Annoyance Modeling in Cooperative Personnel Scheduling

Abstract

Cooperative algorithmic systems that depend on human input seldom model users' cognitive or affective states, even though these states can influence data input quality and, consequently, computational results. This paper demonstrates how annoyance – a prototypical user state elicited when systems repeatedly request input – can be modeled in a scheduling context and integrated into an algorithmic optimization process. We consider an industrial job scheduling scenario in which a cooperative scheduling system coordinates employee access to a shared machine. Rather than requesting all availability information upfront, the system iteratively improves an initial suboptimal schedule by querying users across multiple interaction rounds, subject to individual availability constraints and the goal of minimizing operational costs. Such repeated interactions can cause annoyance to accumulate, potentially resulting in careless responding or cooperation breakdown. To investigate the implications of modeling annoyance in this context, we simulated interactions between users and the scheduling system. Simulated users followed behavioral rules based on individual availability profiles, with annoyance building up across interaction rounds until an individual threshold was exceeded, beyond which users stopped responding truthfully. Results from 45,360 simulation runs showed that integrating annoyance modeling makes the algorithm more robust to parameter variations and yields better performance under suboptimal parameter choices than optimizing for cost reduction alone. These findings demonstrate that engineering psychology and algorithm design can mutually benefit one another: interactive scheduling offers a domain for applied user experience research, while engineering psychology provides algorithm designers with concepts to better account for users’ willingness to cooperate with algorithmic systems.

Keywords: Job Scheduling, Annoyance, Algorithmic Optimization, User Experience

DOI: 10.54941/ahfe1007693