3959 Words Dec 18th, 2012 16 Pages

Chapter 1 LITERATURE SURVEY……………………………………………………. | 1 | | | 1.1 INTRODUCTION............................................................................................... | 1 | 1.2 PHILOSOPHICAL BASIS.................................................................................. | 2 | 1.3 HISTORICAL SKETCH..................................................................................... | 3 | 1.4 VARIANTS........................................................................................................ | 4 | 1.5 APPLICATIONS…………………………………………………………………….. | 5 | 1.6 STRENGTHS AND WEAKNESSES……………………………………………… | 6 | 1.7 THE FUTURE……………………………………………………………………….. | 7 | | | Chapter 2
In short, the problems that one encounters outside the classroom are invariably massive, messy, changeable, complex, and resist treatment via conventional approaches, Yet the vast majority of traditional approaches to such problems utilize conventional models and methods that idealistically and unrealistically(in most cases) presume the optimization of a single objective subject to a set of rigid constraints. Goal Programming was introduced in an attempt to eliminate or, at the least, mitigate this disquieting disconnect. Conceived and developed by Abraham Charnes and William Cooper, goal programming was originally dubbed “constrained regression”. Constrained regression, in turn, was and is a powerful nonparametric method for the development of regression functions (e.g. curve fitting) subject to the side constraints.
Charnes and Cooper first applied constrained regression in the 1950’s to the analysis of executive compensation. Recognizing that the method could be extended to a more general class of problems – that is, any quantifiable problem having multiple objectives and soft, as well as rigid constraints – Charnes and Cooper later renamed the method Goal Programming.

1.2. PHILOSOPHICAL BASIS

The two philosophical concepts that serve to best distinguish Goal Programming from conventional (i.e. single objective) methods of optimization

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