Topic
Goal programming
About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.
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TL;DR: It is in the fourth part that the author describes linear programming in multiple objective systems (i.e. linear goal programming), covering formulation, methods of solution, duality and sensitivity analysis.
Abstract: (1982). Linear Programming in Single and Multiple Objective Systems. Journal of the Operational Research Society: Vol. 33, No. 6, pp. 591-591.
109 citations
TL;DR: A survey of the literature on Goal Programming from 1970 through 1982 is provided, categorized according to 18 areas of application and according to 12 different variants of GP.
109 citations
TL;DR: A sound yet simple priority method for fuzzy AHP is proposed which utilizes a linear goal programming (LGP) model to derive normalized fuzzy weights for fuzzy pairwise comparison matrices.
109 citations
TL;DR: A multi-item inventory model with two-storage facilities is developed with advertisement, price and displayed inventory level-dependent demand in a fuzzy environment (purchase cost, investment amount and storehouse capacity are imprecise).
109 citations
01 Jan 1989
TL;DR: The SUMT algorithm as discussed by the authors transforms one of more objective functions into reduced objective functions, which are analogous to goal constraints used in the goal programming method, and an envelope of the entire function set is computed using the Kreisselmeir-Steinhauser function.
Abstract: A technique is described for converting a constrained optimization problem into an unconstrained problem. The technique transforms one of more objective functions into reduced objective functions, which are analogous to goal constraints used in the goal programming method. These reduced objective functions are appended to the set of constraints and an envelope of the entire function set is computed using the Kreisselmeir-Steinhauser function. This envelope function is then searched for an unconstrained minimum. The technique may be categorized as a SUMT algorithm. Advantages of this approach are the use of unconstrained optimization methods to find a constrained minimum without the draw down factor typical of penalty function methods, and that the technique may be started from the feasible or infeasible design space. In multiobjective applications, the approach has the advantage of locating a compromise minimum design without the need to optimize for each individual objective function separately.
109 citations