Integrated optimization of system design and spare parts allocation by means of multiobjective genetic algorithms and Monte Carlo simulation

2007 ◽  
Vol 221 (1) ◽  
pp. 67-84 ◽  
Author(s):  
E Zio ◽  
L Podofillini
Keyword(s):  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
System Design ◽  
Spare Parts ◽  
Integrated Optimization ◽  
Multiobjective Genetic Algorithms

scholarly journals Optimal sampling in a noisy genetic algorithm for risk-based remediation design

2003 ◽  
Vol 5 (1) ◽  
pp. 11-25 ◽  
Author(s):  
Gayathri Gopalakrishnan ◽  
Barbara S. Minsker ◽  
David E. Goldberg
Keyword(s):  
Genetic Algorithm ◽  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Fitness Function ◽  
Sampling Strategies ◽  
Robust Designs ◽  
Highly Efficient ◽  
Efficient Sampling

A groundwater management model has been developed that predicts human health risks and uses a noisy genetic algorithm to identify promising risk-based corrective action (RBCA) designs. Noisy genetic algorithms are simple genetic algorithms that operate in noisy environments. The noisy genetic algorithm uses a type of noisy fitness function (objective function) called the sampling fitness function, which utilises Monte-Carlo-type sampling to find robust designs. Unlike Monte Carlo simulation modelling, however, the noisy genetic algorithm is highly efficient and can identify robust designs with only a few samples per design. For hydroinformatic problems with complex fitness functions, however, it is important that the sampling be as efficient as possible. In this paper, methods for identifying efficient sampling strategies are investigated and their performance evaluated using a case study of a RBCA design problem. Guidelines for setting the parameter values used in these methods are also developed. Applying these guidelines to the case study resulted in highly efficient sampling strategies that found RBCA designs with 98% reliability using as few as 4 samples per design. Moreover, these designs were identified with fewer simulation runs than would likely be required to identify designs using trial-and-error Monte Carlo simulation. These findings show considerable promise for applying these methods to complex hydroinformatic problems where substantial uncertainty exists but extensive sampling cannot feasibly be done.

Dimensional Adjustment for Assemblability of Rapid Protoyped Parts

2003 ◽  
Author(s):  
Hoda A. ElMaraghy ◽  
Ashraf O. Nassef ◽  
Waguih H. ElMaraghy
Keyword(s):  
Monte Carlo Simulation ◽  
Genetic Algorithms ◽  
Monte Carlo ◽  
Rapid Prototyping ◽  
Functional Requirements ◽  
General Shape ◽  
Adjustment Method ◽  
Optimal Adjustment ◽  
Proposed Modification ◽  
Real Coded Genetic Algorithms

Recent advances in rapid prototyping technology make it a useful tool in assessing the early designs of not only individual parts but also assemblies. These rapid assemblies should allow the designers to evaluate the desired functional requirements for the actual fabricated parts. However, the rapid prototyping errors, especially shrinkage, make it difficult to emulate such functional requirements in the prototype. This paper presents an algorithm for the optimal adjustment of the nominal dimensions of rapid prototyped parts to maximize the probability of adherence to the assembly functional requirements. The proposed modification of the nominal dimensions compensates for shrinkage. In addition, the algorithm preserves the general shape of the parts. Real coded genetic algorithms are used to maximize the probability of adhering to those requirements and a truncated Monte Carlo simulation is used to evaluate it. Several examples have been used to demonstrate the developed algorithm and procedures. Guidelines have been presented for the applicability of this adjustment method for various types of fits. The proposed method allows the designers to experience more realistically the intended fit and feel of actual manufactured parts assemblies.

Turbogroup Spare Part Optimization by Availability Centered Maintenance Methodology: An Application to LTSA Contract

2003 ◽  
Author(s):  
Giuseppe Fabio Cheschini ◽  
Fausto Carlevaro ◽  
Giuseppe Racioppi ◽  
Andrea Masi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Block Diagram ◽  
Spare Part ◽  
Spare Parts ◽  
Minimum Level ◽  
Part List ◽  
Simulation Techniques ◽  
System Components ◽  
Penalty Clauses

Most Oil&Gas companies that currently outsource both maintenance and related engineering activities, by establishing Long Terms Service Agreements (LTSA) with engine manufacturers, base their contracts on several clauses. A minimum level of overall Plant Availability is usually to be guaranteed by the maintenance services supplier. Both parts agree upon this availability threshold and Bonus/Penalty clauses are based on this value. The Availability of a system is a non-linear function of: • Reliability, in terms of components life, system functional configuration and scheduled maintenance frequencies; • Maintainability, in terms of time to repair/restore, duration of scheduled maintenance tasks and special maintenance tools on site; • Logistics. Concerning Logistics, the problem is to define both the set and levels of spare parts in a warehouse for one or more installations (pooling) and the location of the warehouse itself. In this paper a solution for this problem is presented, based on RBD (Reliability Block Diagram) Monte Carlo simulation techniques and an associated What-If analysis. One of the deliverables of this optimisation process is the ranking of all the system components in terms of their influence on the availability of the whole system. This spare part list optimisation is one of the major deliverables of the discipline called ACM “Availability Centered Maintenance”, currently developed in Nuovo Pignone.

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