IJISA Vol. 13, No. 1, 8 Feb. 2021
Cover page and Table of Contents: PDF (size: 1142KB)
Full Text (PDF, 1142KB), PP.1-16
Views: 0 Downloads: 0
Machine Learning, Support Vector Machine, Resource allocation, Services Level Agreement
Determining the resource requirements at airports especially in-ground services companies is essential to successful planning in the future, which is represented in the resources demand curve according to the future flight schedule, through which staff schedules are created at the airport to cover the workload with ensuring the highest possible quality service provided. Given in the presence of variety service level agreements used on flight service vary according to many flight features, the resources assumption method makes planning difficult. For instance, flight position is not included in future flight schedule but it's efficacious in the identification of flight resources. In this regard, based on machine learning, we propose a model for building a resource demand curve for future flight schedules. It is divided into two phases, the first is the use of machine learning to predict resources of the service level agreement required on future flight schedules, and the second is the use of implement a resource allocation algorithm to build a demand curve based on predicted resources. This proposal could be applicable to airports that will provide efficient and realistic for the resources demand curve to ensure the resource planning does not deviate from the real-time resource requirements. the model has proven good accuracy when using one day of flights to measuring deviation between the proposed model predict demand curve when flights did not include the location feature and the actual demand curve when flights include location.
Maged Mamdouh, Mostafa Ezzat, Hesham Hefny, "Optimized Planning of Resources Demand Curve in Ground Handling based on Machine Learning Prediction", International Journal of Intelligent Systems and Applications(IJISA), Vol.13, No.1, pp.1-16, 2021. DOI:10.5815/ijisa.2021.01.01
[1]Marintseva, K., G. Yun, and S. Kachur, Resource allocation improvement in the tasks of airport ground handling operations. Aviation, 2015. 19(1): p. 7-13.
[2]Fitouri-Trabelsi, S., et al., Integrated decision making for ground handling management. 2015.
[3]Padrón, S., et al., A bi-objective approach for scheduling ground-handling vehicles in airports. Computers & Operations Research, 2016. 71: p. 34-53.
[4]Clausen, T. and D. Pisinger, Airport ground staff scheduling. 2011: DTU Management Engineering.
[5]Mamdouh, M., M. Ezzat, and H.A. Hefny, Airport resource allocation using machine learning techniques. Inteligencia Artificial, 2020. 23(65): p. 19-32.
[6]Tien, J.M. and A. Kamiyama, On manpower scheduling algorithms. SIAM review, 1982. 24(3): p. 275-287.
[7]Herbers, J. and J. Hromkovic, Models and algorithms for ground staff scheduling on airports. 2005, Fakultät für Mathematik, Informatik und Naturwissenschaften.
[8]Justesen, T.F., Allocation of Ground Handling Resources at Copenhagen Airport. DTU Management Engineering, 2014.
[9]Kierzkowski, A. and T. Kisiel. Simulation model of logistic support for functioning of ground handling agent, taking into account a random time of aircrafts arrival. in International Conference on Military Technologies (ICMT) 2015. 2015. IEEE.
[10]Mota, M.M. and C.Z. Alcaraz, Allocation of airport check-in counters using a simulation-optimization approach, in Applied Simulation and Optimization. 2015, Springer. p. 203-229.
[11]Brucker, P., et al., Resource-constrained project scheduling: Notation, classification, models, and methods. European journal of operational research, 1999. 112(1): p. 3-41.
[12]Kohl, N., et al., Airline disruption management—perspectives, experiences and outlook. Journal of Air Transport Management, 2007. 13(3): p. 149-162.
[13]Herbers, J., Representing labor demands in airport ground staff scheduling, in Operations Research Proceedings 2005. 2006, Springer. p. 15-20.
[14]Clausen, T., A dynamic programming-based heuristic for the shift design problem in airport ground handling. (DTU Management 2010.
[15]Quimper, C.-G. and L.-M. Rousseau, A large neighbourhood search approach to the multi-activity shift scheduling problem. Journal of Heuristics, 2010. 16(3): p. 373-392.
[16]Stolletz, R., Operational workforce planning for check-in counters at airports. Transportation Research Part E: Logistics and Transportation Review, 2010. 46(3): p. 414-425.
[17]Carotenuto, P., et al., Resource planning for aircraft refueling in airport parking area. Transportation Research Procedia, 2019. 37: p. 250-257.
[18]Nathuji, R., A. Kansal, and A. Ghaffarkhah. Q-clouds: managing performance interference effects for qos-aware clouds. in Proceedings of the 5th European conference on Computer systems. 2010.
[19]Bankole, A.A. and S.A. Ajila. Predicting cloud resource provisioning using machine learning techniques. in 2013 26th IEEE Canadian Conference on Electrical and Computer Engineering (CCECE). 2013. IEEE.
[20]Minarolli, D. and B. Freisleben. Cross-correlation prediction of resource demand for virtual machine resource allocation in clouds. in 2014 Sixth International Conference on Computational Intelligence, Communication Systems and Networks. 2014. IEEE.
[21]Shen, Y. Virtual resource scheduling prediction based on a support vector machine in cloud computing. in 2015
8th International Symposium on Computational Intelligence and Design (ISCID). 2015. IEEE.
[22]Niehorster, O., et al. Autonomic resource management with support vector machines. in 2011 IEEE/ACM 12th International Conference on Grid Computing. 2011. IEEE.
[23]Cao, X., et al., A machine learning-based algorithm for joint scheduling and power control in wireless networks. IEEE Internet of Things Journal, 2018. 5(6): p. 4308-4318.
[24]Ernst, A.T., et al., Staff scheduling and rostering: A review of applications, methods and models. European journal of operational research, 2004. 153(1): p. 3-27.
[25]Mohammed, A. and R. Kora, Deep learning approaches for Arabic sentiment analysis. Social Network Analysis and Mining, 2019. 9(1): p. 52.
[26]Online. sklearn.svm.SVC. 2019; Available from: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html.
[27]Kilany, M., A.E. Hassanien, and A. Badr. Accelerometer-based human activity classification using water wave optimization approach. in 2015 11th International Computer Engineering Conference (ICENCO). 2015. IEEE.
[28]Ren, Y. and G. Bai, Determination of optimal SVM parameters by using GA/PSO. JCP, 2010. 5(8): p. 1160-1168.
[29]Tharwat, A., A.E. Hassanien, and B.E. Elnaghi, A BA-based algorithm for parameter optimization of support vector machine. Pattern Recognition Letters, 2017. 93: p. 13-22.
[30]Brownlee, J. Machinelearningmastery. 2019; Available from: https://machinelearningmastery.com/how-to-connect-model-input-data-with-predictions-for-machine-learning/.
[31]Redell, N., Shapley Decomposition of R-Squared in Machine Learning Models. arXiv preprint arXiv:1908.09718, 2019.