Aggregation and Disaggregation Techniques and Methodology in Optimization

David F. Rogers, Robert D. Plante, Richard T. Wong and James R. Evans
Additional contact information
David F. Rogers: University of Cincinnati, Cincinnati, Ohio
Robert D. Plante: Purdue University, West Lafayette, Indiana
Richard T. Wong: AT&T Bell Laboratories, Holmdel, New Jersey
James R. Evans: University of Cincinnati, Cincinnati, Ohio

Operations Research, 1991, vol. 39, issue 4, 553-582

Abstract: A fundamental issue in the use of optimization models is the tradeoff between the level of detail and the ease of using and solving the model. Aggregation and disaggregation techniques have proven to be valuable tools for manipulating data and determining the appropriate policies to employ for this tradeoff. Furthermore, aggregation and disaggregation techniques offer promise for solving large-scale optimization models, supply a set of promising methodologies for studying the underlying structure of both univariate and multivariate data sets, and provide a set of tools for manipulating data for different levels of decision makers. In this paper, we develop a general framework for aggregation and disaggregation methodology, survey previous work regarding aggregation and disaggregation techniques for optimization problems, illuminate the appropriate role of aggregation and disaggregation methodology for optimization applications, and propose future research directions.

Keywords: programming; linear integer: aggregation/disaggregation and surrogate programming; production/scheduling; approximations/heuristics: large-scale models; statistics; cluster analysis: aggregation process (search for similar items in EconPapers)
Date: 1991
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Citations: View citations in EconPapers (53)

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