Deep Learning Approach to Technician Routing and Scheduling Problem
Abstract This paper proposes a hybrid algorithm including the Adam algorithm and body change operator (BCO). Feasible solutions to technician routing and scheduling problems (TRSP) are investigated by performing deep learning based on the Adam algorithm and the hybridization of Adam-BCO. TRSP is a problem where all tasks are routed, and technicians are scheduled. In the deep learning method based on the Adam algorithm and Adam-BCO algorithm, the weights of the network are updated, and these weights are evaluated as Greedy approach, and routing and scheduling are performed. The performance of the Adam-BCO algorithm is experimentally compared with the Adam and BCO algorithm by solving the TRSP on the instances developed from the literature. The numerical results evidence that Adam-BCO offers faster and better solutions considering Adam and BCO algorithm. The average solution time increases from 0.14 minutes to 4.03 minutes, but in return, Gap decreases from 9.99% to 5.71%. The hybridization of both algorithms through deep learning provides an effective and feasible solution, as evidenced by the results.
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Pekel, E., and Kara, S. S. (2019). Solving fuzzy capacitated location routing problem using hybrid variable neighborhood search and evolutionary local search. Applied Soft Computing, 83, 105665.
Tieleman, T. and Hinton, G. (2012). Lecture 6.5 - RMSProp, Coursera: Neural Networks for Machine Learning. Technical report.
Wang, J., and Sun, L. (2020). Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework. Transportation Research Part C: Emerging Technologies, 116, 102661.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65–85.
Zamorano, E., and Stolletz, R. (2017). Branch-and-price approaches for the multiperiod technician routing and scheduling problem. European Journal of Operational Research, 257(1), 55–68.
Çakirgil, S., Yücel, E., and Kuyzu, G. (2020). An integrated solution approach for multi-objective, multi-skill workforce scheduling and routing problems, Computers & Operations Research 118: 104908.
Charris, E. L. S., Montoya-Torres, J. R., and Guerrero-Rueda, W. (2019). A decision support system for technician routing with time windows. Academia RevistaLatinoamericana de Administración.
Chen, X., Thomas, B. W., and Hewitt, M. (2016). The technician routing problem with experience-based service times. Omega, 61, 49–61.
Duchi, J., Hazan, E., and Singer, Y. (2011). Adaptive subgradient methods for online learning and stochastic optimization. The Journal of Machine Learning Research, 12:2121–2159.
Fu, T., Wang, C., and Cheng, N. (2020). Deep-learning-based joint optimization of renewable energy storage and routing in vehicular energy network. IEEE Internet of Things Journal, 7(7), 6229–6241.
Graf, B. (2020). Adaptive large variable neighborhood search for a multiperiod vehicle and technician routing problem. Networks, 76(2), 256–272.
Hernández-Jiménez, R., Cárdenas, C., and Rodríguez, D. M. (2019). Towards the Optimal Solution for the Routing Problem in Vehicular Delay Tolerant Networks: A Deep Learning Approach. IEEE Latin America Transactions, 17(12), 2028–2036.
Hussain, D., Khan, M. A., Abbas, S., Naqvi, R. A., Mushtaq, M. F., Rehman, A., and Nadeem, A. (2021). Enabling Smart Cities with Cognition Based Intelligent Route Decision in Vehicles Empowered with Deep Extreme Learning Machine. CMC-computers materials & continua, 66(1), 141–156.
James, J. Q., Yu, W., and Gu, J. (2019). Online vehicle routing with neural combinatorial optimization and deep reinforcement learning. IEEE Transactions on Intelligent Transportation Systems, 20(10), 3806–3817.
Khalfay, A., Crispin, A., and Crockett, K. (2017, September). A review of technician and task scheduling problems, datasets and solution approaches. 2017 Intelligent Systems Conference. pp. 288–296.
Kingma, D. P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Koh, S., Zhou, B., Fang, H., Yang, P., Yang, Z., Yang, Q., and Ji, Z. (2020). Real-time deep reinforcement learning based vehicle navigation. Applied Soft Computing, 96, 106694.
Kovacs, A., Parragh, S. N., Doerner, K. F., and Hartl, R. F. (2012). Adaptive large neighborhood search for service technician routing and scheduling problems. Journal of scheduling, 15(5), 579–600.
Lee, K. B., A Ahmed, M., Kang, D. K., and Kim, Y. C. (2020). Deep Reinforcement Learning Based Optimal Route and Charging Station Selection. Energies, 13(23), 6255.
Mathlouthi, I., Gendreau, M., and Potvin, J. Y. (2021). A metaheuristic based on Tabu search for solving a technician routing and scheduling problem. Computers & Operations Research, 125, 105079.
Mathlouthi, I., Gendreau, M., and Potvin, J. Y. (2018). Mixed integer linear programming for a multi-attribute technician routing and scheduling problem. INFOR: Information Systems and Operational Research, 56(1), 33–49.
Pekel, E. (2020). Solving technician routing and scheduling problem using improved particle swarm optimization. Soft Computing, 24(24), 19007–19015.
Pekel, E., and Kara, S. S. (2019). Solving fuzzy capacitated location routing problem using hybrid variable neighborhood search and evolutionary local search. Applied Soft Computing, 83, 105665.
Tieleman, T. and Hinton, G. (2012). Lecture 6.5 - RMSProp, Coursera: Neural Networks for Machine Learning. Technical report.
Wang, J., and Sun, L. (2020). Dynamic holding control to avoid bus bunching: A multi-agent deep reinforcement learning framework. Transportation Research Part C: Emerging Technologies, 116, 102661.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and computing, 4(2), 65–85.
Zamorano, E., and Stolletz, R. (2017). Branch-and-price approaches for the multiperiod technician routing and scheduling problem. European Journal of Operational Research, 257(1), 55–68.
Pekel, E. (2022). Deep Learning Approach to Technician Routing and Scheduling Problem. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(2), 191–206. https://doi.org/10.14201/adcaij.27393
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