Integer and combinatorial optimization
Integer and combinatorial optimization
A Lagrange relaxation approach for very-large-scale capacitated lot-sizing
Management Science
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
General Purpose Heuristics for Integer Programming—Part II
Journal of Heuristics
Over-Constrained Systems
Domain-independent extensions to GSAT: solving large structured satisfiability problems
IJCAI'93 Proceedings of the 13th international joint conference on Artifical intelligence - Volume 1
Solving linear pseudo-Boolean constraint problems with local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Tuning local search for satisfiability testing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
Integer optimization by local search: a domain-independent approach
Integer optimization by local search: a domain-independent approach
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Production planning is an important task in manufacturing systems. We consider a real-world capacitated lot-sizing problem (CLSP) from the process industry. Because the problem requires discrete lot-sizes, domain-specific methods from the literature are not directly applicable. We therefore approach the problem with WSAT (OIP), a new domainindependent heuristic for integer optimization which generalizes the Walks at algorithm. WSAT (OIP) performs stochastic tabu search and operates on over-constrained integer programs. We empirically compare WSAT(OIP) to a state-of the-art mixed integer programming branch-and-bound solver (CPLEX 4.0) on real problem data. We find that integer local search is considerably more robust than MIP branchand-bound in finding feasible solutions in limited time, and branch-and-bound can only solve a sub-class of the CLSP with discrete lot-sizes. With respect to production cost, both methods find solutions of similar quality.