Hybrid Value Function Approximation for Solving the Technician Routing Problem with Stochastic Repair Requests

References

• Alem D, Clark A, Moreno A (2016) Stochastic network models for logistics planning in disaster relief. Eur. J. Oper. Res. 255(1):187–206.Crossref, Google Scholar
• APPLiA (2019) The home appliance industry in Europe. Accessed July 1, 2022, http://statreport2020.applia-europe.eu/files/applia-statistical-report-2020.pdf.Google Scholar
• Avraham E, Raviv T (2021) The steady-state mobile personnel booking problem. Transportation Res. Part B Methodological 154:266–288.Crossref, Google Scholar
• Bijvank M, Koole G, Vis IF (2010) Optimising a general repair kit problem with a service constraint. Eur. J. Oper. Res. 204(1):76–85.Crossref, Google Scholar
• Boutilier C, Cohen A, Daniely A, Hassidim A, Mansour Y, Meshi O, Mladenov M, Schuurmans D 2018 Planning and learning with stochastic action sets. Preprint, submitted May 7, https://arxiv.org/abs/1805.02363.Google Scholar
• Castillo-Salazar JA, Landa-Silva D, Qu R (2016) Workforce scheduling and routing problems: Literature survey and computational study. Ann. Oper. Res. 239(1):39–67.Crossref, Google Scholar
• Chen X, Thomas BW, Hewitt M (2017) Multi-period technician scheduling with experience-based service times and stochastic customers. Comput. Oper. Res. 82:1–14.Crossref, Google Scholar
• Dutta S (2013) Field service 2013: Workforce management guide. Industry research report, Aberdeen Group. Previously available at http://www.aberdeen.com/Aberdeen-Library/8325/RA-field-service-workforce.aspx (account creation is required to access the document at https://www.aberdeen.com/research-insights/ as of December 27, 2023.)Google Scholar
• Fey M, Lenssen JE (2019) Fast graph representation learning with PyTorch geometric. Preprint, submitted March 6, https://arxiv.org/abs/1903.02428.Google Scholar
• Gambella C, Maggioni F, Vigo D (2019) A stochastic programming model for a tactical solid waste management problem. Eur. J. Oper. Res. 273(2):684–694.Crossref, Google Scholar
• Gao H, Ji S (2019) Graph U-Nets. Chaudhuri K, Salakhutdinov R, eds. Proc. 36th Internat. Conf. Machine Learn., Proceedings of Machine Learning Research, vol. 97 (PMLR, New York), 2083–2092.Google Scholar
• Gen M, Cheng R (1996) A survey of penalty techniques in genetic algorithms. Proc. IEEE Internat. Conf. Evolutionary Comput. (Institute of Electrical and Electronics Engineers, Piscataway, NJ), 804–809.Google Scholar
• Gläscher J, Daw N, Dayan P, O’Doherty JP (2010) States vs. rewards: Dissociable neural prediction error signals underlying model-based and model-free reinforcement learning. Neuron 66(4):585–595.Crossref, Google Scholar
• Heeremans D, Gelders L (1995) Multiple period repair kit problem with a job completion criterion: A case study. Eur. J. Oper. Res. 81(2):239–248.Crossref, Google Scholar
• Heinold A, Meisel F, Ulmer MW (2022) Primal-dual value function approximation for stochastic dynamic intermodal transportation with eco-labels. Transportation Sci. 57(6):1452–1472.Google Scholar
• Hildebrandt FD, Thomas BW, Ulmer MW (2022) Opportunities for reinforcement learning in stochastic dynamic vehicle routing. Comput. Oper. Res. 150:106071.Crossref, Google Scholar
• Holland JH (1992) Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence (MIT Press, Cambridge, MA).Crossref, Google Scholar
• Jin Y, Branke J (2005) Evolutionary optimization in uncertain environments—A survey. IEEE Trans. Evolutionary Comput. 9(3):303–317.Crossref, Google Scholar
• Joe W, Lau HC (2020) Deep reinforcement learning approach to solve dynamic vehicle routing problem with stochastic customers. Proc. Internat. Conf. Automated Planning and Scheduling 30(1):394–402.Google Scholar
• Kingma DP, Ba J (2014) Adam: A method for stochastic optimization. Preprint, submitted December 22, https://arxiv.org/abs/1412.6980.Google Scholar
• Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. Preprint, submitted September 9, https://arxiv.org/abs/1609.02907.Google Scholar
• Klibi W, Lasalle F, Martel A, Ichoua S (2010) The stochastic multiperiod location transportation problem. Transportation Sci. 44(2):221–237.Link, Google Scholar
• Konda V, Tsitsiklis J (1999) Actor-critic algorithms. Proc. Adv. Neural Inform. Processing Systems 12 (NIPS 1999) (MIT Press, Cambridge, MA).Google Scholar
• Laitala K, Klepp IG, Haugrønning V, Throne-Holst H, Strandbakken P (2021) Increasing repair of household appliances, mobile phones and clothing: Experiences from consumers and the repair industry. J. Cleaner Production 282:125349.Crossref, Google Scholar
