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Linear Goal Programs

Arthur, J. L. and A. Ravindran. 1980. PAGP An efficient algorithm for Linear Goal Programming problems. ACM IrowsflcfiowsowMafliemaficaZ Software. 6(3) 378-386. [Pg.358]

A.6 Partitioning Algorithm for Preemptive Goal Programs A.6.1 Linear Goal Programs... [Pg.502]

Deb, K. Non-linear Goal Programming Using Multi-Objective Genetic Algorithms, in Computational Intelligence, Universitat Dortmund (2004)... [Pg.341]

An Application of Non-Linear Goal Programming in Electrodischarge Machining of Composite Material... [Pg.166]

B. Satyanarayana, P.N. Rao and N.K. Tewari, "Application of Non-Linear Goal Programming Technique In Metal Cutting," Proceedings 12th AIMTDR Conference, IIT Delhi (1986) 483-486. [Pg.172]

Ravi, V. and Reddy, P.J. (1998) Fuzzy linear fractional goal programming applied to refinery operations planning. Fuzzy Sets and Systems, 96, 173. [Pg.138]

Goal programming techniques used in Sections 74.3.1 and 74.3.2 require completely prespecified preference from a decision maker. In addition, the NP-GP assumes that a decision maker s utility function is linear. In practice, defining preference numerically could be difficult. Another MCMP approach, called an interactive method, can be used to overcome this issue. An interactive method does not require prespecified preference but relies on the progressive articulation of preferences by a decision maker (Masud and Ravindran 2008). The steps for an interactive method are as follows ... [Pg.212]

Deporter, E.L., Ellis, K.P., 1990. Optimization of project networks with goal programming and fuzzy linear programming. Comput. Ind Eng. 19,500-504. [Pg.306]

Minimum-time joint trajectory is a constrained non-linear optimization problem with a single objective function. The optimization procedure used in this work is the non-linear optimization search method with goal programming based on the Modified Hooke and Jeeves Direct Search Method [13]. [Pg.503]

A process-synthesis problem can be formulated as a combination of tasks whose goal is the optimization of an economic objective function subject to constraints. Two types of mathematical techniques are the most used mixed-integer linear programming (MILP), and mixed-integer nonlinear programming (MINLP). [Pg.17]

LINEAR PROGRAMMING GRAPHICAL SOLUTION. Figure 11-9 is the graphical representation of this problem. Line OE represents the overall constraint placed on the problem by Eqs. (58), (59), and (60). The parallel dashed lines represent possible conditions of cost. The goal of the program is to minimize cost (that is, C) while still remaining within the constraints of the problem. The minimum value of C that still meets the constraints occurs for the line OD, and the optimum must be at point 0. Thus, the recommended blend is no water, 37.5 gal of A, 62.5 gal of B, and a total cost C of 27.63 for 100 gal of blend. [Pg.379]


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