A practical weight sensitivity algorithm for goal and multiple objective programming

► A weight sensitivity algorithm for goal and multi-objective programming is presented. ► Solutions from portion of weight space of interest to the decision maker are generated. ► The algorithm is demonstrated on two examples from the literature. This paper presents a weight sensitivity algorithm th...

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Bibliographic Details
Published in:European journal of operational research Vol. 213; no. 1; pp. 238 - 245
Main Author: Jones, Dylan
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 16.08.2011
Elsevier
Elsevier Sequoia S.A
Series:European Journal of Operational Research
Subjects:
Algorithms
Applied sciences
Decision making
Decision theory. Utility theory
Estimates
Exact sciences and technology
Goal programming
Methodology
Multi-objective programming
Multi-objective programming Goal programming Preferential weight choice Multiple criteria decision making
Multiple criteria decision making
Multiple objective
Operational research
Operational research and scientific management
Operational research. Management science
Preferential weight choice
Programming
Sensitivity analysis
Studies
Multiple criteria decision making
Multi-objective programming
Preferential weight choice
Goal programming
Sensitivity analysis
Multicriteria analysis
Decision making
Preference
Multiobjective programming
Optimization
ISSN:0377-2217, 1872-6860
Online Access:Get full text
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Summary:► A weight sensitivity algorithm for goal and multi-objective programming is presented. ► Solutions from portion of weight space of interest to the decision maker are generated. ► The algorithm is demonstrated on two examples from the literature. This paper presents a weight sensitivity algorithm that can be used to investigate a portion of weight space of interest to the decision maker in a goal or multiple objective programme. The preferential information required from the decision maker is an initial estimate of their starting solution, with an equal weights solution being used as a default if this is not available, and preference information that will define the portion of weight space on which the sensitivity analysis is to be conducted. The different types of preferential information and how they are incorporated by the algorithm are discussed. The output of the algorithm is a set of distinct solutions that characterise the portion of weight space searched. The possible different output requirements of decision makers are detailed in the context of the algorithm. The methodology is demonstrated on two examples, one hypothetical and the other relating to predicting cinema-going behaviour. Conclusions and avenues for future research are given.
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ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2011.03.012