• Liu S, Luo Z (2022) On-demand delivery from stores: Dynamic dispatching and routing with random demand. Manufacturing Service Oper. Management 25(2):595–612.Link, Google Scholar
• Mathlouthi I, Gendreau M, Potvin JY (2021) A metaheuristic based on tabu search for solving a technician routing and scheduling problem. Comput. Oper. Res. 125:105079.Crossref, Google Scholar
• Mnih V, Badia AP, Mirza M, Graves A, Lillicrap T, Harley T, Silver D, Kavukcuoglu K (2016) Asynchronous methods for deep reinforcement learning. Balcan MF, Weinberger KQ, eds. Proc. 33rd Internat. Conf. Machine Learn., vol. 48 (PMLR, New York), 1928–1937.Google Scholar
• Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533.Crossref, Google Scholar
• Moreno A, Alem D, Ferreira D (2016) Heuristic approaches for the multiperiod location-transportation problem with reuse of vehicles in emergency logistics. Comput. Oper. Res. 69:79–96.Crossref, Google Scholar
• Neves-Moreira F, Veldman J, Teunter RH (2021) Service operation vessels for offshore wind farm maintenance: Optimal stock levels. Renewable Sustainable Energy Rev. 146:111158.Crossref, Google Scholar
• Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, et al. (2019) PyTorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems 32 (Curran Associates, Inc., Red Hook, NY), 8024–8035.Google Scholar
• Pham DT, Kiesmüller GP (2022) Multiperiod integrated spare parts and tour planning for on-site maintenance activities with stochastic repair requests. Comput. Oper. Res. 148:105967.Crossref, Google Scholar
• Pillac V, Gueret C, Medaglia AL (2013) A parallel matheuristic for the technician routing and scheduling problem. Optim. Lett. 7(7):1525–1535.Crossref, Google Scholar
• Powell WB (2011) Approximate Dynamic Programming: Solving the Curses of Dimensionality (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
• Powell WB (2019) A unified framework for stochastic optimization. Eur. J. Oper. Res. 275(3):795–821.Crossref, Google Scholar
• Powell W (2022) Reinforcement Learning and Stochastic Optimization: A Unified Framework for Sequential Decisions (John Wiley & Sons, Hoboken, NJ).Crossref, Google Scholar
• Prak D, Saccani N, Syntetos A, Teunter R, Visintin F (2017) The repair kit problem with positive replenishment lead times and fixed ordering costs. Eur. J. Oper. Res. 261(3):893–902.Crossref, Google Scholar
• Rippe C, Kiesmüller GP (2023) The repair kit problem with imperfect advance demand information. Eur. J. Oper. Res. 304(2):558–576.Crossref, Google Scholar
• Saccani N, Visintin F, Mansini R, Colombi M (2017) Improving spare parts management for field services: A model and a case study for the repair kit problem. IMA J. Management Math. 28:185–204.Google Scholar
• Smith SA, Chambers JC, Shlifer E (1980) Note-optimal inventories based on job completion rate for repairs requiring multiple items. Management Sci. 26(8):849–854.Link, Google Scholar
• Spall JC (1992) Multivariate stochastic approximation using a simultaneous perturbation gradient approximation. IEEE Trans. Automatic Control 37(3):332–341.Crossref, Google Scholar
• Spall JC (1998) Implementation of the simultaneous perturbation algorithm for stochastic optimization. IEEE Trans. Aerospace Electronic Systems 34(3):817–823.Crossref, Google Scholar
• Sutton RS, McAllester D, Singh S, Mansour Y (1999) Policy gradient methods for reinforcement learning with function approximation. Proc. Adv. Neural Inform. Processing Systems 12 (NIPS 1999) (MIT Press, Cambridge, MA), 1057–1063.Google Scholar
• Terryn E (2019) A right to repair? Toward sustainable remedies in consumer law. Eur. Rev. Private Law 27(4):851–873.Crossref, Google Scholar
• Teunter RH (2006) The multiple-job repair kit problem. Eur. J. Oper. Res. 175(2):1103–1116.Crossref, Google Scholar
• Ulmer MW (2017) Approximate Dynamic Programming for Dynamic Vehicle Routing, vol. 1 (Springer Nature, Cham, Switzerland).Crossref, Google Scholar
• Ulmer MW, Soeffker N, Mattfeld DC (2018) Value function approximation for dynamic multi-period vehicle routing. Eur. J. Oper. Res. 269(3):883–899.Crossref, Google Scholar
• Ulmer MW, Goodson JC, Mattfeld DC, Hennig M (2019) Offline–online approximate dynamic programming for dynamic vehicle routing with stochastic requests. Transportation Sci. 53(1):185–202.Link, Google Scholar
• Van Heeswijk WJ, Mes MR, Schutten JM (2019) The delivery dispatching problem with time windows for urban consolidation centers. Transportation Sci. 53(1):203–221.Link, Google Scholar
• Verbraucherzentrale-Thüringen (2021) Reparaturbonus thüringen. Accessed January 5, 2022, https://www.vzth.de/reparaturbonus-thueringen-60994.Google Scholar