Multi-agent systems and microeconomic theory: a negotiation approach to solve scheduling problems in high dynamic environments

Czap, Hans; Becker, Marc

doi:10.1109/hicss.2003.1174204

Cited by 9 publications

(3 citation statements)

References 7 publications

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“…In [13] a multi-agent approach is suggested to solve scheduling in hospitals. The authors distinguish multiple planning steps, and consider negotiation between agents for a schedule improvement.…”

Section: Introductionmentioning

confidence: 99%

Multi-agent Pareto appointment exchanging in hospital patient scheduling

Vermeulen

Bohté

Somefun

et al. 2007

SOCA

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We present a dynamic and distributed approach to the hospital patient scheduling problem, in which patients can have multiple appointments that have to be scheduled to different resources. To efficiently solve this problem we develop a multi-agent Pareto-improvement appointment exchanging algorithm: MPAEX. It respects the decentralization of scheduling authorities and continuously improves patient schedules in response to the dynamic environment. We present models of the hospital patient scheduling problem in terms of the health care cycle where a doctor repeatedly orders sets of activities to diagnose and/or treat a patient. We introduce the Theil index to the health care domain to characterize different hospital patient scheduling problems in terms of the degree of relative workload inequality between required resources. In experiments that simulate a broad range of hospital patient scheduling problems, we extensively compare the performance of MPAEX to a set of scheduling benchmarks. The distributed and dynamic MPAEX performs almost as good as the best centralized and static scheduling heuristic, and is robust for variations in the model settings.Keywords Health care · Patient scheduling · Multi-agent systems A preliminary version of this work has appeared as [1].

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Section: Introductionmentioning

confidence: 99%

Multi-agent Pareto appointment exchanging in hospital patient scheduling

Vermeulen

Bohté

Somefun

et al. 2007

SOCA

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“…That means that the highest rank is assigned to the most preferred stimulus. 7 Actually, this is the 'quick and dirty method' for deriving part-worths, that is used by most commercial conjoint applications like SPSS, and which assumes equidistance between the ranks revealed by the principal. More correct is the application of non-metric algorithms like MONANOVA [13], [14], LINMAP [22] or PREFMAP [5], which lead to slightly better results at the cost of greater complexity [6].…”

mentioning

confidence: 99%

Estimating Utility-Functions for Negotiating Agents: Using Conjoint Analysis as an Alternative Approach to Expected Utility Measurement

Becker

Czap

Poppensieker

et al. 2005

Multiagent System Technologies

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Abstract. Utility-based software agents are especially suited to represent human principals in recurring automatic negotiation applications. In order to work efficiently, utility-based agents need to obtain models of the relevant part of the principal's preference structure -represented by utility functions. So far agent theory usually applies expected utility measurement. It has, as we will show, certain shortcomings in real life applications. As an alternative, we suggest an approach based on con-joint analysis, which is a well-understood procedure widely used in marketing research and psychology, but gets only small recognition in agent theory. It offers a user-friendly way to derive quantitative utility values for multi-attribute alternatives from the principal's preferences. In this paper, we introduce the technique in detail along with some extensions and improvements suited for agent applications. Additionally a learning algorithm is derived, keeping track of changes of the principal's preference structure and adjusting measurement errors.

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“…These properties make it especially suitable for tackling complex tasks with a lot of stakeholders [11,1]. Multi-agent methods have found applications in a variety of problem domains, such as airport resource scheduling [3], load allocation in transportation logistics [6], supply chain management [10], etc.…”

Section: Introductionmentioning

confidence: 99%