Computers and Operations Research
Access control in heterogeneous multichannel wireless networks
InterSense '06 Proceedings of the first international conference on Integrated internet ad hoc and sensor networks
Parallel machine scheduling through column generation: minimax objective functions
ESA'06 Proceedings of the 14th conference on Annual European Symposium - Volume 14
Proportionate flexible flow shop scheduling via a hybrid constructive genetic algorithm
Expert Systems with Applications: An International Journal
Software product release planning through optimization and what-if analysis
Information and Software Technology
Optimal and heuristic solution methods for a multiprocessor machine scheduling problem
Computers and Operations Research
Computers and Industrial Engineering
SearchCol: metaheuristic search by column generation
HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
Computers and Operations Research
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Two branch-and-bound algorithms for the robust parallel machine scheduling problem
Computers and Operations Research
Scheduling unrelated parallel machines computational results
WEA'06 Proceedings of the 5th international conference on Experimental Algorithms
The Fixed-Charge Shortest-Path Problem
INFORMS Journal on Computing
Scheduling with an outsourcing option on both manufacturer and subcontractors
Computers and Operations Research
A branch-and-price algorithm for the multi-activity multi-task shift scheduling problem
Journal of Scheduling
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Parallel machine scheduling problems concern the scheduling of n jobs on m machines to minimize some function of the job completion times. If preemption is not allowed, then most problems are not only NP-hard, but also very hard from a practical point of view. In this paper, we show that strong and fast linear programming lower bounds can be computed for an important class of machine scheduling problems with additive objective functions. Characteristic of these problems is that on each machine the order of the jobs in the relevant part of the schedule is obtained through some priority rule. To that end, we formulate these parallel machine scheduling problems as a set covering problem with an exponential number of binary variables, n covering constraints, and a single side constraint. We show that the linear programming relaxation can be solved efficiently by column generation because the pricing problem is solvable in pseudo-polynomial time. We display this approach on the problem of minimizing total weighted completion time on m identical machines. Our computational results show that the lower bound is singularly strong and that the outcome of the linear program is often integral. Moreover, they show that our branch-and-bound algorithm that uses the linear programming lower bound outperforms the previously best algorithm.