Optimal and heuristic solution methods for a multiprocessor machine scheduling problem
Computers and Operations Research
Load balancing in project assignment
Computers and Operations Research
A composite algorithm for multiprocessor scheduling
Journal of Heuristics
Two branch-and-bound algorithms for the robust parallel machine scheduling problem
Computers and Operations Research
Improved bin completion for optimal bin packing and number partitioning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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Given a set of jobs with associated processing times, and a set of identical machines, each of which can process at most one job at a time, the parallel machine scheduling problem is to assign each job to exactly one machine so as to minimize the maximum completion time of a job. The problem is strongly NP-hard and has been intensively studied since the 1960s. We present a metaheuristic and an exact algorithm and analyze their average behavior on a large set of test instances from the literature. The metaheuristic algorithm, which is based on a scatter search paradigm, computationally proves to be highly effective and capable of solving to optimality a very high percentage of the publicly available test instances. The exact algorithm, which is based on a specialized binary search and a branch-and-price scheme, was able to quickly solve to optimality all remaining instances.