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By Marc Schniederjans

Goal Programming functions in Accounting seventy four aim Programming purposes in Agriculture seventy six target Programming functions in Economics seventy eight aim Programming functions in Engineering seventy nine objective Programming purposes in Finance eighty objective Programming purposes in executive eighty three target Programming functions in a world Context 88 target Programming purposes in administration ninety objective Programming purposes in advertising and marketing ninety seven precis ninety eight bankruptcy five. destiny tendencies IN target PROORAMMING one hundred and one GP is located for development one zero one moving the lifestyles Cycle of GP examine to development 103 precis 107 Reference 108 APPENDIX A TEXTBOOKS, READINGS BOOKS AND MONOORAPHS ON aim PROORAMMING 109 APPENDIX B. magazine learn courses ON target PROORAMMING 113 INDEX 213 viii record OF FIGURES determine 1-1. precis courting of GP with MS/OR and MCDM determine 1-2. Frequency Distribution for GP magazine guides determine 1-3. existence Cycle ofGP study determine 2-1. Set of GP effective recommendations determine 5-1. lifestyles Cycle of GP examine ix record OF TABLES desk 1-1. MS/OR issues and Their comparable GP subject matters desk 1-2. MCDM Subareas and Their comparable GP issues desk 1-3. Frequency directory ofGP magazine courses and booklet Titles desk 2-1. options for a ruled GP challenge desk 2-2. Conversion ofLP Constraints to objective Constraints desk 2-3. GP Citations on Dominance, Inferiority and Inefficiency desk 2-4. GP Citations on Relative Weighting, Prioritization and Incommensurability desk 2-5. MS/OR issues and Their comparable GP issues desk 3-1. Citations on WeightedlPreemptive GP method desk 3-2. Citations on Pure/Mixed Integer GP technique desk 3-3.

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Goal Programming Model Formulation Strategies 33 Inappropriateness ofpredetermined goals or targets (Min and Storbeck 1991): Setting a priori right-band-side bi values (the model goals or targets) in GP models is viewed by some as being too arbitrary. It is felt that this arbitrary setting may lead to problems like dominance or just limits the information that the GP model can provide (Zeleny 1981). To overcome objections it might be advisable to included a sensitivity analysis of bi Steuer (1976, 1979) that eliminates the uncertainty in the parameter.

35-43). A review of the references in Table 2-4 is also recommended. subject to: + d l -= 100 d2 + + d2- = 100 d3+ + d3- = 100 Xl +X2-dl+ XI - X2 - d/,dj -, x/:? 0, for i=l, ... ,3; forj=l, ... 8) Analytic hierarchy process for determining goal weights and priorities: The analytic hierarchy process (AHP) is a statistical methodology (Saaty 1980). AHP can be used to derive relative weighting or priorities based on the weighting for GP models. In modeling situations where numerous qualitative factors are to be considered in establishing relative weighting, AHP may bring a recognized and uniformly fair framework for the assessment of those weights.

Citations on Nonlinear GP Methodology Reference Notes and Comments on What Reference Provides Armstrong, Chames, and Haksever (1988), Weistoffer (1983) methodology combined with interactive procedure Awerbuch, Ecker and Wallace (1976), Chames, Cooper, Klingman and Niehaus (1975) a general discussion and comments Clayton, Weber, and Taylor (1982) Davis and Whitford (1985) methodology combined with simulation methods to generate a more greedy or efficient solution comments on decomposition method Goal Programming Solution Methodology 51 Table 3-4.

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