Volume 4 (2024/2025)


The E-Dossier for Optimization of Italian Civil Courts: The Telematization of the Cuneo Court
Ilaria Amantea, Marianna Molinari, Marinella Quaranta, Christine Peduto, Francesca Demarchi
DOI: 10.5281/zenodo.11114250

Abstract: This article presents a framework for integrating Business Process Management (BPM) and simulation to optimize administration processes in Italian courts and tribunals. The aim is to support the telematisation and dematerialisation of dossiers so that paper-based records are eliminated and replaced by information systems workflows. Electronic dossiers can have a positive impact in terms of efficiency and productivity in several tribunals. In the Italian context, Legislative Decree no. 149/2022 set the goal of digitalising civil processes, while Legislative Decree no. 150/2022 mandated that the digitalisation of the criminal procedural system should be accelerated. This necessitates an analysis of each change process and a reorganization model. This article analyses the workflow of the civil area of the Tribunal of Cuneo, with a specific focus on files relating to civil litigation and labour practices divisions. The BPM method was used to capture pre-reform processes and introduce some corrective actions in order to create a new workflow model based on the digitalisation reforms and solutions required by the Italian Ministry of Justice. Since the digitalisation reform measures apply to all Italian courts and tribunals, the ultimate goal of this article is to provide an optimisation model that can be applied to all Italian civil processes and, in the future, criminal processes. An optimised exportable model for all the divisions of all tribunals in Italy will result in significant savings in the use of paper, operational costs and labour (which is particularly beneficial in situations of staff shortages). It will reduce backlogs and make smart working more feasible.

Simulating and Validating Vehicular Cloud Computing Applications in MEC-enabled 5G Environments
Angelo Feraudo, Alessandro Calvio, Paolo Bellavista
DOI: 10.5281/zenodo.11114240

Abstract: The groundbreaking advancements in in-vehicle computing/communication resources and software are granting drivers' access to a diverse range of distributed applications and services. Edge Computing, alongside established frameworks like the European Telecommunications Standards Institute (ETSI) Multi-access Edge Computing (MEC), will play a vital role in these scenarios, by enabling the interoperable and standardized execution of these services at the edge of the network. In addition, Vehicular Cloud Computing (VCC) contributes to expanding computational capacity at the edge by leveraging computing/storage/communication resources offered by vehicles. This synergy holds the potential to forge robust computational infrastructures at the network edge, by favoring several benefits like real-time data processing and minimal latency. However, the research community lacks simulation tools for supporting the testing and validation of applications that exploit both the VCC paradigm and edge-enabled networks at the same time. In this paper, we present our novel simulation tool as a platform for researchers and engineers to design, test, and enhance next-generation distributed applications that exploit the concepts of vehicular, edge, and cloud computing. This simulation tool implements our novel ETSI MEC-compliant architecture, which, in a standard way, supports the leveraging of in-vehicle resources to increase edge computing ones. In addition, the paper reports performance results about the efficiency/scalability of our simulation platform and presents a practical use case where an original algorithm to distribute MEC application components on vehicular resources is validated.

Volume 3 (2022/2023)


A Gamified Synthetic Environment for Evaluation of Counter-Disinformation Solutions
Jesse Richman, Lora Pitman, Girish Nandakumar
DOI: 10.5281/zenodo.11114235

Abstract: This paper presents a simulation-based approach to developing strategies aimed at countering online disinformation and misinformation. This disruptive technology experiment incorporated a synthetic environment component, based on an adapted Susceptible-Infected-Recovered (SIR) epidemiological model to evaluate and visualize the effectiveness of suggested solutions to the issue. The participants in the simulation were given two realistic scenarios depicting a disinformation threat and were asked to select a number of solutions, described in Ideas-of-Systems (IoS) cards. During the event, the qualitative and quantitative characteristics of the IoS cards were tested in a synthetic environment, built after a SIR model. The participants, divided into teams, presented and justified their strategy which included three IoS card selections. A jury of subject matter experts, announced the winning team, based on the merits of the proposed strategies and the compatibility of the different cards, grouped together.


Improving Delivery Performance of Construction Manufacturing Using Machine Learning
Ian Flood, Xiaoyan Zhou
DOI: 10.5281/zenodo.11114237

Abstract: This paper is concerned with the development, testing, and optimization of a machine learning method for controlling the production of precast reinforced concrete components. A discussion is given identifying the unique challenges associated with achieving production efficiency in the construction industry, namely: uncertain and sporadic demand for work; high customization of the design of components; a need to produce work to order; and little prospect for stockpiling work. This is followed by a review of the methods available to tackle this problem, which can be divided into search-based techniques (such as heuristics) and experience-based techniques (such as artificial neural networks). A model of an actual factory for producing precast reinforced concrete components is then described, to be used in the development and testing of the controller. A reinforcement learning strategy is proposed for training a deep artificial neural network to act as the control policy for this factory. The ability of this policy to learn is evaluated, and its performance is compared to that of a rule-of-thumb and a random policy for a series of testing production runs. The reinforcement learning method developed an effective and reliable policy that significantly outperformed the rule-of-thumb and random policies. An additional series of experiments were undertaken to further optimize the performance of the method, ranging the number of input variables presented to the policy. The paper concludes with an indication of proposed future research designed to further improve performance and to extend the scope of application of the method.

