This thesis proposes and develops an interface and model in which advanced optimisation for general employee scheduling is made available to non-experts in computer science or optimisation. The interface teaches, guides, configures, dynamically creates a constraint programming (CP) model, iteratively improves, decreases black box properties, increases trust in the outcome, and complies with relevant European Union Artificial Intelligence law. The objective of this study is to allow a wider range of organisations to take advantage of CP techniques, with the potential to greatly improve efficiency, reduce unfairness, meet company goals, and improve employee satisfaction.

Employees are assigned to personalised shifts based on the expected demands of departments within an organisation, these are set by the domain expert in the field of employee scheduling. Next, to tailor the model to the organisation’s needs, the domain expert is guided in setting both the restrictive assumptions and priorities of shift assignment. To optimise the generated optimal schedule, it is encouraged to create and compare multiple sets of configurations and subsequent schedules. Historical demand data, organisation structure, contract information, and employee preferences are included. Multiple visual design iterations have been made, after which a working interface has been developed and improved iteratively in conjunction with experts in the field. Validations with external domain experts from various industries and organisations have shown that the interface performed effectively in supporting the objectives. Further research can be done to improve the speed of solving, implement diversity of solutions, support for highly custom constraints based on natural language, or interface reusability for other optimisation problems.