Paper Info
Title
Efficacy of Incentive Structures for Boundedly Rational Dispatchers in Large-Scale Queueing Networks
Abstract
This paper employs computational approaches to model and explore the efficacy of different incentive structures on the decision making behavior of dispatchers in complex queueing networks, and the subsequent effects of these decisions on teams working within the network and on network performance itself. Computational models that express network structure and function, as well as the decision making process of dispatchers operating within the network and the effect these decisions have on team performance, are presented. Performance of the network under <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">status quo</italic> and other incentive structures and decision making processes is illustrated via simulation, validated against data from a large-scale debris removal mission that followed a series of tornadoes in the U.S. state of Alabama in 2011. Results of the simulation experiments suggest that the optimal incentive structure assuming a rational decision maker remains optimal under lower levels of rationality. Furthermore, a simple uniform reward structure is likely to produce performance improvements over the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">status quo</italic> incentive structure under most scenarios.
Year
DOI
Venue
2020
10.1109/THMS.2019.2906618
IEEE Transactions on Human-Machine Systems
Keywords
Field
DocType
Disaster relief,dynamic decision making,queueing system,simulation
Rationality,Status quo,Incentive,Computer science,Dynamic decision-making,Operations research,Computational model,Queueing theory,Artificial intelligence,Decision-making,Machine learning,Network performance
Journal
Volume
Issue
ISSN
50
1
2168-2291
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
James D. Brooks146.23
David Mendonça211713.40
Xin Zhang300.34