Authors:
Roman Barták
and
Marek Vlk
Affiliation:
Charles University in Prague and Faculty of Mathematics and Physics, Czech Republic
Keyword(s):
Schedule Updates, Rescheduling, Predictive-Reactive Scheduling, Constraint Satisfaction, Resource Failure.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Constraint Satisfaction
;
Formal Methods
;
Industrial Applications of AI
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Planning and Scheduling
;
Reactive AI
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
;
Uncertainty in AI
Abstract:
One of the classical problems of real-life production scheduling is dynamics of manufacturing environments
with new production demands coming and breaking machines during the schedule execution. Simple
rescheduling from scratch in response to unexpected events occurring on the shop floor may require excessive
computation time. Moreover, the recovered schedule may be deviated prohibitively from the ongoing
schedule. This paper studies two methods how to modify a schedule in response to a resource failure: rightshift
of affected activities and simple temporal network recovery. The importance is put on the speed of the
rescheduling procedures as well as on the minimum deviation from the original schedule. The scheduling
model is motivated by the FlowOpt project, which is based on Temporal Networks with Alternatives and
supports simple temporal constraints between the activities.