The Experts below are selected from a list of 252 Experts worldwide ranked by ideXlab platform
Richard Curran - One of the best experts on this subject based on the ideXlab platform.
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an evaluation of forecasting methods for aircraft non Routine Maintenance material demand
International Journal of Agile Systems and Management, 2014Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard CurranAbstract:Aircraft Maintenance can be divided into Routine and non-Routine activities. Material demand associated with non-Routine Maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-Routine Maintenance material demand. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-Routine Maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-Routine Maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate Maintenance C-check.
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An evaluation of forecasting methods for aircraft non-Routine Maintenance material demand
International Journal of Agile Systems and Management, 2014Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard CurranAbstract:Copyright ? 2014 Inderscience Enterprises Ltd.Aircraft Maintenance can be divided into Routine and non-Routine activities. Material demand associated with non-Routine Maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-Routine Maintenance material demand. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-Routine Maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-Routine Maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate Maintenance C-check.
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a predictive method for the estimation of material demand for aircraft non Routine Maintenance
Computers & Education, 2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the non-Routine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
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ISPE CE - A predictive method for the estimation of material demand for aircraft non-Routine Maintenance
2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. NonRoutine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the nonRoutine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
M. Zorgdrager - One of the best experts on this subject based on the ideXlab platform.
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an evaluation of forecasting methods for aircraft non Routine Maintenance material demand
International Journal of Agile Systems and Management, 2014Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard CurranAbstract:Aircraft Maintenance can be divided into Routine and non-Routine activities. Material demand associated with non-Routine Maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-Routine Maintenance material demand. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-Routine Maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-Routine Maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate Maintenance C-check.
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An evaluation of forecasting methods for aircraft non-Routine Maintenance material demand
International Journal of Agile Systems and Management, 2014Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard CurranAbstract:Copyright ? 2014 Inderscience Enterprises Ltd.Aircraft Maintenance can be divided into Routine and non-Routine activities. Material demand associated with non-Routine Maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-Routine Maintenance material demand. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-Routine Maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-Routine Maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate Maintenance C-check.
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a predictive method for the estimation of material demand for aircraft non Routine Maintenance
Computers & Education, 2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the non-Routine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
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ISPE CE - A predictive method for the estimation of material demand for aircraft non-Routine Maintenance
2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. NonRoutine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the nonRoutine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
C Water - One of the best experts on this subject based on the ideXlab platform.
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a predictive method for the estimation of material demand for aircraft non Routine Maintenance
Computers & Education, 2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the non-Routine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
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ISPE CE - A predictive method for the estimation of material demand for aircraft non-Routine Maintenance
2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. NonRoutine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the nonRoutine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
Wim J. C. Verhagen - One of the best experts on this subject based on the ideXlab platform.
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an evaluation of forecasting methods for aircraft non Routine Maintenance material demand
International Journal of Agile Systems and Management, 2014Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard CurranAbstract:Aircraft Maintenance can be divided into Routine and non-Routine activities. Material demand associated with non-Routine Maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-Routine Maintenance material demand. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-Routine Maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-Routine Maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate Maintenance C-check.
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An evaluation of forecasting methods for aircraft non-Routine Maintenance material demand
International Journal of Agile Systems and Management, 2014Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard CurranAbstract:Copyright ? 2014 Inderscience Enterprises Ltd.Aircraft Maintenance can be divided into Routine and non-Routine activities. Material demand associated with non-Routine Maintenance is typically intermittent or lumpy: it has a large variance in frequency and quantity. Consequently, this type of demand is hard to predict. This paper introduces a method to collect time series datasets for aircraft non-Routine Maintenance material demand. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. A structural part selection of the Boeing 737NG fleet of an aviation partner has been sampled to generate various test cases. Subsequently, various forecasting methods are applied to these test cases and evaluated using various accuracy metrics. For the small time series datasets associated with non-Routine Maintenance, exponentially weighted moving average (EMA) outperformed smoothing methods such as Croston's method (CR) and the Syntetos-Boylan approximation (SBA). To validate the practical applicability of EMA for non-Routine Maintenance material demand, the method has been applied and verified in the prediction of actual demand for a separate Maintenance C-check.
