DISTANCE-SAT: complexity and algorithms
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Two-machine flowshop scheduling with a secondary criterion
Computers and Operations Research
Application of an optimization problem in max-plus algebra to scheduling problems
Discrete Applied Mathematics - Special issue: International symposium on combinatorial optimization CO'02
A robust approach for the single machine scheduling problem
Journal of Scheduling
Solution-guided multi-point constructive search for job shop scheduling
Journal of Artificial Intelligence Research
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Nogood recording from restarts
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Closing the open shop: contradicting conventional wisdom
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
Worst-case evaluation of flexible solutions in disjunctive scheduling problems
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part III
Job shop scheduling with setup times and maximal time-lags: a simple constraint programming approach
CPAIOR'10 Proceedings of the 7th international conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems
| Hi-index | 0.00 |
In a two-machine flow shop scheduling problem, the set of ε -approximate sequences (i.e. , solutions within a factor 1+ε of the optimal) can be mapped to the vertices of a permutation lattice. We introduce two approaches, based on properties derived from the analysis of permutation lattices, for characterizing large sets of near-optimal solutions. In the first approach, we look for a sequence of minimum level in the lattice, since this solution is likely to cover many optimal or near-optimal solutions. In the second approach, we look for all sequences of minimal level, thus covering all ε -approximate sequences. Integer linear programming and constraint programming models are first proposed to solve the former problem. For the latter problem, a direct exploration of the lattice, traversing it by a simple tree search procedure, is proposed. Computational experiments are given to evaluate these methods and to illustrate the interest and the limits of such approaches.