Topic
Goal programming
About: Goal programming is a research topic. Over the lifetime, 4330 publications have been published within this topic receiving 117758 citations.
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TL;DR: A survey of recent developments in goal programming and multiple objective optimizations can be found in this paper with emphasis on the authors' own work (with others) in a variety of applications.
665 citations
TL;DR: Modelling techniques such as detection and restoration of pareto efficiency, normalisation, redundancy checking, and non-standard utility function modelling are overviewed, and the rationality of ranking Multi-Criteria Decision Making techniques is discussed.
621 citations
TL;DR: An improved IS project selection methodology which reflect interdependencies among evaluation criteria and candidate projects using analytic network process (ANP) within a zero–one goal programming (ZOGP) model is suggested.
593 citations
TL;DR: This paper relates product characteristics to supply chain strategy and adopt supply chain operations reference (SCOR) model level I performance metrics as the decision criteria and develops an integrated analytic hierarchy process and preemptive goal programming based multi-criteria decision-making methodology.
590 citations
Proceedings Article•
01 Jan 2005TL;DR: This study illustrates how a technique such as the multiobjective genetic algorithm can be applied and exemplifies how design requirements can be refined as the algorithm runs, and demonstrates the need for preference articulation in cases where many and highly competing objectives lead to a nondominated set too large for a finite population to sample effectively.
Abstract: In this talk, fitness assignment in multiobjective evolutionary algorithms is interpreted as a multi-criterion decision process. A suitable decision making framework based on goals and priorities is formulated in terms of a relational operator, characterized, and shown to encompass a number of simpler decision strategies, including constraint satisfaction, lexicographic optimization, and a form of goal programming. Then, the ranking of an arbitrary number of candidates is considered, and the ef- fect of preference changes on the cost surface seen by an evolutionary algorithm is illustrated graphically for a simple problem. The formulation of a multiobjective genetic algorithm based on the pro- posed decision strategy is also discussed. Niche formation techniques are used to promote diversity among preferable candidates, and progressive articulation of preferences is shown to be possible as long as the genetic algorithm can recover from abrupt changes in the cost landscape. Finally, an application to the optimization of the low-pressure spool speed governor of a Pegasus gas turbine engine is described, which il- lustrates how a technique such as the Multiobjective Genetic Algorithm can be applied, and exemplifies how design requirements can be refined as the algorithm runs. The two instances of the problem studied demonstrate the need for pref- erence articulation in cases where many and highly competing objectives lead to a non-dominated set too large for a finite population to sample ef- fectively. It is shown that only a very small portion of the non-dominated set is of practical relevance, which further substantiates the need to sup- ply preference information to the GA.
587 citations