期刊
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
卷 23, 期 1, 页码 117-129出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TEVC.2018.2835565
关键词
Business process; constraint acquisition; linear programming (LP); model induction; wine quality
资金
- Poznan University of Technology, Poland [09/91/DSMK/0634]
- Foundation for Polish Science
- National Science Centre, Poland [2014/15/B/ST6/05205]
Mathematical programming (MP) models are common in optimization of real-world processes. Models are usually built by optimization experts in an iterative manner: an imperfect model is continuously improved until it approximates the reality well-enough and meets all technical requirements (e.g., linearity). To facilitate this task, we propose a genetic one-class constraint synthesis method (GOCCS). Given a set of exemplary states of normal operation of a business process, GOCCS synthesizes constraints in linear programming or nonlinear programming form. The synthesized constraints can be then paired with an arbitrary objective function and supplied to an off-the-shelf solver to find optimal parameters of the process. We assess GOCCS on three families of MP benchmarks and conclude promising results. We also apply it to a real-world process of wine production and optimize that process.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据