The model is structured from two sub-models, where the first based on linear programming and the second based on weighted goal programming are supported with penalty functions the authors dispatch the weaknesses of lp with the application of wgp in the model and support this statement with results in this paper. Of goals in linear and integer programming methods the objective function is measured in one dimension only but in goal programming, conflicting goals or goals with different priorities and weights can be combined with each other and solved by using simplex algorithm and programs such as qm solver or excel. Goal programming is a branch of multiobjective optimization, which in turn is a branch of multi-criteria decision analysis (mcda) this is an optimization programme it can be thought of as an extension or generalisation of linear programming to handle multiple, normally conflicting objective measures each of these measures is given a goal or target value to be achieved. Goal programming problems can be categorized according to the type of mathemat- ical programming model (linear programming, integer programming, nonlinear program- ming, etc) that it fits except for having multiple goals instead of a single objective.
Goal programming belongs to multi criteria linear programming methods, but while these methods deal with the maximization or minimization of various objective functions, goal programming is focused on achieving predetermined goals. A key element of a goal programming model is the achievement function that is, the function that measures the degree of minimisation of the unwanted deviation variables of the goals considered in the model other common applications of lp linear programming is a powerful tool for selecting alternatives in a decision problem and. This paper presents a review of the current literature on the branch of multi-criteria decision modelling known as goal programming (gp) the result of our indepth investigations of the two main gp methods, lexicographic and weighted gp together with their distinct application areas is reported. Linear programming is the analysis of problems in which a linear function of a number of variables is to be optimized (maximized or minimized) when whose variables are subject to a number of constraints in the mathematical near inequalities.
Specific techniques include linear, nonlinear, dynamic, integer, goal and stochastic programming, as well as various network-based methods a detailed exposition of these is beyond the scope of this chapter, but there are a number of excellent texts in mathematical programming that describe many of these methods and the interested reader should. Kwak and lee (1997) suggested the linear goal programming for human resource allocation in a health care organization also romero (1986) generalized the goal programming approach. Linear programming application transportation problem the navy has 9,000 pounds of material in albany, georgia that it wishes to ship to three installations: san diego, norfolk, and pensacola they require 4,000, 2,500, and 2,500 pounds, respectively government regulations require equal distribution of shipping among the three carriers. Goal programming (gp) models was originally introduced by charnes and cooper in early 1961 for a linear model this approach allows the simultaneous solution of a.
Goal programming now encompasses any linear, integer, zero-one, or nonlinear multi- objective problem, for which preemptive priorities may be established, the field of application is increasing rapidly. This paper presents an application of linear goal programming to the distribution decision faced by the marketing department of a food products manufacturer. Linear programming (lp, also called linear optimization) is a method to achieve the best outcome (such as maximum profit or lowest cost) in a mathematical model whose requirements are represented by linear relationships. Goal programming models are very similar to linear programming models but whereas linear programs have one objective goal programs can have several objectives consider the following example suppose that a company manufactures two products (x1 and x2.
Applications goal programming’s label as the ―workhorse‖ of multiple-objective optimization has been achieved by its successful solutions of important real-world problems over a period of more than 50 years. The use of linear goal program in the general literature of computer applications, goal programming appears more prominently for example, gross and talavage  develop a planning methodology that applies goal program linear goal programming for academic library acquistions allocations 315 26 24 18 16 14 12 10 6 4. Tained by solving two linear programming model with the goal of maximizing profits and application of llinear and nnon-linear programming model / seyed abolghasem mortazavi et al.
2-6 characteristics of linear programming problems a decision amongst alternative courses of action is required the decision is represented in the model by decision variables the problem encompasses a goal, expressed as an objective function, that the decision maker wants to achieve restrictions (represented by constraints) exist that limit. Linear programming brewer’s problem simplex algorithm implementation linear programming applications agriculture diet problem computer science compiler register allocation, data mining standard form linear program input: real numbers a ij, c j, b i. Introduction to linear programming,: with applications by smythe, william r and a great selection of similar used, new and collectible books available now at abebookscom. Application of goal programming model for budgeting in rivers state university of science and technology , port harcourt maxwell, awoingo a department of mathematics and statistics, rivers state college of arts and science, rumuola, port harcourt.