Allotment of aircraft spare parts using genetic alorithms

Batchoun, Pascale; Ferland, Jacques A.; Cléroux, Robert

doi:10.1590/s0101-74382003000100011

Cited by 8 publications

(7 citation statements)

References 13 publications

(12 reference statements)

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“…One of the widely-used probability distributions in reliability to model fatigue and wear-out phenomena is the normal distribution, as illustrated by the works of Tuomas et al (2001), Byington et al (2002), Batchoun et al (2003), Deshpande et al (2006), Muchiri andSmit (2011), andKiyak (2012). If we assume that f(x) is normally distributed, with a mean m x and standard deviation s x , the formula for the PDF is.…”

Section: Numerical and Iteration Methodsmentioning

confidence: 99%

Efficient aircraft spare parts inventory management under demand uncertainty

Zhang

2015

Journal of Air Transport Management

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a b s t r a c tIn airline industries, the aircraft maintenance cost takes up about 13% of the total operating cost. It can be reduced by a good planning. Spare parts inventories exist to serve the maintenance planning. Compared with commonly used reorder point system (ROP) and forecasting methods which only consider historical data, this paper presents two non-linear programming models which predict impending demands based on installed parts failure distribution. The optimal order time and order quantity can be found by minimizing total cost. The first basic mathematical model assumes shortage period starts from mean time to failure (MTTF). An iteration method and GAMS are used to solve this model. The second improved mathematical model takes into account accurate shortage time. Due to its complexity, only GAMS is applied in solution methodology. Both models can be proved effective in cost reduction through revised numerical examples and their results. Comparisons of the two models are also discussed.

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Section: Numerical and Iteration Methodsmentioning

confidence: 99%

Efficient aircraft spare parts inventory management under demand uncertainty

Zhang

2015

Journal of Air Transport Management

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“…In order to reduce the cost of delay caused by unexpected failures, Batchoun et al 131 used genetic algorithms to determine the best configuration plan of spare parts. Marseguerra et al 132 used genetic algorithms to optimize the number of spare parts required for a multi-component system, so as to optimize multiple goals such as system revenue and total inventory.…”

Section: Optimization Algorithms Of Spare Parts Configurationmentioning

confidence: 99%

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Feng

Chen

et al. 2021

Advances in Mechanical Engineering

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Spare parts are treated as the basis to guarantee the safe and economic operation of civil aircraft, its scientific prediction and reasonable configuration play an important role in perfecting integrated logistics support (ILS) and achieving win-win situation of stakeholders (e.g. manufacturers, operators, maintenance providers). This paper studies the existing spare parts prediction and configuration methods of civil aircraft, and discusses future development trend of spare parts from the perspective of prediction and configuration, respectively. The current development status of civil aircraft spare parts prediction techniques are firstly introduced, according to demand characteristics of spare parts; the present research status of civil aircraft spare parts configuration methods are then elaborated, based on the traditional methods and machine learning methods; the development trend of civil aircraft spare parts prediction and configuration methods is finally analyzed, combined with the advantages and disadvantages of the existing methods of civil aircraft spare parts management methods. The efforts of this work provide a reference for the comprehensive management of civil aircraft spare parts and improve the perfection degree for the ILS of civil aircraft.

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“…One difference with our problem is that in the METRIC model a defective part can be replaced only by another item available at the station or at the central depot. Batchoun, Ferland and Cléroux (2003) also considered a similar aircraft spare parts inventory problem. They applied Genetic Algorithms in an adaptive search procedure to allocate the initial quantity of spare parts to the airports.…”

Section: Planning Aircraft Spare Parts Inventory Among Airportsmentioning

confidence: 99%

Application of Multi-objective Simulation-optimization Techniques to Inventory Management Problems

Lee¹,

Teng²,

Chew³

et al.

Proceedings of the Winter Simulation Conference, 2005.

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In this paper, we present how a solution framework developed for (a special case of) the multi-objective simulationoptimization problems can be applied to evaluate and optimally select the non-dominated set of inventory policies for two case study problems. Based on the concept of Pareto optimality, the solution framework mainly includes how to evaluate the quality of the selected Pareto set by two types of errors, and how to allocate the simulation replications according to some asymptotic allocation rules. Given a fixed set of inventory policies for both case study problems, the proposed solution method is applied to allocate the simulation replications. Results show that the solution framework is efficient and robust in terms of the total number of simulation replications needed to find the nondominated Pareto set of inventory policies.

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Allotment of aircraft spare parts using genetic alorithms

Cited by 8 publications

References 13 publications

Efficient aircraft spare parts inventory management under demand uncertainty

Efficient aircraft spare parts inventory management under demand uncertainty

Civil Aircraft Spare Parts Prediction and Configuration Management Techniques: Review and Prospect

Application of Multi-objective Simulation-optimization Techniques to Inventory Management Problems

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