AGGREGATION AND DISAGGREGATION TECHNIQUES AND METHODOLOGY IN OPTIMIZATION
成果类型：
Review
署名作者：
ROGERS, DF; PLANTE, RD; WONG, RT; EVANS, JR
署名单位：
Purdue University System; Purdue University; Nokia Corporation; Nokia Bell Labs; AT&T
刊物名称：
OPERATIONS RESEARCH
ISSN/ISSBN：
0030-364X
DOI：
10.1287/opre.39.4.553
发表日期：
1991
页码：
553-582
关键词：
摘要：
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 determing 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.