A Manufacturing-as-a-Service Scheduling Problem and its Tripartite Decision Perspective

The Manufacturing-as-a-Service (MaaS) paradigm offers flexible and on-demand manufacturing services through digital platforms, including the case where customer facilities outsource tasks to providers of manufacturing resources (i.e., machines). A key challenge lies in matching the tasks and resources requested by customers with suitable providers according to their specific requirements. Within the limits of the available requirements and their level of accuracy, task-machine eligibility can be represented in various ways, such as binary values or score-based weights. These requirements are then integrated into the subsequent scheduling problem to be solved by the MaaS platform. In this paper, we extend a scheduling model by integrating task-machine eligibility score-based weights as input to the scheduling problem via eligibility level constraints. Numerical experiments are conducted on randomly generated small instances based on benchmark schemes to study the implications of the introduced task-machine eligibility levels. The quality of sequences proposed within the MaaS framework is empirically evaluated for customers and providers in both aggregate and individual manners.