CP-WSP: Declarative CP-SAT Framework for Configurable Work Schedules

In the field of work schedule optimization, the emergence of declarative frameworks like CP-WSP represents a significant advancement for companies managing large teams and complex shifts. This approach, based on constraint programming (CP-SAT), allows defining up to 14 hard constraints —such as labor regulations, split shifts, or night rotations— and simultaneously optimizing 15 soft objectives through a weighted penalty function. Most interestingly, all configuration is done via a JSON file, eliminating the need to modify source code when business needs change. For organizations looking to implement such solutions, having a specialized team in custom applications is essential, as each company has unique coverage requirements, employee preferences, and workload equity standards. CP-WSP introduces innovations such as shift window decomposition to schedule mandatory breaks with midpoint control, adjustable time granularity (from 30 minutes to 2 hours), and grid preprocessing for patterns crossing midnight. These capabilities are especially valuable in sectors like healthcare, logistics, or manufacturing, where demand varies every fraction of an hour and week-to-week stability is critical. Process automation through artificial intelligence not only generates optimal schedules but also analyzes hypothetical scenarios and dynamically adapts to unforeseen events. At Q2BSTUDIO, we develop custom software integrating AWS and Azure cloud services, cybersecurity, and AI agents that help companies transform data into decisions. For example, combining Power BI with optimization models allows HR managers to visualize the impact of each constraint and adjust parameters in real time. The key lies in designing a framework that, like CP-WSP, is declarative and configurable, drastically reducing implementation time and human errors. Our business intelligence services and AI agents for companies extend these capabilities to other domains, from delivery route assignment to project planning. Ultimately, adopting advanced optimization techniques, supported by a robust technological platform and expert teams in AI for businesses, makes the difference between an efficient operation and one that constantly struggles with bottlenecks.