Abstract
AbstractThe maintenance optimization of multi-component machines has been recently formalized as an Answer Set Optimization (ASO) problem based on component selection and grouping of overlapping maintenance intervals. The motivation of the current work is to develop an extension that would integrate resources and availability constraints into this maintenance model. This article outlines an extended ASO model with the primary focus on modeling and optimizing costly maintenance resources, culminating in cost savings facilitated by the progressive development of workforce competence. The model presented in this work extends the cost function of the prior ASO formalization in a modular way with additional cost priorities concerning parallelism, workforce, and expertise. Due to the presented extensions, the complexity of the integrated maintenance model increases compared to the prior formalization.
Publisher
Springer Nature Switzerland
Reference37 articles.
1. Banbara, M., et al.: teaspoon: solving the curriculum-based course timetabling problems with answer set programming. Ann. Oper. Res. 275, 3–37 (2019). https://doi.org/10.1007/s10479-018-2757-7
2. Benner, P.: From novice to expert. Am. J. Nurs. 82(3), 402–407 (1982). https://doi.org/10.1002/nur.4770080119
3. Brewka, G., Eiter, T., Truszczynski, M.: Answer set programming at a glance. Commun. ACM 54(12), 92–103 (2011). https://doi.org/10.1145/2043174.2043195
4. Camacho, A., Icarte, R.T., Klassen, T.Q., Valenzano, R.A., McIlraith, S.A.: LTL and beyond: formal languages for reward function specification in reinforcement learning. In: IJCAI 2019, pp. 6065–6073. ijcai.org (2019). https://doi.org/10.24963/IJCAI.2019/840
5. Cao, Y., et al.: GALOIS: boosting deep reinforcement learning via generalizable logic synthesis. In: NeurIPS 2022, pp. 19930–19943 (2022)