Minimizing total tardiness on one machine is NP-hard
Mathematics of Operations Research
Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
Memetic algorithms: a short introduction
New ideas in optimization
Parallel machine scheduling with earliness and tardiness penalties
Computers and Operations Research
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Improved Large-Step Markov Chain Variants for the Symmetric TSP
Journal of Heuristics
Design of Iterated Local Search Algorithms
Proceedings of the EvoWorkshops on Applications of Evolutionary Computing
An Iterated Dynasearch Algorithm for the Single-Machine Total Weighted Tardiness Scheduling Problem
INFORMS Journal on Computing
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The multi-processor total tardiness problem (MPTTP)is an NP-hard scheduling problem, in which the the goal is to minimise the tardness of a set of jobs that are processed on a number of processors. Exact algorithms like branch and bound have proven to be impractical for the MPTTP, leaving stochastic local search (SLS) algorithms as the main alternative to find high-quality schedules. Among the available SLS techniques, iterated local search (ILS) has been shown to be an effective algorithm for the single processor case. Therefore, here we extend this technique to the multi-processor case, but our computational results indicate that ILS performance is not fully satisfying. To enhance ILS performance, we consider the use of population-based ILS extensions. Our final experimental results show that the usage of a population of search trajectories yields a more robust algorithm capable of finding best known solutions to difficult instances more reliably and in less computation time than a single ILS trajectory.