An Interactive Approach for Multi-Criterion Optimization, with an Application to the Operation of an Academic Department

Geoffrion, Arthur M.; Dyer, James S.; Feinberg, A.

doi:10.1287/mnsc.19.4.357

Cited by 792 publications

(219 citation statements)

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“…Stewart, 1987;Jacquet-Lagrèze, Meziani and Slowinski, 1987;Roy et al, 2008), the IMOP literature has also considered other value function specifications. Geoffrion, Dyer, Feinberg (1972) and Zionts and Wallenius (1983) consider the case of a concave value function. When a quasi-concave value function is assumed, a popular idea in IMOP is to use "convex cones" to eliminate entire dominated regions in multiattribute space.…”

Section: Literature Reviewmentioning

confidence: 99%

CUT: A Multicriteria Approach for Concavifiable Preferences

Argyris

Morton

Figueira

2014

Operations Research

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We consider the problem of helping a decision maker (DM) choose from a set of multiattributed objects when her preferences are "concavifiable", i.e. representable by a concave value function. We establish conditions under which preferences or preference intensities are concavifiable. We also derive a characterization for the family of concave value functions compatible with a set of such preference statements expressed by the DM. This can be used to validate dominance relations over discrete sets of alternatives and forms the basis of an interactive procedure. We report on the practical use of this procedure with several DMs for a flat-choice problem and its computational performance on a set of project-portfolio selection problem instances. The use of preference intensities is found to provide significant improvements to the performance of the procedure.

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Section: Literature Reviewmentioning

confidence: 99%

CUT: A Multicriteria Approach for Concavifiable Preferences

Argyris

Morton

Figueira

2014

Operations Research

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“…A posteriori approach provides a large number of non-dominated solutions, from which the choice has to be made. An interactive approach presents alternate pareto-optimal solutions based on the feedback given by the decision maker [12].…”

Section: Multi Objective Optimizationmentioning

confidence: 99%

Load Allocation in Academic Environment: A Multi Objective PSO Approach

Rout¹,

Misra²,

Samanta³

2014

GSTF J Comput

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Abstract:In an Organization, mapping the competency of personnel with different level of expertise, skill set, and experience in professional fields is a tough, complex but essential task. In this work, we have considered an Engineering College with moderate number of faculties with different level of experience, expertise and research exposure. Here we have considered the load assignment to the faculties at the beginning of a semester as the competency mapping task. Each faculty having capabilities of teaching different subjects out of the total set of papers needs to take about two theory papers with or without laboratory component. The decisive factors for subject assignment may be depth of knowledge, sincerity, class management, contribution towards research, text book publication. Further preference of the faculty member should be considered with top priority unless there are some valid constraints. Again the teaching personnel in a department hold different designations and different administrative responsibility, therefore each of them cannot be assigned equal hours of teaching load. The All India Council for Technical Education (AICTE) guidelines is considered as a baseline for assignment of teaching load. The decisive factors are considered as objectives to be optimized and multi-objective particle Swarm optimization (MOPSO) is employed to perform the competency mapping task. The simulation results show the effectiveness of this approach.

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“…This is done, for example, in goal programming (see earlier references), in various approaches to interactive programming (Sas ka , 1968;Benayoun and Tergny , 1969;Benayoun et al, 1971;Geoffrion, 1970;Geoffrion et a].. , 1972;Boyd , 1970;Dyer , 1972Dyer , , 1973Zionts and Wallenius, 1976), in vector maximi zation's search for undominated alternatives (DaCun ha and Polak, 1967;Geoffrion, 1968;Philip , 1972;Benson and Morin, 1977), and in multiobjective linear programming (Zeleny, 1974;Yu andZe1~ny, 1975, 1976), domination structure analysis (Yu, 1974;Bergstresser et al, 1976) and Zeleny's ( 1976b) parametric goal programming approach. Additional discussions of several of these topics are provided by Roy (1971) and Hirsch (1976).…”

Section: Criterion Functionsmentioning

confidence: 99%