Volume 2 (2020/2021)


Implementing Asynchronous Linear Solvers Using Non-uniform Distributions
Erik John Jensen, Evan Coleman, Masha Sosonkina
DOI: 10.5281/zenodo.11114215

Abstract: Asynchronous iterative methods may improve the time-to-solution of their synchronous counterparts on highly parallel computational platforms. This paper considers asynchronous iterative linear system solvers that employ non-uniform randomization and develops a new implementation for such methods. Experiments with a two-dimensional finite-difference discrete Laplacian problem are presented. The new finer grain implementation is compared with an existing block-based one and shown to be superior in terms of the convergence speed and accuracy. In general, using non-uniform distributions in selecting components to update may lead to faster convergence. In particular, the new implementation converges up to 10% faster when it uses a non-uniform distribution.

Volume 1 (2018/2019)


Schema-based Ontological Representations of a Domain-Specific Scenario Modeling Language
Shafagh Jafer, Bharvi Chhaya, Jessica Updegrove, Umut Durak
DOI: 10.5281/zenodo.11114156

Abstract: The first step in designing a domain-specific simulation scenario definition language is constructing its ontology. The recently published Aviation Scenario Definition Language (ASDL) aims at providing a common platform to specify scenarios in the aviation domain. To capture an ASDL ontology, the Web Ontology Language and the Protégé tool was utilized, which was then converted into an XML schema by means of tool automation in the Eclipse Modeling Framework. On the other hand, the System Entity Structure (SES) provides a formal basis to represent the ontological foundations of a domain language. Following the XML Schema representation of SES, a scenario modeling ontology that has been recently published, we illustrate how an equivalent schema for a scenario definition language can be constructed using a domain-specific language ontology-driven approach and SES. We take both approaches to represent the ASDL ontology and prove that the resulting schema produced from these two approaches converge to the same result.

Assessments of Introducing New Technologies in Disaster Prevention Planning
Tomoichi Takahashi
DOI: 10.5281/zenodo.11114175

Abstract: Simulations have been used in multiple fields. Social simulation, in which humans play a major role in simulated scenarios, is one of the most promising fields in research. Human behavior is difficult to mathematically formulate. This makes it challenging to determine how well simulations mimic real situations; confirming simulation results empirically is also difficult. In this study, we present a new device that could change human behavior and provide a good method for mitigating damages during emergencies. We also consider ways of validating the simulation results to assess the new technology adoption.

Simulation Framework for Asynchronous Iterative Methods
Evan Christopher Coleman, Erik Jensen, Masha Sosonkina
DOI: 10.5281/zenodo.11114192

Abstract: As high-performance computing (HPC) platforms progress towards exascale, computational methods must be revamped to successfully leverage them. In particular, (1) asynchronous methods become of great importance because synchronization becomes prohibitively expensive and (2) resilience of computations must be achieved, e.g., using checkpointing selectively which may otherwise become prohibitively expensive due to the sheer scale of the computing environment. In this work, a simulation framework for asynchronous iterative methods is proposed and tested on HPC accelerator (shared-memory) architecture. The design proposed here offers a lightweight alternative to existing computational frameworks to allow for easy experimentation with various relaxation iterative techniques, solution updating schemes, and predicted performance. The simulation framework is implemented in MATLAB® using function handles, which offers a modular and easily extensible design. An example of a case study using the simulation framework is presented to examine the efficacy of different checkpointing schemes for asynchronous relaxation methods.


Information and Process Modeling for Simulation – Part I: Objects and Events
Gerd Wagner
DOI: 10.5281/zenodo.11110129

Abstract: In simulation engineering, a system model mainly consists of an information model describing a system's state structure and a process model describing its dynamics. In the fields of Information Systems and Software Engineering, there are widely used standards such as the Class Diagrams of the Unified Modeling Language (UML) for making information models, and the Business Process Modeling Notation (BPMN) for making process models. This tutorial presents a general Object Event Modeling (OEM) approach for Discrete Event Simulation modeling using UML class diagrams and BPMN-based process diagrams at all three levels of model-driven simulation engineering: for making conceptual domain models, for making platform-independent simulation design models, and for making platform-specific, executable simulation models. In this approach, object and event types are modeled as special categories of UML classes, random variables are modeled as a special category of UML operations constrained to comply with a specific probability distribution, and queues are modeled as ordered association ends, while event rules are modeled both as BPMN-based process diagrams and pseudo-code. In Part II, we discuss the more advanced OEM concepts of activities and GPSS/SIMAN/Arena-style Processing Networks. Finally, in Part III, we further extend the OEM paradigm towards agent-based modeling and simulation by adding the concepts of agents with perceptions, actions and beliefs.


Panel Discussion: On the Unity and Diversity of Computer Simulation
Alexis Drogoul, Paul Fishwick, Nigel Gilbert, Dennis Pegden, Gerd Wagner, Levent Yilmaz
DOI: 10.5281/zenodo.11114180

Abstract: The term Computer Simulation subsumes different simulation paradigms, languages and implementation technologies as well as many different application areas each with its own scientific communities. So, there is clearly a lot of conceptual, methodological, technological and application diversity in the area of Computer Simulation. From its start in 1967, the Winter Simulation Conference managed to get four scientific communities involved: computer scientists, electrical engineers, industrial engineers and mathematicians (operations researchers). Only later, in 2011 and 2012, an attempt was made to get environmental and social scientists involved who have been adopting the idea of "individual-based" or "agent-based" simulation. Today, two American, a European and an Asian social simulation conference have been established. How much unity exists between the scientific areas and communities represented by the Winter Simulation Conference? How much unity exists between the scientific areas and communities represented by the newer social science simulation conferences? And how much unity exists between Discrete Event Simulation and the newer forms of social science simulation? These and other questions about the unity and diversity of Computer Simulation have been discussed via email from April 17 to May 17, 2018, by five leading experts: Alexis Drogoul, Paul Fishwick, Nigel Gilbert, Dennis Pegden and Levent Yilmaz, moderated by Gerd Wagner.