Maria Pereda1,2,3

1 Grupo de Investigación Ingeniería de Organización y Logística (IOL), Departamento Ingeniería de Organización, Administración de empresas y Estadística,
Escuela Técnica Superior de Ingenieros Industriales, Universidad Politécnica de Madrid. C/ José Gutiérrez Abascal, 2. 28006, Madrid, Spain

2 Unidad Mixta Interdisciplinar de Comportamiento y Complejidad Social
(UMICCS), 28911 Leganés, Madrid, Spain

3 Grupo Interdisciplinar de Sistemas Complejos, Madrid, Spain

Keywords: Engineering education, simulation, decision biases, queuing theory, behavioural operations research.

1. Introduction

Today’s world is marked by continuous digitalization at all levels, including education. Moreover, the recent health crisis caused by the SARS-CoV-2 pandemic has only accelerated this process of digitization, and the reinvention of teaching methods and ways of teaching. Among these new methods are simulations, which allow students to learn concepts by experiencing them for themselves, and to be surprised by results that are not so intuitive a priori. Many industrial engineering programs include courses on stochastic systems and queuing theory [1]. When modelling social systems or systems in which there are interactions with humans, the decision-making capabilities of those humans and the cognitive biases they suffer from must be taken into account in order to accurately model such systems.

The aim of this work is to create an interactive simulation tool to explain the influence of decision biases in queuing systems in an engineering classroom. Specifically, the model includes the trembling hand effect, when people in a strategic interaction choose a different strategy than the one they intended to choose [2]; and the herding effect, when people imitate the behaviour of the group instead of using their own information to decide independently [3].

2. Decision Biases in Queuing systems model

The DBQ (Decision Biases in Queuing systems) model represents a system composed of five servers that serve people arriving at the system. Since a server can only serve one person at a time, a queue precedes each server. People are assigned by default to the emptiest queue but may experience decision biases that direct them to another queue instead of the emptiest one. With probability pTrembling a person suffers the trembling hand effect and chooses a server at random; with probability pHerding a person experiences the herding effect and chooses the server preferred by most people and whose queue is the busiest; and with probability (1- pTrembling – pHerding) a person chooses the emptiest queue.

The model is implemented in Simio 10.165.15447[4] and can be downloaded from GitHub

3. Experiments to show in class

The model includes a series of experiments to test the effects, first, separately, and then operating both at the same time.  The trembling hand effect separately produces two types of results: it has almost no effect whether the system is very empty or fully congested, and it does have an effect in intermediate cases where it produces a noise-like effect, increasing the variability in both queue times and server usage.

The herding effect produces an exponential growth in queuing times when the probability of this effect occurring is greater than 70%.

Both effects occurring at the same time produce an unequal use of system resources, and an overall increase in queuing times (being more striking as the herding effect becomes more pronounced).

4. Conclusions

This simple model makes it possible to teach these phenomena in the classroom, how they influence the time people spend waiting in a queuing system and how they contribute to the system congestion. This work also contributes to the introduction of psychological and sociological concepts to engineering students, as well as to the understanding of the effect that heterogeneity can have on systems.

Acknowledgments. This research has been funded by the Spanish Ministerio de Ciencia, Innovación y Universidades-FEDER funds of the European Union support, under project BASIC (PGC2018-098186-B-I00).


  1. Saaty, T.L.: Elements of queueing theory, with applications. McGraw-Hill, New York (1961)
  2. Baddeley, M.: Herding, social influence and economic decision-making: socio- psychological and neuroscientific analyses. Philosophical transactions of the Royal Society of London. Series B, Biological sciences 365(1538), 281–290 (Jan 2010)
  3. Osborne, M., Rubinstein, A.: A Course in Game Theory. The MIT Press, MIT Press (1994)
  4. Simio: Simio simulation and scheduling software (2021),


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Proceedings of the 15th International Conference on Industrial Engineering and Industrial Management and XXV Congreso de Ingeniería de Organización Copyright © by (Eds.) José Manuel Galán; Silvia Díaz-de la Fuente; Carlos Alonso de Armiño Pérez; Roberto Alcalde Delgado; Juan José Lavios Villahoz; Álvaro Herrero Cosío; Miguel Ángel Manzanedo del Campo; and Ricardo del Olmo Martínez is licensed under a Creative Commons Attribution 4.0 International License, except where otherwise noted.

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