A Hybrid Framework for Over-Constrained Generalized

Authors:
A. Lim;B. Rodrigues;R. Thangarajoo;F. Xiao
Affiliations:
Department of IEEM, Hong Kong University of Science and Technology, Hong Kong;School of Business, Singapore Management University, Singapore 259756;Defense Science and Technology Agency, Singapore 109676;Department of Computer Science, National University of Singapore, Singapore 117543
Venue:
Artificial Intelligence Review
Year:
2003

Citing 16
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Abstract

In this work we study an over-constrained scheduling problem where constraints cannot be relaxed. This problem originates from a local defense agency where activities to be scheduled are strongly ranked in a priority scheme determined by planners ahead of time and operational real-time demands require solutions to be available almost immediately. A hybrid framework is used which is composed of two levels. A high-level component explores different orderings of activities by priorities using Tabu Search or Genetic Algorithm heuristics, while in a low-level component, constraint programming and minimal critical sets are used to resolve conflicts. Real-data used to test the algorithm show that a larger number of high priority activities are scheduled when compared to a CP-based system used currently. Further tests were performed using randomly generated data and results compared with CPLEX. The approach provided in this paper offers a framework for problems where all constraints are treated as hard constraints and where conflict resolution is achieved only through the removal of variables rather than constraints.