Connections between cutting-pattern sequencing, VLSI Design, and flexible machines
Computers and Operations Research
Computers and Operations Research
Dynamic Programming to Minimize the Maximum Number of Open Stacks
INFORMS Journal on Computing
Solving Talent Scheduling with Dynamic Programming
INFORMS Journal on Computing
Automatically exploiting subproblem equivalence in constraint programming
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
A generic method for identifying and exploiting dominance relations
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Inter-instance nogood learning in constraint programming
CP'12 Proceedings of the 18th international conference on Principles and Practice of Constraint Programming
Improving combinatorial optimization: extended abstract
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We describe a new exact solver for the minimization of open stacks problem (MOSP). By combining nogood recording with a branch and bound strategy based on choosing which customer stack to close next, our solver is able to solve hard instances of MOSP some 5-6 orders of magnitude faster than the previous state of the art. We also derive several pruning schemes based on dominance relations which provide another 1-2 orders of magnitude improvement. One of these pruning schemes largely subsumes the effect of the nogood recording. This allows us to reduce the memory usage from an potentially exponential amount to a constant ∼2Mb for even the largest solvable instances. We also show how relaxation techniques can be used to speed up the proof of optimality by up to another 3-4 orders of magnitude on the hardest instances.