Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning

ABSTRACT Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade‐offs involve, while the...

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Veröffentlicht in:	Decision sciences Jg. 23; H. 1; S. 1 - 20
1. Verfasser:	Olson, David L.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford, UK Blackwell Publishing Ltd 01.01.1992 American Institute for Decision Sciences
Schlagworte:	Decision Analysis Decision makers Decision making Decision theory Goal programming Human Information Processing Learning Linear Programming Mathematical programming Objectives Research subjects
ISSN:	0011-7315, 1540-5915
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Abstract	ABSTRACT Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade‐offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision‐making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade‐offs of value. Support of nonlinear utility and enhancing decision‐maker learning are considered.
AbstractList	Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be reconciled. A variety of multiobjective programming techniques have been presented in the multicriteria decision-making literature. Published studies with human subjects in which some of these techniques were applied are reviewed. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade-offs of value. Support of nonlinear utility and enhancement of decision-maker learning are considered. ABSTRACT Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade‐offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision‐making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade‐offs of value. Support of nonlinear utility and enhancing decision‐maker learning are considered. Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must be reconciled. Interactive multiobjective programming provides a means for decision makers to learn what these trade‐offs involve, while the mathematical program generates solutions that seek improvement of the implied utility of the decision maker. A variety of multiobjective programming techniques have been presented in the multicriteria decision‐making literature. This study reviews published studies with human subjects where some of these techniques were applied. While all of the techniques have the ability to support decision makers under conditions of multiple objectives, a number of features in applying these systems have been tested by these studies. A general evolution of techniques is traced, starting with methods relying upon linear combinations of value, to more recent methods capable of reflecting nonlinear trade‐offs of value. Support of nonlinear utility and enhancing decision‐maker learning are considered.
Author	Olson, David L.
Author_xml	– sequence: 1 givenname: David L. surname: Olson fullname: Olson, David L. organization: Department of Business Analysis and Research, Texas A&M University, College Station, TX 77843-4217
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Cites_doi	10.1007/978-94-009-8165-2 10.1016/0022-2496(77)90033-5 10.1016/0305-0548(87)90002-5 10.1016/0377-2217(82)90133-3 10.1287/mnsc.21.12.1387 10.1111/j.1540-5915.1985.tb01479.x 10.1287/mnsc.22.6.652 10.1002/1520-6750(198906)36:3<321::AID-NAV3220360309>3.0.CO;2-F 10.1111/j.1540-5915.1988.tb00309.x 10.1016/0377-2217(85)90224-3 10.1007/BF02591870 10.1287/mnsc.30.11.1268 10.1029/WR010i004p00615 10.1016/0377-2217(86)90162-1 10.1287/mnsc.19.4.357 10.1016/0005-1098(78)90046-8 10.1016/0895-7177(89)90363-4 10.1111/j.1540-5915.1990.tb00322.x 10.1007/978-3-642-46536-9_30 10.1287/mnsc.23.3.305 10.1016/0377-2217(87)90248-7 10.1007/BF01584098 10.1111/j.1540-5915.1981.tb00081.x
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Notes	ark:/67375/WNG-6VLXP7M2-Q ArticleID:DECI1 istex:6F90184FF6DD3AD259E01FF356636B5ACEAC570F His research interests are in the area of multiple objective decision making. David L. Olson is an associate professor in the Department of Business Analysis and Research, Texas A&M University. He has an M.B.A. from Kearney State College and a Ph.D. in business administration from the University of Nebraska‐Lincoln. Dr. Olson has published in a number of journals including the and the Academy of Management Journal European Journal of Operations Research, Journal of the Operational Research Society ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
