A planning/scheduling methodology for the constrained resource problem

Authors:
Naiping Keng;David Y. Y. Yun
Affiliations:
Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas;Department of Computer Science and Engineering, Southern Methodist University, Dallas, Texas
Venue:
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 2
Year:
1989

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Abstract

This paper presents a new planning/scheduling methodology for the constrained resource problem (CRP), in which the amount of available resources is limited and usually monotonically diminishing as the planning process progresses. The tasks are tightly-coupled since they compete for the limited resources. Two domain independent policies -- most-constraint and least-tmpact help to make this planning/scheduling approach sensitive to dynamic interactions among tasks. The most-constraint policy selects a task dynamically according to the criticality, which measures how a task is constrained by task interaction. The least-impact policy dynamically chooses a solution for the selected task according to the cruciality of each possible solution, which expresses the impact on the rest of the unachieved tasks. These policies have enhanced the operability and measurability of problem solving by planning/scheduling. Hence, this method can provide realistic and executable planning/scheduling guidelines for CRP solvers. This model has been successfully applied to several CRPs in which the amount of backtracking has been reduced dramatically.