Planning, Scheduling, and Uncertainty in the Sequence of Future Events

TL;DR

This paper discusses the challenges of scheduling under uncertainty in manufacturing, emphasizing the importance of adaptive planning and the principle of least commitment for efficient robot operation despite unpredictable future events.

Contribution

It introduces the principle of least commitment as a key strategy for adaptive scheduling in uncertain environments, illustrated through vision-directed assembly.

Findings

Adaptive scheduling improves efficiency under uncertainty.

Least commitment principle enables intelligent robot operation.

Scheduling methods can handle significant temporal uncertainty.

Abstract

Scheduling in the factory setting is compounded by computational complexity and temporal uncertainty. Together, these two factors guarantee that the process of constructing an optimal schedule will be costly and the chances of executing that schedule will be slight. Temporal uncertainty in the task execution time can be offset by several methods: eliminate uncertainty by careful engineering, restore certainty whenever it is lost, reduce the uncertainty by using more accurate sensors, and quantify and circumscribe the remaining uncertainty. Unfortunately, these methods focus exclusively on the sources of uncertainty and fail to apply knowledge of the tasks which are to be scheduled. A complete solution must adapt the schedule of activities to be performed according to the evolving state of the production world. The example of vision-directed assembly is presented to illustrate that the principle of least commitment, in the creation of a plan, in the representation of a schedule, and in the execution of a schedule, enables a robot to operate intelligently and efficiently, even in the presence of considerable uncertainty in the sequence of future events.