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AppliedMath
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Trends in Simulation and Its Applications
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Trends in Simulation and Its Applications

A special issue of AppliedMath (ISSN 2673-9909).

Deadline for manuscript submissions: closed (31 October 2023) | Viewed by 3539

Special Issue Editors

Prof. Dr. David F. Muñoz

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Guest Editor
Department of Industrial and Operations Engineering, Instituto Tecnológico Autónomo de Mexico, Ciudad de México 01080, Mexico
Interests: modelling and optimization; systems simulation; production and operations management
Prof. Dr. Rodolfo Morales

E-Mail Website
Guest Editor
Department of Metallurgy, Instituto Politécnico Nacional-ESIQIE, Ciudad de México 07738, Mexico
Interests: turbulent flows; CFD; steelmaking and continuous casting; kinetics of metallurgical processes
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Adán Ramírez-López

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Guest Editor
Department of Industrial Engineering, Technological and Autonomous Institute of Mexico (ITAM), Rio Hondo #1 Col. Tizapan, Mexico City 01080, Mexico
Interests: computer simulation; numerical analysis; industrial processes
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Vladimir Strezov

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Guest Editor
School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
Interests: environmental modelling; energy modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Alejandro Mac Cawley

E-Mail Website
Guest Editor
Department of Industrial and Systems Engineering, Pontificia Universidad Católica de Chile, Santiago 8331150, Chile
Interests: modelling; simulation and optimization; logistics; production planning; Industry 4.0; healthcare

Special Issue Information

Dear Colleagues,

We are pleased to draw your attention to the Special Issue of AppliedMath on “Trends in Simulation and Its Applications”.

Simulation, broadly understood as “computer imitation of the behavior of a system, using a (mathematical) model to explain the relevant characteristics of the system, in order to numerically evaluate the system's performance measures”, has long been recognized as a powerful tool for the design and analysis of existing and new systems.

Computer simulation has become a powerful tool for the analysis of complex systems on every engineering area, development of algorithms and the creation of software simulators which use finite-element and discrete-event theory have made possible the analysis of multiple sophisticated scientific and industrial cases.

Simulation experiments using a stochastic simulation model have long been used for the estimation of performance measures for the assessment and mitigation of risk. Stochastic simulation has been widely used for risk assessment in many areas, for example, in supply chain management, where risk measures are mainly related to shortages, the occurrence of catastrophes, and the costs incurred. Stochastic simulation has also been extensively used in the areas related to production planning to design products with high reliability, for example, for water distribution, for the design of integrated circuits, or for the design of highly reliable products. One area of production planning where stochastic simulation is particularly important for risk mitigation is operations scheduling, where the achievement of programs that meet delivery dates is especially important when using a digital twin under the framework of Industry 4.0. In this Special Issue, we invite manuscripts dealing with theoretical and application results with consistent methods for the estimation of expectations, nonlinear functions of expectations, quantiles, and other performance measures from the output of simulation experiments. Applications and results for both transient and steady-state simulations will be considered, and methodologies that consider the assessment of the accuracy of the corresponding point estimators are especially welcome.

Works related to numerical approaching theory and computer simulation with direct applications in physics such as fluid flow, heat and mass transfer, mechanics, industrial processing analysis based on simulation are also welcome.

Topics of interest include but are not limited to:

  • Analysis methodology
  • Data science for simulation
  • Digital twins and Industry 4.0
  • Markov chain Monte Carlo theory and applications
  • Model uncertainty and robust simulation
  • Simulation input analysis
  • Simulation optimization
  • Simulation output analysis
  • Numerical approaching theory
  • Computational fluids dynamics
  • Fundamentals of physics with mathematical and computational applications
  • Computer simulation of industrial processes
  • Simulation applications: supervised learning in all areas, environmental sciences, climate sciences, biomedical sciences, chemical sciences and chemometrics, econometrics and finance, engineering design, astronomy and physics, agronomy and forestry sciences, energy, sustainable development, mining sciences, social web, information and communications technology, security, biometry, internet of things, natural disaster modeling.