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References	Geoffrion, A. M., Dyer, J. S., & Feinberg, A. An interactive approach for multicriterion optimization with an application to the operation of an academic department. Management Science, 1971, 19, 357-368. Nakayama, H., & Furukawa, K. Satisficing trade-off method with an application to multiobjective structural design. Large Scale Systems, 1985, 8, 47-57. Michalowski, W. An experiment with Zionts-Wallenius and Steuer interactive programming methods. In Y. Y. Haimes & V. Chankong (Eds.), Decision making with multiple objectives-Proceedings VI. New York : Springer-Verlag, 1985. Brockhoff, K. Experimental test of MCDM algorithms in a modular approach. European Journal of Operational Research, 1985, 22, 159-166. Franz, L., Lee, S. M., & van Horn, I. C. An adaptive decision support system for academic resource planning. Decision Sciences, 1981, 12, 276-293. Haimes, Y. Y. & Hall, W. Multiobjectives in water resources systems analysis: The surrogate worth trade-off method. Water Resources Research, 1974, 10, 615. Ramesh, R., Karwan, M. H., & Zionts, S. Interactive multicriteria linear programming: An extension of the method of Zionts and Wallenius. Naval Research Logistics Quarterly, 1989, 36(3), 321-335. Spronk, J. Interactive multiple goal programming: Applications to financial planning. Boston : Nijhof, 1981. Saaty, T. L. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 1977, 15, 234-281. Wallenius, J. Comparative evaluation of some interactive approaches to multicriterion optimization. Management Science, 1975, 21, 1387-1396. Buchanan, J. T., & Daellenbach, H. G. A comparative evaluation of interactive solution methods for multiple objective decision models. European Journal of Operational Research, 1987, 29, 353-359. Gardner, J. C., Huefner, R. J., & Lotfi, V. A multiperiod audit staff planning model using multiple objectives: Development and evaluation. Decision Sciences, 1990, 21, 154-170. Michalowski, W. Evaluation of a multiple criteria interactive programming approach: An experiment. INFOR, 1987, 25, 165-172. Gibson, M., Bernardo, J. J., Chung, C., & Badinelli, R. A comparison of interactive multiple-objective decision making procedures. Computers and Operations Research, 1987, 14, 97-105. Steuer, R. E., & Choo, E.-U. An interactive weighted Tchebycheff procedure for multiple objective programming. Mathematical Programming, 1983, 26, 326-344. Wallenius, J. Interactive multiple criteria decision methods: An investigation and approach. The Helsinki School of Economics, 1975. Narasimhan, R., & Vickery, S. K. An experimental evaluation of articulation of preferences in multiple criterion decision-making (MCDM) methods. Decision Sciences, 1988, 19, 880-888. Kok, M. The interface with decision makers and some experimental results in interactive multiple objective programming methods. European Journal of Operational Research, 1986, 26, 96-107. de Samblanckx, S., Depraetere, P., & Muller, H. Critical considerations concerning the multicriteria analysis by the method of Zionts and Wallenius. European Journal of Operational Research, 1982, 10, 70-76. Vanderpooten, D. The interactive approach in MCDA: A technical framework and some basic conceptions. Mathematical and Computer Modelling, 1990, 12, 1213-1220. Benayoun, R., de Montgolfier, J., Tergny, J., & Larichev, D. Linear programming with multiple objective functions: Step method (STEM). Mathematical Programming, 1971, 1, 366-375. Evans, G. W. An overview of techniques for solving multiobjective mathematical programs. Management Science, 1984, 30(11), 1268-1282. Rosenthal, R. E. Principles of multiobjective optimization. Decision Sciences, 1985, 16(2), 133-152. Steuer, R. E. Multiple objective linear programming with interval criterion. Management Science, 1976, 23, 305-316. Balachandran, K. R., & Steuer, R. E. An interactive model for the CPA firm