The single machine early/tardy problem
Management Science
Sequencing with earliness and tardiness penalties: a review
Operations Research
Single-Machine Scheduling of Unit-Time Jobs with Earliness and Tardiness Penalties
Mathematics of Operations Research
A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem
Computers and Operations Research
Computers and Industrial Engineering - Special issue: Focussed issue on applied meta-heuristics
Scheduling with Inserted Idle Time: Problem Taxonomy and Literature Review
Operations Research
The One-Machine Problem with Earliness and Tardiness Penalties
Journal of Scheduling
Single machine scheduling with early and quadratic tardy penalties
Computers and Industrial Engineering
Improved heuristics for the early/tardy scheduling problem with no idle time
Computers and Operations Research
Computers and Industrial Engineering
Biased random-key genetic algorithms for combinatorial optimization
Journal of Heuristics
Dispatching heuristics for the single machine weighted quadratic tardiness scheduling problem
Computers and Operations Research
Computers and Operations Research
Computers and Industrial Engineering
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In this paper, we consider the single machine scheduling problem with quadratic earliness and tardiness costs, and no machine idle time. We propose several dispatching heuristics, and analyse their performance on a wide range of instances. The heuristics include simple and widely used scheduling rules, as well as adaptations of those rules to a quadratic objective function. We also propose heuristic procedures that specifically address both the earliness and the tardiness penalties, as well as the quadratic cost function. Several improvement procedures were also analysed. These procedures are applied as an improvement step, once the heuristics have generated a schedule. The computational experiments show that the best results are provided by the heuristics that explicitly consider both early and tardy costs, and the quadratic objective function. Therefore, it is indeed important to specifically address the quadratic feature of the cost function, instead of simply using procedures originally developed for a linear objective function. The heuristics are quite fast, and are capable of quickly solving even very large instances. The use of an improvement step is recommended, since it usually improves the solution quality with little additional computational effort.