Optimization Method of Equipment Spare Parts Based on Monte Carlo Simulation

2019 ◽  
Author(s):  
Guangze Pan ◽  
Man You ◽  
Qin Luo ◽  
Xiaobing Li ◽  
Yuanhang Wang
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Optimization Method ◽  
Spare Parts

scholarly journals Social-network-based water supply network sectorization methodology using monte carlo simulation to predict economical and operational benefits

2017 ◽  
Vol 26 ◽  
pp. 44-53
Author(s):  
Enrique Campbell ◽  
Amilkar Illaya-Ayza ◽  
Joaquín Izquierdo ◽  
Rafael Pérez-García ◽  
Idel Montalvo
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Water Supply ◽  
Optimization Method ◽  
Supply Network ◽  
Water Supply Network ◽  
Water Supply Management ◽  
Net Profit ◽  
Short Run ◽  
The Impact

Water Supply Network (WSN) sectorization is a broadly known technique aimed at enhancing water supply management. In general, existing methodologies for sectorization of WSNs are limited to assessment of the impact of its implementation over reduction of background leakage, underestimating increased capacity to detect new leakage events and undermining appropriate investment substantiation. In this work, we raise this issue and put in place a methodology to optimize sectors' design. To this end, we carry out a novel combination of the Short Run Economic Leakage Level concept (SRELL- corresponding to leakage level that can occur in a WSN in a certain period of time and whose reparation would be more costly than the benefits that can be obtained). With a non-deterministic optimization method based on Genetic Algorithms (GAs) in combination with Monte Carlo simulation. As an example of application, methodology is implemented over a 246 km pipe-long WSN, reporting 72 397 $/year as net profit.

Optimization-Based Probabilistic Consequence Scenario Construction for Lifeline Systems

2014 ◽  
Vol 30 (4) ◽  
pp. 1531-1551 ◽  
Author(s):  
Jared Gearhart ◽  
Nathanael Brown ◽  
Dean Jones ◽  
Linda Nozick ◽  
Natalia Romero ◽  
...  
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Joint Distribution ◽  
Optimization Method ◽  
Seismic Zone ◽  
Realistic Case ◽  
Lifeline System ◽  
Highway Network ◽  
Lifeline Systems

The construction of a suite of consequence scenarios that is consistent with the joint distribution of damage to a lifeline system is critical to properly estimating regional loss after an earthquake. This paper describes an optimization method that identifies a suite of consequence scenarios that can be used in regional loss estimation for lifeline systems when computational demands are of concern, and it is important to capture the spatial correlation associated with individual events. This method is applied to a realistic case study focused on the highway network in Memphis, Tennessee, within the New Madrid Seismic Zone. This case study illustrates that significantly fewer consequence scenarios are needed with this method than would be required using Monte Carlo simulation.

DHIMAN: A novel algorithm for economic Dispatch problem based on optimization metHod usIng Monte Carlo simulation and Astrophysics coNcepts

2019 ◽  
Vol 34 (04) ◽  
pp. 1950032 ◽  
Author(s):  
Gaurav Dhiman ◽  
Pritpal Singh ◽  
Harsimran Kaur ◽  
Ritika Maini
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
State Of The Art ◽  
Real Life ◽  
Optimization Method ◽  
Optimization Approach ◽  
Effective Number ◽  
Number Of Solutions ◽  
Efficient Prediction ◽  
Competing Models

This paper presents a new model using optimization approach for efficient prediction of load in real-life environment. Monte Carlo simulation and Schrödinger equations provide the effective number of solutions. This technique is useful in representation of relationships between different models. The proposed algorithm is verified and validated with various state-of-the-art approaches for solving economic load power dispatch problem to demonstrate its efficiency. Experimental results signify that the proposed algorithm is more precise than existing competing models.

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.

Optimization on Motion Errors of Ultra-Precision Machine Tools Based on Multi-Body System and Monte Carlo Simulation

2011 ◽  
Vol 201-203 ◽  
pp. 543-549 ◽  
Author(s):  
Tian Jian Li ◽  
Shi Jin Chen ◽  
Kai Cheng
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Machine Tools ◽  
Optimization Method ◽  
Body System ◽  
Monte Carlo Optimization ◽  
Precision Machine ◽  
Ultra Precision ◽  
Precision Machine Tools ◽  
Multi Body

Numerical optimization method is increasingly applied into the design of machine tools so as to improve their performance. This paper employs Monte Carlo optimization method to predict and reduce the errors of ultra-precision machine tools in term of motion errors between cutting tool and workpiece closely associated with machine processing of ultra-precision machine tools. Using a quite different origin position of location coordinate system from traditional in that every ideal frame on current body is coincided with the reference actual one on the adjacent body, the motion errors are expressed in homogeneous matrix defined with adjacent bodies’ residuals in multi-body system analysis. This expression clearly shows that the final position errors are decided by the motion accuracy of the guideline, and gesture errors are affected by accuracy of the spindle assembly. With geometric errors extracted from the matrix, a new optimization method of error allocation is presented to maximize the machine precision by reasonable distribution of tolerances key parts. Using variables intimately related to motion errors and under constrained by cost, optimization model is established, then it is solved with Monte Carlo simulation method to compute top ten key factors contributed to errors and obtain distribution probabilities of both position error and gesture error. Case study formulated is reported to illustrate the method proposed and to evaluate its effectiveness.

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