Improved CLP scheduling with task intervals
Proceedings of the eleventh international conference on Logic programming
Fast planning through planning graph analysis
Artificial Intelligence
Computational intelligence: a logical approach
Computational intelligence: a logical approach
Resource-Based vs. Task-Based Approaches for Scheduling Problems
ISMIS '96 Proceedings of the 9th International Symposium on Foundations of Intelligent Systems
Scaling up Planning by Teasing out Resource Scheduling
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Modelling Resource Transitions in Constraint-Based Scheduling
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
A New Multi-resource cumulatives Constraint with Negative Heights
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Visopt ShopFloor: On the Edge of Planning and Scheduling
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
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Planning and scheduling attracts an unceasing attention of computer science community. However, despite of similar character of both tasks, in most current systems planning and scheduling problems are solved independently using different methods. Recent development of Constraint Programming brings a new breeze to these areas. It allows using the same techniques for modelling planning and scheduling problems as well as exploiting successful methods developed in Artificial Intelligence and Operations Research. In the paper we analyse the problems behind planning and scheduling in complex process environments and we propose to enhance the traditional schedulers by planning capabilities to solve these problems. We argue for using dynamic models to capture such mixed planning and scheduling environment. Despite of studying the proposed framework using the complex process environment background we believe that the results are applicable in general to other (nonproduction) problem areas where mixed planning and scheduling capabilities are desirable.