A weighted GP is a form of GP that uses penalty weights to find the optimal solution that minimizes the sum of the weights.
Published in Chapter:
Goal Programming and Its Variants
John Wang (Montclair State University, USA), Dajin Wang (Montclair State University, USA), and Aihua Li (Montclair State University, USA)
Copyright: © 2008
|Pages: 8
DOI: 10.4018/978-1-59904-843-7.ch047
Abstract
Within the realm of multicriteria decision making (MCDM) exists a powerful method for solving problems with multiple objectives. Goal programming (GP) was the first multiple-objective technique presented in the literature (Dowlatshahi, 2001). The premise of GP traces its origin back to a linear programming study on executive compensation in 1955 by Charnes, Cooper, and Ferguson even though the specific name did not appear in publications until the 1961 textbook entitled Management Models and Industrial Applications of Linear Programming, also by Charnes and Cooper (Schniederjans, 1995). Initial applications of this new type of modeling technique demonstrated its potential for a variety of applications in numerous different areas. Until the middle of the 1970s, GP applications reported in the literature were few and far between. Since that time, primarily due to influential works by Lee and Ignizio, a noticeable increase of published GP applications and technical improvements has been recognized. The number of case studies, along with the range of fields, to which GP has been and still is being applied is impressive, as shown in surveys by Romero (1991) and Aouni and Kettani (2001). It can be said that GP has been, and still is, the “most widely used multi-criteria decision making technique” (Tamiz, Jones, & Romero, 1998, p. 570).