Prof. Dr. David F. Muñoz
Prof. Dr. Rodolfo Morales
Prof. Dr. Adán Ramírez-López
Prof. Dr. Vladimir Strezov
Dr. Alejandro Mac Cawley
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. AppliedMath is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • risk management
  • industrial processes
  • simulation-based design
  • systems simulation
  • numerical analysis

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Published Papers (3 papers)

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Research

26 pages, 1614 KiB  
Article
Simulation and Analysis of Line 1 of Mexico City’s Metrobus: Evaluating System Performance through Passenger Satisfaction
by Jose Pablo Rodriguez and David F. Muñoz
AppliedMath 2023, 3(3), 664-689; https://doi.org/10.3390/appliedmath3030035 - 8 Sep 2023
Viewed by 1798
Abstract
The Mexico City Metrobus is one of the most popular forms of public transportation inside the city, and since its opening in 2005, it has become a vital piece of infrastructure for the city; this is why the optimal functioning of the system [...] Read more.
The Mexico City Metrobus is one of the most popular forms of public transportation inside the city, and since its opening in 2005, it has become a vital piece of infrastructure for the city; this is why the optimal functioning of the system is of key importance to Mexico City, as it plays a crucial role in moving millions of passengers every day. This paper presents a model to simulate Line 1 of the Mexico City Metrobus, which can be adapted to simulate other bus rapid transit (BRT) systems. We give a detailed description of the model development so that the reader can replicate our model. We developed various response variables in order to evaluate the system’s performance, which focused on passenger satisfaction and measured the maximum occupancy that a passenger experiences inside the buses, as well as the time that he spends in the queues at the stations. The results of the experiments show that it is possible to increase passenger satisfaction by considering different combinations of routes while maintaining the same fuel consumption. It was shown that, by considering an appropriate combination of routes, the average passenger satisfaction could surpass the satisfaction levels obtained by a 10% increase in total fuel consumption. Full article
(This article belongs to the Special Issue Trends in Simulation and Its Applications)
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19 pages, 661 KiB  
Article
Estimation of Expectations and Variance Components in Two-Level Nested Simulation Experiments
by David Fernando Muñoz
AppliedMath 2023, 3(3), 582-600; https://doi.org/10.3390/appliedmath3030031 - 7 Aug 2023
Cited by 1 | Viewed by 1196
Abstract
When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm n observations [...] Read more.
When there is uncertainty in the value of parameters of the input random components of a stochastic simulation model, two-level nested simulation algorithms are used to estimate the expectation of performance variables of interest. In the outer level of the algorithm n observations are generated for the parameters, and in the inner level m observations of the simulation model are generated with the values of parameters fixed at the values generated in the outer level. In this article, we consider the case in which the observations at both levels of the algorithm are independent and show how the variance of the observations can be decomposed into the sum of a parametric variance and a stochastic variance. Next, we derive central limit theorems that allow us to compute asymptotic confidence intervals to assess the accuracy of the simulation-based estimators for the point forecast and the variance components. Under this framework, we derive analytical expressions for the point forecast and the variance components of a Bayesian model to forecast sporadic demand, and we use these expressions to illustrate the validity of our theoretical results by performing simulation experiments with this forecast model. We found that, given a fixed number of total observations nm, the choice of only one replication in the inner level (m=1) is recommended to obtain a more accurate estimator for the expectation of a performance variable. Full article
(This article belongs to the Special Issue Trends in Simulation and Its Applications)
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19 pages, 559 KiB  
Article
Assessing by Simulation the Effect of Process Variability in the SALB-1 Problem
by Luis A. Moncayo-Martínez and Elias H. Arias-Nava
AppliedMath 2023, 3(3), 563-581; https://doi.org/10.3390/appliedmath3030030 - 28 Jul 2023
Viewed by 1514
Abstract
The simple assembly line balancing (SALB) problem is a significant challenge faced by industries across various sectors aiming to optimise production line efficiency and resource allocation. One important issue when the decision-maker balances a line is how to keep the cycle time under [...] Read more.
The simple assembly line balancing (SALB) problem is a significant challenge faced by industries across various sectors aiming to optimise production line efficiency and resource allocation. One important issue when the decision-maker balances a line is how to keep the cycle time under a given time across all cells, even though there is variability in some parameters. When there are stochastic elements, some approaches use constraint relaxation, intervals for the stochastic parameters, and fuzzy numbers. In this paper, a three-part algorithm is proposed that first solves the balancing problem without considering stochastic parameters; then, using simulation, it measures the effect of some parameters (in this case, the inter-arrival time, processing times, speed of the material handling system which is manually performed by the workers in the cell, and the number of workers who perform the tasks on the machines); finally, the add-on OptQuest in SIMIO solves an optimisation problem to constrain the cycle time using the stochastic parameters as decision variables. A Gearbox instance from literature is solved with 15 tasks and 14 precedence rules to test the proposed approach. The deterministic balancing problem is solved optimally using the open solver GLPK and the Pyomo programming language, and, with simulation, the proposed algorithm keeps the cycle time less than or equal to 70 s in the presence of variability and deterministic inter-arrival time. Meanwhile, with stochastic inter-arrival time, the maximum cell cycle is 72.04 s. The reader can download the source code and the simulation models from the GitHub page of the authors. Full article
(This article belongs to the Special Issue Trends in Simulation and Its Applications)
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