An AI Planning Approach to Emergency Material Scheduling Using Numerical PDDL
- DOI
- 10.2991/978-94-6463-010-7_7How to use a DOI?
- Keywords
- Artificial Intelligence; AI planning; Emergency Material; Material Scheduling
- Abstract
Emergency material planning and scheduling aims to schedule emergency material to destination efficiently to reduce property losses and personal casualties caused by material shortage. Existing researches usually rely on optimization of transportation routes. They ignore the rich scenarios in which human and material resources participate in scheduling simultaneously. Moreover, sudden emergencies may involve the supply of multiple material demand points. To address these issues, we propose an AI planning approach to model emergency material scheduling domain and construct a standard planning task using PDDL. Then a state-of-the-art planner is employed to solve the generated planning task. Experimental results show that the proposed approach can fit the actual situation of emergencies and give high-quality instruction to guide emergency material planning and scheduling.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Liping Yang AU - Ruishi Liang PY - 2022 DA - 2022/12/02 TI - An AI Planning Approach to Emergency Material Scheduling Using Numerical PDDL BT - Proceedings of the 2022 International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID 2022) PB - Atlantis Press SP - 47 EP - 54 SN - 2589-4919 UR - https://doi.org/10.2991/978-94-6463-010-7_7 DO - 10.2991/978-94-6463-010-7_7 ID - Yang2022 ER -