An Integrated Method for Planning and Scheduling to Minimize Tardiness

Authors:
J. N. Hooker
Affiliations:
Carnegie Mellon University, Pittsburgh, USA
Venue:
Constraints
Year:
2006

Citing 5
Cited 5

Branch-and-Check: A Hybrid Framework Integrating Mixed Integer Programming and Constraint Logic Programming

CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
Hybrid Benders Decomposition Algorithms in Constraint Logic Programming

CP '01 Proceedings of the 7th International Conference on Principles and Practice of Constraint Programming
A Relaxation of the Cumulative Constraint

CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Algorithms for Hybrid MILP/CP Models for a Class of Optimization Problems

INFORMS Journal on Computing
A Hybrid Method for the Planning and Scheduling

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Solving Lot-Sizing Problems on Parallel Identical Machines Using Symmetry-Breaking Constraints

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A combined CLP-MILP approach for scheduling commodities in a pipeline

Journal of Scheduling
Optimal methods for resource allocation and scheduling: a cross-disciplinary survey

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Single-Facility scheduling over long time horizons by logic-based benders decomposition

CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
Improvements of constraint programming and hybrid methods for scheduling of tests on vehicle prototypes

Constraints

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Abstract

We combine mixed integer linear programming (MILP) and constraint programming (CP) to minimize tardiness in planning and scheduling. Tasks are allocated to facilities using MILP and scheduled using CP, and the two are linked via logic-based Benders decomposition. We consider two objectives: minimizing the number of late tasks, and minimizing total tardiness. Our main theoretical contribution is a relaxation of the cumulative scheduling subproblem, which is critical to performance. We obtain substantial computational speedups relative to the state of the art in both MILP and CP. We also obtain much better solutions for problems that cannot be solved to optimality.