@inproceedings {pub6738,
	title = {Annoyance Modeling in Cooperative Personnel Scheduling},
	author = {Christiane Attig AND Johannes Varga AND Tim Schrills AND Tobias Rodemann AND Guenther Raidl},
	year = {2026},
	abstract = {Although many studied settings in algorithmic optimization affect humans, human factors and user behavior are often neglected. For instance, optimization algorithms that require human input seldomly model or consider humans cognitive states, even though these might affect data input quality and consequently computational results. The objective of this work is to demonstrate how annoyance{\textemdash}as one prototypical user state that can be elicited when systems repeatedly request user input, particularly when requested at inconvenient times{\textemdash}can be modeled in a scheduling setting and used to improve algorithmic processes. We thereby position algorithmic optimization as an application domain for human factors research by showing how to translate concepts from engineering psychology into algorithmic mechanisms. In particular, we study an industrial job scheduling scenario in which a cooperative scheduling system coordinates employee (i.e., user) access to a shared machine. The system relies on users{\textquoteright} cooperation: through few rounds of interaction, it requests availability information and iteratively improves an initial, suboptimal schedule, subject to individual availability constraints and the goal of minimizing operational costs. While the approach of having few interaction rounds is assumed to improve the user experience compared to classical systems, which ask for detailed availabilities upfront, it still puts a burden on users. We argue that the interruptions through system interaction can cause annoyance in users, which might result in careless responding or even cooperation breakdown, ultimately leading to poorer data quality and non-optimal schedules. To investigate the implications of considering annoyance in this algorithmic optimization application, we perform a simulation of the interaction between the users and the scheduling system. Simulated users have behavioral rules that are based on individual availability profiles. Annoyance in users is assumed to build up with each interaction round. Moreover, each user is assumed to have an individual annoyance threshold beyond which they stop responding truthfully. We use a scheduling system from the literature that features an optimization component and an internal user model to anticipate replies to questions and integrate the annoyance model to make it continuously minimize and weigh the users{\textquoteright} annoyance levels against operational costs. Results from 45,360 simulation runs showed that the performance in terms of achieved cost reductions significantly depends on algorithm parameters. Integrating the annoyance model makes the algorithm more robust to parameter changes, translating into a better performance under suboptimal parameter choices. The results thus underline that optimizing for both cost reduction and annoyance prevention leads to a better performance in practice, compared to cost reduction alone. More broadly, the results demonstrate that an exchange between the scientific fields can be beneficial: interactive scheduling, and more generally interactive optimization, offers engineering psychology a domain for studying user experience while engineering psychology provides algorithm designers with concepts to better account for the human cooperation their systems depend on.
        },
	publisher = {AFFE International},
	url = {https://openaccess.cms-conferences.org/publications/book/978-1-964867-96-0/article/978-1-964867-96-0_5},
	booktitle = {17th International Conference on Applied Human Factors and Ergonomics and the Affiliated Conferences},
	editor = {Tareq Z. Ahram},
	city = {Istanbul, Turkey},
	volume = {220},
	pages = {49-59},
	series = {Human Factors in Simulation, Software and Systems Engineering}
}