Assessment of Multiattributed Measurable Value and Utility Functions via Mathematical Programming*

Klein, Gary; Moskowitz, Herbert; Mahesh, Sathiadev; Ravindran, A. Ravi

doi:10.1111/j.1540-5915.1985.tb01682.x

Cited by 23 publications

(3 citation statements)

References 15 publications

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“…Therefore, methods from multiple objective (vector) optimization (Benson and Morin, 1977;Geoffrion, 1968;Soland, 1979 andYu, 1985) or multi-criteria utility theory (Debreu, 1954;Fishburn, 1964;Keeney and Raiffa, 1976;Klein, 1985 andYu, 1985) could be effectively used for quantifying the degree of nesting and for comparing alternative designs. In the former case, we would solve for the set of all efficient (Pareto or vector optimal) designs.…”

Section: Volume Occupancymentioning

confidence: 99%

Evaluating part designs for automatic feeding in robotic assembly systems: A geometric analysis of the nesting of polyhedral objects

Veeramani

Morin

1991

J Intell Manuf

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One of the problems encountered by automatic feeding devices, such as vibratory bowl feeders, in robotic assembly systems is nesting which leads to inseparability of parts and jamming. In this paper, we describe a design evaluation procedure to determine whether a part is prone to nesting and to quantify the degree of nesting. The proposed methodology reduces the 3-D protrusion-hole polyhedral containment problem of nesting to a 2-D polygon containment problem by employing a loop-based feature recognition scheme. Subsequently, the 3-D containment configuration is reconstructed from that of the 2-D by using a strategy called drop, pull andpush. In this context, we introduce the notion of axis as a characteristic direction of a feature that facilitates the analysis of generalized 3-D polyhedral containment. The algorithms are applicable to generic B-Rep CAD data, and their time and space complexities are polynomial.

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Section: Volume Occupancymentioning

confidence: 99%

Evaluating part designs for automatic feeding in robotic assembly systems: A geometric analysis of the nesting of polyhedral objects

Veeramani

Morin

1991

J Intell Manuf

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“…This problem is considered as one of the most classical problems in the operational research, where the allocation is decided by validating a single optimization criterion (mono-criterion problem) or multiple optimization criteria (multi-criteria problem) [4], [5]. This paper attends to face the problem of task allocation in a multi-agent system.…”

Section: Introductionmentioning

confidence: 99%

AgentAllocator: An Agent-Based Multi-criteria Decision Support System for Task Allocation

Matsatsinis

Delias

2003

Holonic and Multi-Agent Systems for Manufacturing

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Abstract. The multi-agent systems in Artificial Intelligence very often include task allocation problems. These problems are arduous to be modelled; therefore it is also difficult to end up with an optimal allocation plan. AgentAllocator is an easy to use, platform independent application, which implements a multicriteria method to support the decision of Task Allocation. The decision maker is able to model the problem (according to his policy) through its inputs dialogs and employ the final solution proposed by the system. In this paper, both the theoretical background of AgentAllocator and the DSS itself are presented.

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“…Those functions should be normalized according to (8.4) conditions. Generally, the approaches applied in this construction step are: techniques based on MAUT theory and described by Keeney and Raiffa (1976) [48] and Klein et al (1985) [51], a) b) the MACBETH method [3,4,2] and Chapter 10 in this book, c) the Quasi-UTA method by Beuthe et al (2000) [8], that uses "recursive exponential" marginal value functions, and d) the MIIDAS system (see Section 4) that combines artificial intelligence and visual procedures in order to extract the DM's preferences [95].…”

Section: Other Variants and Extensionsmentioning

confidence: 99%

UTA Methods

Siskos

Grigoroudis

Matsatsinis

International Series in Operations Research &Amp; Management Science

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UTA methods refer to the philosophy of assessing a set of value or utility functions, assuming the axiomatic basis of MAUT and adopting the preference disaggregation principle. UTA methodology uses linear programming techniques in order to optimally infer additive value/utility functions, so that these functions are as consistent as possible with the global decision-maker's preferences (inference principle). The main objective of this chapter is to analytically present the UTA method and its variants and to summarize the progress made in this field. The historical background and the philosophy of the aggregation-disaggregation approach are firstly given. The detailed presentation of the basic UTA algorithm is presented, including discussion on the stability and sensitivity analyses. Several variants of the UTA method, which incorporate differentforms of optimality criteria, are also discussed. The implementation of the UTA methods is illustrated by a general overview of UTA-based DSSs, as well as real-world decision-making applications. Finally, several potential future research developments are discussed.UTA methods, preference disaggregation, ordinal regression, additive utility, multicriteria analysis.

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Assessment of Multiattributed Measurable Value and Utility Functions via Mathematical Programming*

Cited by 23 publications

References 15 publications

Evaluating part designs for automatic feeding in robotic assembly systems: A geometric analysis of the nesting of polyhedral objects

Evaluating part designs for automatic feeding in robotic assembly systems: A geometric analysis of the nesting of polyhedral objects

AgentAllocator: An Agent-Based Multi-criteria Decision Support System for Task Allocation

UTA Methods

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