Generating Experimental Data for Computational Testing with Machine Scheduling Applications

Nicholas G. Hall () and Marc E. Posner ()
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Nicholas G. Hall: Fisher College of Business, Department of Management Sciences, The Ohio State University, Columbus, Ohio 43210-1399
Marc E. Posner: Department of Industrial and Systems Engineering, The Ohio State University, Columbus, Ohio 43210-1271

Operations Research, 2001, vol. 49, issue 6, 854-865

Abstract: 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.

Keywords: Simulation; random variable generation: methods for generating random data; Production/scheduling: experimental data for testing algorithms and heuristics (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (43)

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