Sequencing with earliness and tardiness penalties: a review
Operations Research
Computers and Industrial Engineering
Common due-date determination and sequencing using tabu search
Computers and Operations Research
Using tabu search to solve the common due date early/tardy machine scheduling problem
Computers and Operations Research
A composite heuristic for the single machine early/tardy job scheduling problem
Computers and Operations Research
Mechanical engineering design optimization by differential evolution
New ideas in optimization
How to solve it: modern heuristics
How to solve it: modern heuristics
Benchmarks for scheduling on a single machine against restrictive and unrestrictive common due dates
Computers and Operations Research
Journal of Global Optimization
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Population set-based global optimization algorithms: some modifications and numerical studies
Computers and Operations Research
Computers and Operations Research
An effective hybrid DE-based algorithm for multi-objective flow shop scheduling with limited buffers
Computers and Operations Research
A DE-based approach to no-wait flow-shop scheduling
Computers and Industrial Engineering
A differential evolution approach for NTJ-NFSSP with SDSTs and RDs
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Expert Systems: The Journal of Knowledge Engineering
Information Sciences: an International Journal
Parallel machine scheduling with splitting jobs by a hybrid differential evolution algorithm
Computers and Operations Research
Permutation flow-shop scheduling using a hybrid differential evolution algorithm
International Journal of Computing Science and Mathematics
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The problem of scheduling multiple jobs on a single machine so that they are completed by a common specified date is addressed in this paper. This type of scheduling set costs depend on whether a job is finished before (earliness) or after (tardiness) the specified due date. The objective is to minimize a summation of earliness and tardiness penalty costs. Minimizing these costs pushes the completion time of each job as close as possible to the due date. The use of differential evolution as the optimization heuristic to solve this problem is investigated in this paper. Computational experiments over multiple (280 in total) public benchmark problems with up to 1000 jobs to be scheduled show the effectiveness of the proposed approach. The results obtained are of high quality putting new upper bounds to 60% of the benchmark instances.