium Enrichment ◽  
Statistical Behavior ◽  
Technological Data ◽  
Core Areas ◽  
Manufacturing Tolerances ◽  
Influential Parameters

A nuclear data-based uncertainty propagation methodology is extended to enable propagation of manufacturing/technological data (TD) uncertainties in a burn-up calculation problem, taking into account correlation terms between Boltzmann and Bateman terms. The methodology is applied to reactivity and power distributions in a Material Testing Reactor benchmark. Due to the inherent statistical behavior of manufacturing tolerances, Monte Carlo sampling method is used for determining output perturbations on integral quantities. A global sensitivity analysis (GSA) is performed for each manufacturing parameter and allows identifying and ranking the influential parameters whose tolerances need to be better controlled. We show that the overall impact of some TD uncertainties, such as uranium enrichment, or fuel plate thickness, on the reactivity is negligible because the different core areas induce compensating effects on the global quantity. However, local quantities, such as power distributions, are strongly impacted by TD uncertainty propagations. For isotopic concentrations, no clear trends appear on the results.

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