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A Hybrid Framework for Over-Constrained Generalized

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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.

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Authors and Affiliations

  1. Department of IEEM, Hong Kong University of Science and Technology, Clearwater Bay

    A. Lim

  2. School of Business, Singapore Management University, 469 Bukit Timah Road, Singapore

    B Rodrigues

  3. Defense Science and Technology Agency, Singapore

    R. Thangarajoo

  4. Department of Computer Science, National University of Singapore, 3 Science Drive 2, Singapore

    F. Xiao

Authors
  1. A. Lim
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  2. B Rodrigues
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  3. R. Thangarajoo
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  4. F. Xiao
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Lim, A., Rodrigues, B., Thangarajoo, R. et al. A Hybrid Framework for Over-Constrained Generalized. Artificial Intelligence Review 22, 211–243 (2004). https://doi.org/10.1007/s10462-004-1286-8

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