Computers and Operations Research
Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups
Computers and Operations Research
Computers and Operations Research
An effective hybrid genetic algorithm for flow shop scheduling with limited buffers
Computers and Operations Research
Preemption in single machine earliness/tardiness scheduling
Journal of Scheduling
A guide to statistical analysis for single criteria heuristics evaluation
International Journal of Innovative Computing and Applications
Computers and Operations Research
Synthetic Optimization Problem Generation: Show Us the Correlations!
INFORMS Journal on Computing
Two-machine group scheduling problems in discrete parts manufacturing with sequence-dependent setups
Computers and Operations Research
The Coordination of Pricing and Scheduling Decisions
Manufacturing & Service Operations Management
Computers and Operations Research
Rescheduling for Job Unavailability
Operations Research
A unified approach for the evaluation of quay crane scheduling models and algorithms
Computers and Operations Research
Solving the single-machine sequencing problem using integer programming
Computers and Industrial Engineering
Algorithm engineering: bridging the gap between algorithm theory and practice
Algorithm engineering: bridging the gap between algorithm theory and practice
Capacity Allocation and Scheduling in Supply Chains
Operations Research
Journal of Intelligent Manufacturing
Review: Measuring instance difficulty for combinatorial optimization problems
Computers and Operations Research
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Scheduling of uniform parallel machines with s-precedence constraints
Mathematical and Computer Modelling: An International Journal
Parallel-machine scheduling to minimize tardiness penalty and power cost
Computers and Industrial Engineering
A new genetic algorithm for lot-streaming flow shop scheduling with limited capacity buffers
Journal of Intelligent Manufacturing
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The operations research literature provides little guidance about how data should be generated for the computational testing of algorithms or heuristic procedures. We discuss several widely used data generation schemes, and demonstrate that they may introduce biases into computational results. Moreover, such schemes are often not representative of the way data arises in practical situations. We address these deficiencies by describing several principles for data generation and several properties that are desirable in a generation scheme. This enables us to provide specific proposals for the generation of a variety of machine scheduling problems. We present a generation scheme for precedence constraints that achieves a target density which is uniform in the precedence constraint graph. We also present a generation scheme that explicitly considers the correlation of routings in a job shop. We identify several related issues that may influence the design of a data generation scheme. Finally, two case studies illustrate, for specific scheduling problems, how our proposals can be implemented to design a data generation scheme.