zbMATH — the first resource for mathematics

The interface with decision makers and some experimental results in interactive multiple objective programming methods. (English) Zbl 0589.90049
Summary: We discuss the basic concepts of interactive methods in multiple objective linear programming. The underlying mathematical formulation is investigated. It turns out that these methods are all based on three different types of scalarization. This explains the exchange of information - the interface - between the decision makers and the model. Problems in designing this interface will be investigated using results from psychology concerning the ability of a human being to oversee a large number of stimuli. We present the results of some practical experiments with a model of the energy system in the Netherlands, and finally we draw some conclusions concerning the possible use of interactive methods in long-term planning.

90B50 Management decision making, including multiple objectives
90C31 Sensitivity, stability, parametric optimization
90C90 Applications of mathematical programming
Full Text: DOI
[1] Belton, V., A comparison of the analytic hierarchy process and a simple multi-attribute value function, European journal of operational research, (1986), (this issue)
[2] Boonekamp, P.G.M., \scselpe: A model of the Dutch energy system, Energy study centre, ESC-17, (1982), Petten (in Dutch)
[3] Chankong, V.; Haimes, Y.Y., Multiobjective decision making: theory and methodology, (1983), North-Holland New York · Zbl 0525.90085
[4] Cohon, J.L., Multi-objective programming and planning, (1978), Academic Press New York
[5] Despontin, M.; Moscarola, J.; Spronk, J., A user-oriented listing of multiple criteria decision methods, The Belgian journal of statistics, computer science and operations research, 23, 4, 3-110, (1983)
[6] Despontin, M.; Nijkamp, P.; Spronk, J., Conflict analysis in macro-economic planning models, (), 1-19
[7] Dijk, D.; Kok, M., Strategic planning and energy models, ()
[8] Grauer, M.; Lewandowski, A.; Schrattenholzer, L., Use of the reference level approach for the generation of efficient energy supply strategies, (), 425-444
[9] Hwang, C.L.; Masud, A.S., Multiple objective decision making, methods and applications. A state-of-the-art survey, ()
[10] Hwang, C.L.; Yoon, K., Multiple attribute decision making, methods and applications. A state-of-the-art survey, () · Zbl 0453.90002
[11] Isermann, H.; Steuer, R.E., Pay-off tables and minimum criterion values over the efficient set, ()
[12] Kallio, M.; Lewandowski, A.; Orchard-Hays, W., An implementation of the reference-point approach for multiobjective optimization, (), 1025-1054 · Zbl 0539.90095
[13] Kok, M., Trade-off information in interactive multi-objective linear programming methods, ()
[14] Kok, M., Multiple objective energy modelling: experimental results with interactive methods in The Netherlands, ()
[15] Kok, M.; Lootsma, F.A., Pairwise-comparison methods in multiple objective programming, with applications in a long-term energy-planning model, European journal of operational research, 22, 1, 44-55, (1985) · Zbl 0578.90040
[16] Kok, M.; Spronk, J., A note on the pay-off matrix in multiple objective programming, Omega, 13, 580-583, (1985)
[17] Légrády, K.; Lootsma, F.A.; Meisner, J.; Schellemans, F., Multi-criteria decision analysis to aid budget allocation, (), 164-174
[18] Lootsma, F.A., Saaty’s priority theory and the nomination of a senior Professor in operations research, European journal of operational research, 4, 380-388, (1980)
[19] Nakayama, H.; Sawaragi, Y., Satisficing trade-off method for multi-objective programming and its applications, ()
[20] Payne, J.W., Task complexity and contingent processing in decision making: an information search and protocol analysis, Organizational behavior and human performance, 16, 366-387, (1976)
[21] Rietveld, P., Multiple objective decision methods and regional planning, () · Zbl 0696.90001
[22] Roy, B., Meaning and validity of interactive procedures as tools for decision aid, ()
[23] Roy, B.; Vincke, Ph., Multicriteria analysis: survey and new directions, European journal of operational research, 8, 207-218, (1981) · Zbl 0464.90068
[24] Sawaragi, Y.; Nakayama, H.; Tanino, T., Theory of multiobjective optimization, (1985), Academic Press New York · Zbl 0566.90053
[25] ()
[26] Slovic, P.; Lichtenstein, S., Comparison of Bayesian and regression approaches to the study of information processing in judgement, Organizational behavior and human performance, 11, 649-744, (1971)
[27] Spronk, J., Interactive multiple goal programming: applications to financial planning, (1981), Nijhof Boston
[28] Spronk, J.; Veeneklaas, F., A feasibility study of economic and environmental scenarios by means of interactive multiple goal programming, Regional science and urban economics, 13, 141-160, (1983)
[29] Steuer, R.E.; Choo, E.U., An interactive weighted Tchebycheff procedure for multiple objective programming, Mathematical programming, 26, 326-344, (1983) · Zbl 0506.90075
[30] White, D.J., Optimality and efficiency, (1982), Wiley Chichester · Zbl 0433.90001
[31] Yu, P.L., Multiple criteria decision making: concepts, techniques and extensions, (1985), Plenum New York
[32] Zeleny, M., Multiple criteria decision making, (1982), McGraw-Hill New York · Zbl 0588.90019
[33] Zionts, S.; Deshpande, D., Energy planning using a multiple criteria decision method, (), 153-162
[34] Zionts, S.; Wallenius, J., An interactive multiple objective linear programming method for a class of underlying nonlinear functions, Management science, 29, 519-529, (1983) · Zbl 0519.90083
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.