混合整数线性规划自动化测试装配R包的比较

IF 0.6 Q3 SOCIAL SCIENCES, INTERDISCIPLINARY
Michael R. Peabody
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引用次数: 0

摘要

许多组织在测试装配过程中使用某种形式的自动化;完全由算法或启发式构造的。然而,启发式模型的一个问题是,当测试组装问题发生变化时,整个模型可能需要重新概念化和重新编码。相反,混合整数规划(MIP)是测试装配问题的数学表示,它寻找统计上最优的解决方案。因为MIP是一种数学表示,所以对测试装配问题的更改通常只涉及对编程的微小更改。本文重点比较了两个免费和开源的R混合整数线性规划包:inlinelpSolveAPI和inlineompr。编程风格(提供代码)、易用性、运行时和其他考虑因素将被检查。来自其他开源平台(如Python、Julia)的求解器也将被讨论。还提供了代码和示例数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Comparison of R Packages for Automated Test Assembly with Mixed-Integer Linear Programming
ABSTRACT Many organizations utilize some form of automation in the test assembly process; either fully algorithmic or heuristically constructed. However, one issue with heuristic models is that when the test assembly problem changes the entire model may need to be re-conceptualized and recoded. In contrast, mixed-integer programming (MIP) is a mathematical representation of the test assembly problem that looks for the statistically optimal solution. Because MIP is a mathematical representation, changes to the test assembly problem typically involve only minor changes to the programming. This review focuses on comparing two free and open-source R packages for mixed integer linear programming: inlinelpSolveAPI and inlineompr. Programming style (with code provided), ease of use, run time, and other considerations will be examined. Solvers from other open-source platforms (e.g. Python, Julia) will also be discussed. Code and sample data are also provided.
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来源期刊
Measurement-Interdisciplinary Research and Perspectives
Measurement-Interdisciplinary Research and Perspectives SOCIAL SCIENCES, INTERDISCIPLINARY-
CiteScore
1.80
自引率
0.00%
发文量
23
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