Abstract
When solving a job scheduling problem that involves humans, the times in which they are available must be taken into account. For practical acceptance of a scheduling tool, it is further crucial that the interaction with the humans is kept simple and to a minimum. Requiring users to fully specify their availability times is typically not reasonable. We consider a scenario in which initially users only suggest single starting times for their jobs and an optimized schedule shall then be found within a small number of interaction rounds. In each round users may only be suggested a small set of alternative time intervals, which are accepted or rejected. To make the best out of these limited interaction possibilities, we propose an approach that utilizes integer linear programming and a theoretically derived probability calculation for the users’ availabilities based on a Markov model. Educated suggestions of alternative time intervals for performing jobs are determined from these acceptance probabilities as well as the optimization’s current state. The approach is experimentally evaluated and compared to diverse baselines. Results show that an initial schedule can be quickly improved over few interaction rounds, and the final schedule may come close to the solution of the full-knowledge case despite the limited interaction.
J. Varga acknowledges the financial support from Honda Research Institute Europe.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Aghaei-Pour, P., Rodemann, T., Hakanen, J., Miettinen, K.: Surrogate assisted interactive multiobjective optimization in energy system design of buildings. Optim. Eng. 23(1), 303–327 (2022)
Anghinolfi, D., Paolucci, M., Ronco, R.: A bi-objective heuristic approach for green identical parallel machine scheduling. Eur. J. Oper. Res. 289(2), 416–434 (2021)
Blum, A.: Empirical support for winnow and weighted-majority algorithms: results on a calendar scheduling domain. Mach. Learn. 26(1), 5–23 (1997)
Cheng, J., Chu, F., Zhou, M.: An improved model for parallel machine scheduling under time-of-use electricity price. IEEE Trans. Autom. Sci. Eng. 15(2), 896–899 (2018)
Ding, J.Y., Song, S., Zhang, R., Chiong, R., Wu, C.: Parallel machine scheduling under time-of-use electricity prices: new models and optimization approaches. IEEE Trans. Autom. Sci. Eng. 13(2), 1138–1154 (2016)
Jatschka, T., Raidl, G.R., Rodemann, T.: A general cooperative optimization approach for distributing service points in mobility applications. Algorithms 14(8), 232 (2021). https://www.mdpi.com/1999-4893/14/8/232
Mitchell, T.M., Caruana, R., Freitag, D., McDermott, J., Zabowski, D., et al.: Experience with a learning personal assistant. Commun. ACM 37(7), 80–91 (1994)
Saberi-Aliabad, H., Reisi-Nafchi, M., Moslehi, G.: Energy-efficient scheduling in an unrelated parallel-machine environment under time-of-use electricity tariffs. J. Clean. Prod. 249, 119393 (2020)
Saha, S., Minku, L.L., Yao, X., Sendhoff, B., Menzel, S.: Exploiting linear interpolation of variational autoencoders for satisfying preferences in evolutionary design optimization. In: 2021 IEEE Congress on Evolutionary Computation, pp. 1767–1776 (2021)
Wang, S., Wang, X., Yu, J., Ma, S., Liu, M.: Bi-objective identical parallel machine scheduling to minimize total energy consumption and makespan. J. Clean. Prod. 193, 424–440 (2018)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Varga, J., Raidl, G.R., Rönnberg, E., Rodemann, T. (2023). Interactive Job Scheduling with Partially Known Personnel Availabilities. In: Dorronsoro, B., Chicano, F., Danoy, G., Talbi, EG. (eds) Optimization and Learning. OLA 2023. Communications in Computer and Information Science, vol 1824. Springer, Cham. https://doi.org/10.1007/978-3-031-34020-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-031-34020-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-34019-2
Online ISBN: 978-3-031-34020-8
eBook Packages: Computer ScienceComputer Science (R0)