首页 | 本学科首页   官方微博 | 高级检索  
     检索      


Linear programming system identification
Institution:1. Department of Management and Information Systems, Kent State University, Kent, OH 44242-0001, USA;2. Department of Management, Southern Illinois University, Carbondale, IL 62901-4627, USA;3. Department of Business Administration, School of Business Administration, The Catholic University of Korea, San 43-1, Yokkok 2-Dong, Wonmi-Gu, Puchon City, Gyuonggi-Do 420-743, South Korea;4. Department of Management and Information Systems, Kent State University, Kent, OH 44242-0001, USA;1. Departamento de Fisioterapia, Universidade Federal de São Carlos (UFSCar), São Carlos, SP, Brazil;2. Department of Biomedical Sciences for Health, University of Milan, Milan, Italy;3. Department of Cardiothoracic, Vascular Anesthesia and Intensive Care, IRCCS Policlinico San Donato, Milan, Italy;1. Sport Sciences – Performance and Technology, Department of Health Science and Technology, Faculty of Medicine, Aalborg University, Aalborg, Denmark;2. Department of Occupational and Environmental Medicine, Danish Ramazzini Centre, Aalborg University Hospital, Aalborg, Denmark
Abstract:We define a version of the Inverse Linear Programming problem that we call Linear Programming System Identification. This version of the problem seeks to identify both the objective function coefficient vector and the constraint matrix of a linear programming problem that best fits a set of observed vector pairs. One vector is that of actual decisions that we call outputs. These are regarded as approximations of optimal decision vectors. The other vector consists of the inputs or resources actually used to produce the corresponding outputs. We propose an algorithm for approximating the maximum likelihood solution. The major limitation of the method is the computation of exact volumes of convex polytopes. A numerical illustration is given for simulated data.
Keywords:
本文献已被 ScienceDirect 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号