Flexibility in a Knowledge-Based System for Solving Dynamic Resource-Constrained Scheduling Problems

The resource-constrained scheduling problem (RCSP) involves the assignment of a limited set of resources to a collection of tasks, with the intent of satisfying some particular qualitative objective, under a variety of technological and temporal constraints. Real-world environments, however, introduce a variety of complications to the standard RCSP. The dynamic resource-constrained scheduling problem describes a class of real-world RCSPs that exist within the context of dynamic and unpredictable environments, where the details of the problem are often incomplete, and subject to change over time, without notice. Previous approaches to solving resource-constrained scheduling problems failed to focus on the dynamic nature of real-world environments. The scheduling process occurs away from the environment in which the resulting schedule is executed. Complete prior knowledge of the order set is assumed, and reaction to changes in the environment, if at all, is limited. We have developed a generic, multi-faceted, knowledge-based approach to solving dynamic resource-constrained scheduling problems, which focuses on issues of flexibility during the solution process to enable effective reaction to dynamic environments. Our approach is characterized by a highly opportunistic control scheme that provides the ability to adapt quickly to changes in the environment, a least-commitment scheduling procedure that preserves maneuverability by explicitly incorporating slack time into the developing schedule, and the systematic consultation of a range of relevant scheduling perspectives at key decision-making points that provides an informed view of the current state of problem-solving at all times. The Dynamic Scheduling System (DSS) is a working implementation of our scheduling approach, capable of representing a wide range of dynamic RCSPs, and producing quality schedules under a variety of real-world conditions. It handles a number of additional domain complexities, such as inter-order tasks and mobile resources with significant travel requirements. We discuss our scheduling approach and its application to two different RCSP domains, and evaluate its effectiveness in each, using special application systems built with DSS.