The setting of weights in linear goal-programming problems
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Cited by (51)
A fuzzy multi-objective model for solving project network problem with bonus and incremental penalty cost
2015, Computers and Industrial EngineeringCitation Excerpt :The authors have stressed that the preemptive priority approach presents computational superiority over existing methods (Chen & Tsai, 2001). However, Akoz and Petrovic (2007) have emphasized the possibility of the results giving high achievements value for only the higher priority level goals while Gass (1987) has highlighted the difficulty in determining definite objective hierarchy. Hannan (1985) has also indicated that the approach is time consuming and determining the weights is hard.
Solving multi-period project selection problems with fuzzy goal programming based on TOPSIS and a fuzzy preference relation
2013, Information SciencesCitation Excerpt :The key idea behind GP is to minimize the unwanted deviations from the goals set by the DMs [60]. Further development of the original GP model are proposed by Cooper [17], Lee [47], Ignizio [37,38], Hannan [27], Gass [23], Min and Storbeck [51], Jones and Tamiz [40], Romero [56], Romero [57], Liao and Ho [48], and Chang [12]. GP models can be classified into three major subsets (i.e., non-preemptive, lexicographic, and Chebyshev) based on the achievement function used for combining the unwanted deviations [60,57].
A fuzzy goal programming approach for mid-term assortment planning in supermarkets
2011, European Journal of Operational ResearchCitation Excerpt :In fact, the deviations from the zero target level of space utilization goal may only be positive values. Gass (1987) argues that a hard pre-emptive multi-objective model may be unrealistic because this assumes infinite trade-offs between different levels, and also the sequential solution technique may cut-off some interesting parts of the solution space. So, we apply the flexible pre-emptive goal hierarchy proposed by Akoz and Petrovic (2007) because it takes simultaneously the achievement degrees of fuzzy goals and constraints, as well as the satisfaction degrees of fuzzy goals priorities into consideration through modeling fuzzy binary relations between each pair of goals belonging to different priority levels.
A shift scheduling model for employees with different seniority levels and an application in healthcare
2009, European Journal of Operational ResearchA fuzzy goal programming method with imprecise goal hierarchy
2007, European Journal of Operational ResearchPlanning relocation of people for developing surface mines in densely populated areas: Optimization of multiple objectives
2011, Asia-Pacific Journal of Operational Research
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Saul I. Gass received his U.S. in Education and M.A. in Mathematics from Boston University, and his Ph.D. in Engineering Science/Operations Research from the University of California, Berkeley. He is currently Professor in Management Science and Statistics, College of Business and Management, University of Maryland, College Park. Dr Gass began his career in operations research with the USAF Directorate of Management Analysis, and was Manager of Federal Civil Programs for IBM's Federal Systems Division, Senior Vice-President of World Systems Laboratories, and Vice-President of Mathematica, Inc. He is a past President of the Operations Research Society of America, and is currently President of Omega Rho, the international honor society in operations research. His books include Linear Programming (McGraw-Hill, fifth edition) and Decision Making, Models and Algorithms (Wiley-Interscience). Dr Gass has jogged over 10, 000 miles in the major cities of the world, including Potomac, Maryland.