audit staff planning problem with multiple objectives. The Accounting Review, 1982, LVII, 125-140. Zeleny, M. Stable patterns from decision-producing networks: New interfaces of DSS and MCDM. MCDM WorldScan, 1989, 3(2&3), 6-7. Rustem, B., Velupillai, K., & Wescott, J. Respecifying the weighting matrix of a quadratic objective function. Automatica, 1976, 14, 567-582. Steuer, R. E. Operating manual for the ADBASE multiple objective linear programming computer package (Release: 8/80). College of Business Administration, University of Georgia, 1982. Zionts, S., & Wallenius, J. An interactive programming method for solving the multiple criteria problem. Management Science, 1976, 22, 652-663. 1989; 3 1987; 14 1990; 12 1974; 10 1976; 23 1976; 22 1982; LVII 1988; 19 1985; 8 1982; 10 1975 1973 1985; 22 1978 1987; 25 1984; 30 1990; 21 1976; 14 1977; 15 1971; 19 1986; 26 1985 1983 1982 1981 1975; 21 1971; 1 1983; 26 1989; 36 1987; 29 1985; 16 1981; 12 e_1_2_1_22_2 Steuer R. E. (e_1_2_1_26_2) 1982 Dyer J. S. (e_1_2_1_8_2) 1973 e_1_2_1_23_2 e_1_2_1_20_2 e_1_2_1_21_2 e_1_2_1_27_2 e_1_2_1_24_2 e_1_2_1_25_2 Deissenberg C. (e_1_2_1_6_2) 1983 e_1_2_1_29_2 Wallenius J. (e_1_2_1_31_2) 1975 e_1_2_1_30_2 e_1_2_1_7_2 Michalowski W. (e_1_2_1_17_2) 1987; 25 e_1_2_1_4_2 e_1_2_1_5_2 e_1_2_1_11_2 e_1_2_1_3_2 e_1_2_1_12_2 Steuer R. E. (e_1_2_1_28_2) 1978 e_1_2_1_33_2 e_1_2_1_10_2 Balachandran K. R. (e_1_2_1_2_2) 1982 e_1_2_1_15_2 e_1_2_1_16_2 e_1_2_1_13_2 Zeleny M. (e_1_2_1_32_2) 1989; 3 e_1_2_1_14_2 Nakayama H. (e_1_2_1_18_2) 1985; 8 e_1_2_1_19_2 e_1_2_1_9_2
References_xml	– reference: Kok, M. The interface with decision makers and some experimental results in interactive multiple objective programming methods. European Journal of Operational Research, 1986, 26, 96-107. – reference: Steuer, R. E. Operating manual for the ADBASE multiple objective linear programming computer package (Release: 8/80). College of Business Administration, University of Georgia, 1982. – reference: Brockhoff, K. Experimental test of MCDM algorithms in a modular approach. European Journal of Operational Research, 1985, 22, 159-166. – reference: Narasimhan, R., & Vickery, S. K. An experimental evaluation of articulation of preferences in multiple criterion decision-making (MCDM) methods. Decision Sciences, 1988, 19, 880-888. – reference: Gardner, J. C., Huefner, R. J., & Lotfi, V. A multiperiod audit staff planning model using multiple objectives: Development and evaluation. Decision Sciences, 1990, 21, 154-170. – reference: Rosenthal, R. E. Principles of multiobjective optimization. Decision Sciences, 1985, 16(2), 133-152. – reference: Michalowski, W. Evaluation of a multiple criteria interactive programming approach: An experiment. INFOR, 1987, 25, 165-172. – reference: Ramesh, R., Karwan, M. H., & Zionts, S. Interactive multicriteria linear programming: An extension of the method of Zionts and Wallenius. Naval Research Logistics Quarterly, 1989, 36(3), 321-335. – reference: Balachandran, K. R., & Steuer, R. E. An interactive model for the CPA firm audit staff planning problem with multiple objectives. The Accounting Review, 1982, LVII, 125-140. – reference: Zionts, S., & Wallenius, J. An interactive programming method for solving the multiple criteria problem. Management Science, 1976, 22, 652-663. – reference: Vanderpooten, D. The interactive approach in MCDA: A technical framework and some basic conceptions. Mathematical and Computer Modelling, 1990, 12, 1213-1220. – reference: Michalowski, W. An experiment with Zionts-Wallenius and Steuer interactive programming methods. In Y. Y. Haimes & V. Chankong (Eds.), Decision making with multiple objectives-Proceedings VI. New York : Springer-Verlag, 1985. – reference: Nakayama, H., & Furukawa, K. Satisficing trade-off method with an application to multiobjective structural design. Large Scale Systems, 1985, 8, 47-57. – reference: Franz, L., Lee, S. M., & van Horn, I. C. An adaptive decision support system for academic resource planning. Decision Sciences, 1981, 12, 276-293. – reference: Saaty, T. L. A scaling method for priorities in hierarchical structures. Journal of Mathematical Psychology, 1977, 