Multiobjective Optimization of Aircraft Maintenance in Thailand Using Goal Programming: A Decision-Support Model

Pleumpirom, Yuttapong; Amornsawadwatana, Sataporn

doi:10.1155/2012/128346

Cited by 7 publications

(5 citation statements)

References 21 publications

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“…Furthermore, Pleumpirom and Amorsawadwatana had developed a multi objective optimization model using goal programming in forecasting spare parts requirements for schedule maintenance with the aims to minimize cost, minimize lead time and maximize the quality of the spare parts. This model is used as a tool in making decision of either to purchase, repair or loan the spare parts for the scheduled maintenance requirements with the aims to reduce the total spare part cost, reduce maintenance down time and also improve the availability of the aircraft [15].…”

Section: Related Workmentioning

confidence: 99%

“…Meanwhile, in order to achieve second goal, another constraint is used to ensure total cost of the component purchase or repair does not exceed the total allocated budget as represented in the Equation (14) and (15). The underachievement, and overachievement, deviations are also assigned to this constraint together with the weighted constants, in order to ensure the total cost is within the allocated budget.…”

Section: Goal Programmingmentioning

confidence: 99%

“…Binary constant 1 is assigned on the overachievement since the total cost must be within the allocated budget and 0 if otherwise. (15) In this model, , and should be always positive is in order to ensure this model is feasible. If the is greater than , the component is not required to be purchased or repaired, therefore will be equal to zero as in Equation (16).…”

Section: Goal Programmingmentioning

confidence: 99%

See 2 more Smart Citations

Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

Rahim

Noor

Abdullah

et al. 2020

IJIE

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Spare parts management of the aircrafts is a part of maintenance planning that requires effective and efficient planning in order to reduce aircraft downtime in maintenance processes. Spare parts unavailability during maintenance is one of the factors that affects operational availability of the aircrafts fleet. This study focused on identifying the current problem affecting operational availability of the aircrafts fleet, developing an optimization model for cost minimization of spare parts and assessing the impact on spare parts availability using the cost minimization optimization model. Data analysis using machine learning techniques in WEKA was used to identify the current problem that is affecting aircrafts fleet operational availability. Then, Linear Integer Programming (LIP) and Goal Programming (GP) methods were used to develop the cost minimization optimization model by considering the current stock level of the critical components and the budget constrain. From this study, we discovered that spare parts unavailability during maintenance was the biggest contributing factor to the operational availability of the aircrafts fleet. Besides that, the cost minimization optimization model developed in the research had produced optimum level of inventory for the critical components with minimum cost. Spare parts availability for the aircrafts fleet had been improved with the application of the model which directly maximizes the operational availability of the aircraft fleet.

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Section: Related Workmentioning

confidence: 99%

Section: Goal Programmingmentioning

confidence: 99%

Section: Goal Programmingmentioning

confidence: 99%

See 1 more Smart Citation

Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

Rahim

Noor

Abdullah

et al. 2020

IJIE

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“…An approach for maximizing aircraft utilization by defining routes that satisfy the scheduled maintenance checks was proposed in [12]. A model to optimize the inventory management regarding the components and spare parts needed for maintenance was developed in [13]. The use of RL in maintenance scheduling problems was studied in [14].…”

Section: Introductionmentioning

confidence: 99%

Aircraft Maintenance Check Scheduling Using Reinforcement Learning

et al. 2021

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This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

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“…Such counting processes can be modelled using various representations, for instance as a (non)homogeneous Poisson process. Accidental damage concerns failure modes and mechanisms which act in short time, which is in marked contrast to damage caused by failure mechanisms such as structural aging, fatigue and wear, where the underlying physical failure process(es) generate deteriorating behaviour over a larger timespan (Pleumpirom & Amornsawadwatana, 2012;Pogacnik, Duhovnik, & Tavčar, 2017).…”

Section: Introductionmentioning

confidence: 99%

Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations

Narayanan

Verhagen

Dhanisetty

2019

Journal of Management Analytics

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Airline operators face accidental damages on their fleet of aircraft as part of operational practice. Individual occurrences are hard to predict; consequently, the approach towards repairing accidental damage is reactive in aircraft maintenance practice. However, by aggregating occurrence data and predicting future occurrence rates, it is possible to predict future long-term (strategic) demand for maintenance capacity. In this paper, a novel approach for integration of reliability modelling and inventory control is presented. Here, the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity. Demand has been modelled using a superposed Non-homogeneous Poisson Process (NHPP). A case study has been performed on damage data from a fleet of Boeing 777 aircraft. The results prove the feasibility of adopting an integrated approach towards strategic capacity identification, using real-life data to predict future damage occurrence and associated maintenance slot requirements.

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Multiobjective Optimization of Aircraft Maintenance in Thailand Using Goal Programming: A Decision-Support Model

Cited by 7 publications

References 21 publications

Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

Cost Minimization of Aircraft Critical Components for Planning and Maintenance Requirements

Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations

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