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a predictive method for the estimation of material demand for aircraft non Routine Maintenance
Computers & Education, 2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. Non-Routine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the non-Routine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
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ISPE CE - A predictive method for the estimation of material demand for aircraft non-Routine Maintenance
2013Co-Authors: M. Zorgdrager, Wim J. C. Verhagen, Richard Curran, B H L Boesten, C WaterAbstract:A method is developed to forecast material demand caused by aircraft non-Routine Maintenance. NonRoutine material consumption is linked to scheduled Maintenance tasks to gain insight in demand patterns. Subsequently, a suitable prediction model can be applied to forecast material demand. To test this approach, a structural part selection of the Boeing 737NG fleet of KLM Royal Dutch Airlines has been sampled to form a test case. Several regression and stochastic models have been applied to the part selection to judge model fit and validity. Resulting from this analysis, the Exponential Moving Average (EMA) was chosen as superior model for its small error values and ability to capture general demand trends. The forecast method incorporating the EMA model has been validated by forecasting and comparison against an independent dataset. Concluding, the nonRoutine Maintenance forecast method, comprising the non-Routine material consumption forecasts linked to scheduled Maintenance tasks, can be used to produce material predictions expressed in probability and average quantity figures for upcoming Maintenance checks.
Antonio Puliafito - One of the best experts on this subject based on the ideXlab platform.
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optimizing Routine Maintenance team routes
International Conference on Enterprise Information Systems, 2015Co-Authors: Francesco Longo, Andrea Rocco Lotronto, Marco Scarpa, Antonio PuliafitoAbstract:Simulated annealing is a metaheuristic approach for the solution of optimization problems inspired to the controlled cooling of a material from a high temperature to a state in which internal defects of the crystals are minimized. In this paper, we apply a simulated annealing approach to the scheduling of geographically distributed Routine Maintenance interventions. Each intervention has to be assigned to a Maintenance team and the choice among the available teams and the order in which interventions are performed by each team are based on team skills, cost of overtime work, and cost of transportation. We compare our solution algorithm versus an exhaustive approach considering a real industrial use case and show several numerical results to analyze the effect of the parameters of the simulated annealing on the accuracy of the solution and on the execution time of the algorithm.
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ICEIS (1) - Optimizing Routine Maintenance Team Routes
Proceedings of the 17th International Conference on Enterprise Information Systems, 2015Co-Authors: Francesco Longo, Andrea Rocco Lotronto, Marco Scarpa, Antonio PuliafitoAbstract:Simulated annealing is a metaheuristic approach for the solution of optimization problems inspired to the controlled cooling of a material from a high temperature to a state in which internal defects of the crystals are minimized. In this paper, we apply a simulated annealing approach to the scheduling of geographically distributed Routine Maintenance interventions. Each intervention has to be assigned to a Maintenance team and the choice among the available teams and the order in which interventions are performed by each team are based on team skills, cost of overtime work, and cost of transportation. We compare our solution algorithm versus an exhaustive approach considering a real industrial use case and show several numerical results to analyze the effect of the parameters of the simulated annealing on the accuracy of the solution and on the execution time of the algorithm.
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a simulated annealing based approach for the optimization of Routine Maintenance interventions
International Conference on Enterprise Information Systems, 2015Co-Authors: Francesco Longo, Andrea Rocco Lotronto, Marco Scarpa, Antonio PuliafitoAbstract:Metaheuristics are often adopted to solve optimization problems where some requests need to be scheduled among a finite number of resources, i.e., the so called scheduling problems. Such techniques approach the optimization problems by taking inspiration from a certain physical phenomenon. Simulated annealing is a metaheuristic approach inspired to the controlled cooling of a material from a high temperature to a state in which internal defects of the crystals are minimized. In this paper, we use a simulated annealing-based approach to solve the problem of the scheduling of geographically distributed Routine Maintenance interventions. Each intervention has to be assigned to a Maintenance team and the choice among the available teams and the order in which interventions are performed by each team are based on team skills, cost of overtime work, and cost of transportation. We compare our solution algorithm versus an exhaustive approach. First, we consider a real industrial use case and show several numerical results to analyze the effect of the parameters of the simulated annealing on the accuracy of the solution and on the execution time of the algorithm. Then, we provide results varying the parameters and dimension of the considered problem highlighting how they affect reliability and efficiency of our algorithm.