15, 234-281. – reference: Wallenius, J. Comparative evaluation of some interactive approaches to multicriterion optimization. Management Science, 1975, 21, 1387-1396. – reference: Zeleny, M. Stable patterns from decision-producing networks: New interfaces of DSS and MCDM. MCDM WorldScan, 1989, 3(2&3), 6-7. – reference: Benayoun, R., de Montgolfier, J., Tergny, J., & Larichev, D. Linear programming with multiple objective functions: Step method (STEM). Mathematical Programming, 1971, 1, 366-375. – reference: Evans, G. W. An overview of techniques for solving multiobjective mathematical programs. Management Science, 1984, 30(11), 1268-1282. – reference: Steuer, R. E., & Choo, E.-U. An interactive weighted Tchebycheff procedure for multiple objective programming. Mathematical Programming, 1983, 26, 326-344. – reference: Steuer, R. E. Multiple objective linear programming with interval criterion. Management Science, 1976, 23, 305-316. – reference: Buchanan, J. T., & Daellenbach, H. G. A comparative evaluation of interactive solution methods for multiple objective decision models. European Journal of Operational Research, 1987, 29, 353-359. – reference: de Samblanckx, S., Depraetere, P., & Muller, H. Critical considerations concerning the multicriteria analysis by the method of Zionts and Wallenius. European Journal of Operational Research, 1982, 10, 70-76. – reference: Geoffrion, A. M., Dyer, J. S., & Feinberg, A. An interactive approach for multicriterion optimization with an application to the operation of an academic department. Management Science, 1971, 19, 357-368. – reference: Gibson, M., Bernardo, J. J., Chung, C., & Badinelli, R. A comparison of interactive multiple-objective decision making procedures. Computers and Operations Research, 1987, 14, 97-105. – reference: Spronk, J. Interactive multiple goal programming: Applications to financial planning. Boston : Nijhof, 1981. – reference: Rustem, B., Velupillai, K., & Wescott, J. Respecifying the weighting matrix of a quadratic objective function. Automatica, 1976, 14, 567-582. – reference: Wallenius, J. Interactive multiple criteria decision methods: An investigation and approach. The Helsinki School of Economics, 1975. – reference: Haimes, Y. Y. & Hall, W. Multiobjectives in water resources systems analysis: The surrogate worth trade-off method. Water Resources Research, 1974, 10, 615. – year: 1985 – volume: 14 start-page: 97 year: 1987 end-page: 105 article-title: A comparison of interactive multiple‐objective decision making procedures publication-title: Computers and Operations Research – year: 1983 – year: 1981 – volume: 29 start-page: 353 year: 1987 end-page: 359 article-title: A comparative evaluation of interactive solution methods for multiple objective decision models publication-title: European Journal of Operational Research – volume: 19 start-page: 357 year: 1971 end-page: 368 article-title: An interactive approach for multicriterion optimization with an application to the operation of an academic department publication-title: Management Science – volume: 1 start-page: 366 year: 1971 end-page: 375 article-title: Linear programming with multiple objective functions: Step method (STEM) publication-title: Mathematical Programming – volume: 23 start-page: 305 year: 1976 end-page: 316 article-title: Multiple objective 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Snippet	ABSTRACT Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must... Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade‐offs among conflicting objectives must be... Multiple objective programming provides a means of aiding decision makers facing complex decisions where trade-offs among conflicting objectives must be...
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SubjectTerms	Decision Analysis Decision makers Decision making Decision theory Goal programming Human Information Processing Learning Linear Programming Mathematical programming Objectives Research subjects
Title	Review of Empirical Studies in Multiobjective Mathematical Programming: Subject Reflection of Nonlinear Utility and Learning
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