The selection of optimum maintenance strategy based on ANP integrated with GRA-TOPSIS

2015 ◽  
Vol 8 (2) ◽  
pp. 190 ◽  
Author(s):  
B. Kirubakaran ◽  
M. Ilangkumaran
Keyword(s):  
Maintenance Strategy ◽  
Selection Of

Application of Hybrid VIKOR Model in Selection of Maintenance Strategy

2012 ◽  
Vol 5 (2) ◽  
pp. 59-81 ◽  
Author(s):  
M. Ilangkumaran ◽  
S. Kumanan
Keyword(s):  
Textile Industry ◽  
Maintenance Strategy ◽  
Analytic Hierarchy ◽  
Proposed Model ◽  
Order Preference ◽  
Hierarchy Process ◽  
Pair Wise Comparison ◽  
Insight Into ◽  
Selection Of

This paper focuses on the use of Fuzzy Analytic Hierarchy Process (FAHP) and VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian (VIKOR) to select an optimum maintenance strategy for a textile industry. In the proposed methodology, first the weight of each criterion is calculated by using improved AHP with fuzzy set theory to overcome the problems of unbalanced scale of judgments, uncertainty and imprecision in the pair-wise comparison process and then the VIKOR method is applied to compensate the imprecise ranking of the AHP in the selection of maintenance strategy. The real case study is conducted for a textile industry to illustrate the utilization of the proposed model for the maintenance strategy selection problem. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to make sure that the result of the proposed model can be acceptable. A sensitivity analysis is also conducted to show the validity of the proposed model. The paper gives an insight into multi criteria decision-making (MCDM) techniques to select an optimum maintenance strategy for a process industry using a case study.

Artificial Neural Network for Enhancing Selection of Pavement Maintenance Strategy

2000 ◽  
Vol 1699 (1) ◽  
pp. 16-22 ◽  
Author(s):  
Ashraf M. Abdelrahim ◽  
K.P. George
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Group Discussion ◽  
Maintenance Strategy ◽  
Factors Affecting ◽  
Pavement Maintenance ◽  
Out Of Sample ◽  
Artificial Neural ◽  
Hidden Layer ◽  
Selection Of

In the selection of an economical treatment for rehabilitation of a deteriorated pavement section, decision makers usually encounter various situations. Factors affecting selection of a flexible pavement maintenance strategy may include distress conditions, traffic volume, and road class, among others. Traditionally engineers make their selection on the basis of their experience and judgment and past maintenance data. Experts’ judgments are usually compromised in a group discussion to construct decision trees or decision matrices or even to develop knowledge-based expert systems. An artificial neural network is known to be an efficient technique for selection of an appropriate maintenance strategy. A genetic adaptive neural network training algorithm with a single hidden layer and sigmoid squashing function constitutes the network. The input vector represents the factors affecting maintenance strategy selection, and the output vector represents the appropriate maintenance strategy. A set of examples is derived from experts’ judgments with a total of 144 cases randomly divided into “in-sample” and “out-of-sample” data for training and testing purposes, respectively. The trained network successfully predicted 83 percent of the test cases. The remaining 17 percent of cases were one or two levels away from the expert judgments used in network testing. Neural networks provide an efficient and optimum solution for such complex problems with the added advantage of faster implementation and easier updating than with other traditional techniques.

Fleet profiling as element of vehicle operation

2018 ◽  
Vol 121 ◽  
pp. 391-398
Author(s):  
Marcin Wawrzyński
Keyword(s):  
Maintenance Strategy ◽  
Vehicle Operation ◽  
Selection Of

The article presents modifications of the maintenance strategy using telematics tools to monitor vehicle operation. It was proposed, in descriptive and formal terms, profiling of the fleet enabling the selection of sets of objects with similar utility potential. It gives the opportunity to plan operation and organization of service inspections in accordance with the principle "not too early, not too late".

scholarly journals Development of Fuzzy Multi Criteria Decision Making Method for Selection of Optimum Maintenance Alternative

2012 ◽  
pp. 206-211
Author(s):  
Arun Nagar
Keyword(s):  
Decision Making ◽  
Manufacturing Industry ◽  
Model Development ◽  
Cost Effective ◽  
Multi Criteria Decision Making ◽  
Maintenance Strategy ◽  
Fuzzy Decision ◽  
Optimal Maintenance ◽  
Frame Work ◽  
Selection Of

An optimal maintenance strategy is a key support to production in the manufacturing industry. This paper present a fuzzy approach based on Multi-Criteria Decision-Making (MCDM) methodology for selecting the optimal maintenance alternative. In the present work the criticality of each equipment is achieved by ranking (based on production loss).It is very difficult to quantify the qualitative factors in exact numerical value. These factors can be expressed in the linguistics terms which can be translated into mathematical measures by using fuzzy sets & system theory. The study problem to develop a fuzzy decision approach to rank the suitable maintenance alternative. The objective of this paper is to propose fuzzy frame work based on fuzzy number theory to solve optimal maintenance alternative which includes decision criteria analysis, weight assessment & decision model development. The approach can aid formulating a cost-effective maintenance strategy for a manufacturing plant.

The model for selection of a maintenance strategy for municipal buildings

2008 ◽  
Vol 35 (2/3/4) ◽  
pp. 219 ◽  
Author(s):  
Bernd Reichelt ◽  
Borisas Melnikas ◽  
Tatjana Vilutiene
Keyword(s):  
Maintenance Strategy ◽  
Selection Of

scholarly journals A Cost-Benefit Methodology for Selecting Analytical Reinforced Concrete Corrosion Onset Models

2020 ◽  
Vol 2020 ◽  
pp. 1-22
Author(s):  
N. Rakotovao Ravahatra ◽  
T. de Larrard ◽  
F. Duprat ◽  
E. Bastidas-Arteaga ◽  
F. Schoefs
Keyword(s):  
Case Studies ◽  
Cost Benefit ◽  
Chloride Ingress ◽  
Relative Importance ◽  
Maintenance Strategy ◽  
Concrete Carbonation ◽  
Concrete Type ◽  
Available Information ◽  
Reinforced Concrete Corrosion ◽  
Selection Of

This work focuses on predicting corrosion onset induced by concrete carbonation or chloride ingress when using analytical predictive models. The paper proposes a procedure that helps building and infrastructure managers to select an appropriate model depending on the available information and the means granted to auscultation campaigns. The approach proposed combines the costs of input parameters, their relative importance, the benefits brought through obtaining parameters, and the maintenance strategy of the manager. Costs represent the intellectual investment to obtain parameters. This encompasses the time spent to obtain and analyze a result and the required expertise. Relative importance and benefits are obtained from sensitivity analysis. The effect of the maintenance strategy is introduced through a scalar called efficiency of the model. The proposed methodology is illustrated with two case studies where it is supposed that more or less extended information is available. Three concrete qualities are also considered in the case studies. The results highlight that the available data and concrete type have significant impacts on the selection of the most appropriate model.

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