The scheduling of jobs and resources In a manufacturing environment is important because o f its basic impact on production costs, but is difficult because of the problems of combinatorial complexity and executwnal uncertainty. Scheduling suffers from combinatorial complexity because there are a very large number of schedules which can be generated f o r a set of jobs and resources, but there is no good way to choose between the options prior to execution. Scheduling is complicated by executional uncertainty in that unforeseeable events will almost certainly occur to disrupt any particular schedule once execution commences. This paper describes a novel approach to scheduling which overcomes these two difficulties. Implementation of the approach requires a new representation for schedules and techniques for knowledge-based reasoning about this representation. The issues involved in performing the knowledge-based reasoning are described and illustrated by examples drawn from the domain of robotic assembly.

Impressive strides have been made in dealing with the spatial complexity of robotic assembly tasks. Unfortunately, advances in dealing with temporal complexity have not kept pace. It is proposed that one reason for this deficiency is the unnecessary confounding of planning and scheduling. These two activities are differentiated on the basis of knowledge required/knowledge available at robot programming time. It is suggested that giving the robot the ability to reason opportunistically over knowledge of part availability at run time is a practical, efficient way to streamline assembly tasks. Initial experimental results are presented to substantiate this conclusion.