This report provides a comparison of discrete-event simulation tools for supply chain models. We use different simulation tools (Arena and AnyLogic) to analyze and inspect the C14 supply chain management benchmark, as well as, a real-world business supply chain model that is provided by Vantai company in China. In this study, we consider different aspects of these simulation tools (such as, the capability of discrete-event simulation, visualization, simulation efficiency and accuracy, and debugging) to explore their advantages and disadvantages. We hope that our simulation results will have a positive impact on the supply chain management of the companies that provided us with data for this study; furthermore, the comparison results may be useful to developers and researchers in future simulation studies.

En vue d’éviter ou de diminuer l’importance des dégâts causés par les accidents majeurs, il convient de modéliser les fonctions et les relations entre les composants d’un système industriel. Pour cela, dans cette thèse, on utilise la démarche de modélisation par la méthode STAMP (Systems-Theoretic Accident Model and Processes) pour représenter la structure hiéarchique du système, ainsi que les mécanismes de contrôle nécessaire pour préserver la sécurité d’un processus industriel. Afin de traiter les problématiques liées aux contrôles de la sécurité industrielle, on propose l’utilisation de l’outil de simulation Anylogic. Cet outil, permet modéliser et de simuler le comportement du système en fonction du temps en mode normal et en mode dégradé.L’objectif de ces travaux est donc de proposer une démarche basée à la fois sur la modélisation et la simulation pour analyser les risques et prévenir les accidents d’un site de stockage et distribution de GPL (Gaz Pétrole liquéfié). / System thinking concepts and simulation tools are used to model the risk prevention plan and operational modes designed to enforce safety constraints at a French liquefied petroleum gas (LPG) storage and distribution facility. In France, such facilities are classified and the subject of special legislation and safety regulations. Their supervision is the responsibility of a control and regulatory body. A technological risk and prevention plan is provided, where all the dangerous phenomena likely to occur in addition to the safety control measures are listed in the safety report. Safety is therefore addressed through rules, and control mechanisms ensure that the system complies with safety constraints. Taking this facility as a case study, we use the STAMP theoretical framework together with AnyLogic simulation software to model technical elements and human and organizational behavior. We simulate how the system evolves over time and the strategies that are deployed in a loss of control scenario. The aim is to assess whether the prescribed safety program covers all of the system's phases; namely operations and audits. The results enrich other research that focuses on the contribution of system dynamics to risk analysis and accident prevention.

Currently, the road cargo system with low or zero CO2 emission is under rapid development. Heavy-duty trucks with electrified driveline systems will be the workhorse of future freight. But developing such a brand new and very complex system and adapting it to various application scenarios, such as long-haul freight, city distribution or construction loading, is still a big problem, because there is no previous experience to refer to. There is no standard development procedure or constraint framework for uncertainty either. Simulation on a massive scale with thousands of truck agents will be of great use for developing such a road-cargo system. System engineering will be the guiding methodology for this thesis project about developing a high-performance and multi-adaptive electrified driveline system. Referring to the classical V-shape development methodology, the complex concept will be divided into different levels of subsystems, from the large application scenarios to traffic simulation, driveline system simulation, electric motor and controller blocks development, and the system integration, performance verification and output of the results. The massive scale of traffic simulation will be implemented in AnyLogic, which does not contain any accurate agent model with vehicle dynamic motion during simulation. Thus, a precise vehicle agent model needs to be developed and embedded into AnyLogic’s simulation scenario, so as to make the simulation very close to reality, and to be able to evaluate vehicle concepts as well. The driveline system will be developed in Matlab/Simulink while the information communication between them will be realised in the form of computational calculation functions through the C language program. The development of the driveline model is also progressive. First, an equation-based full glider model was constructed. It simulates the scenario of a heavy-loaded truck driving on a steep slope (30% grade), decelerating from the initial 70 km/h to 0 km/h and then remaining stationary. The second model added the functionality of velocity input and output, enabling information exchange with AnyLogic. It will judge the real-time speed and the desired speed to decide whether to accelerate or decelerate and it uses the “Bang-Bang” control method of the electric motor. But this control mode results in a massive and frequent change in the electric motor output power, leading to extremely high energy consumption and in real life significantly shortened motor lifetime. So a powerful PI controller was introduced to the third Simulink model. The PI controller is embedded in the electric motor and it will replace the “Bang-Bang” control method. The “PID” control method provides a more stable power output so that the truck’s real-time speed can approach the target speed more smoothly. This control system can adapt to a variety of speed inputs and it can decide whether to output full power or partial power, depending on the speed difference. The third version of the Simulink model with PI controller has been verified as an acceptable model through various inputs of different speeds, and it will be converted into a C language program to be embedded in AnyLogic for massive traffic simulation. / Utveckling av godstrafiksystem på väg med låga eller noll CO2-utsläpp är under snabb utveckling. Tunga lastbilar med elektrifierade drivlinesystem kommer förmodligen att vara dominerande i framtiden för vägfrakt. Att utveckla ett helt nytt system med hög teknisk komplexitet och anpassning till olika tillämpningsscenarier, som fjärrtransport, distribution eller bygg och anläggning, är en stor utmaning på grund av kritiska begränsningar i exempelvis erfarenhet. Det finns heller ingen etablerad utvecklingsmetodik baserad på tidigare erfarenhet eller ramverk för att hantera osäkerheter. Simuleringar med tusentals lastbilsagenter kan vara till stor nytta och stöd för att utveckla lastbilssystem. Systemteknik kommer att vara den vägledande metodiken för detta examensarbete för att utveckla ett avancerat och multiadaptivt elektrifierat drivlinesystem. Med hjälp av den klassiska "V"-utvecklingsmetodiken kommer drivlinemodellen delas in i olika nivåer av delsystem utifrån de tidigare nämnda olika tillämpnings-scenarierna för trafiksimulering för att sedan utföra simulering av komplett drivlinesystem, utveckling av elmotor och reglersystem till systemintegration, prestandaverifiering och analys av resultat. Trafiksimulering är tänkt att implementeras i AnyLogic, som inte innehåller någon modell baserad på fordonsdynamik där drivlineegenskaper beaktas. Därför måste en specifk fordonsdynamikmodell utvecklas för fordonsagenten och bäddas in i AnyLogics simuleringsscenario för att göra simuleringen närmre verkligheten. Drivlinesystemet har utvecklats i Matlab/Simulink för att sedan realiseras i form av beräkningsfunktioner i C. Utvecklingen av drivlinemodellen görs iterativt. Först konstruerades en ekvationsbaserad full s.k. glidermodell. Den simulerar scenariot för en tung lastad lastbil som kör i en brant sluttning (30\% lutning), bromsar in från de ursprungliga 70 km/h till 0 km/h och förblir stillastående. I den andra modellen lades till gränssnitt för agentens begynnelsevärden och tillstånd vid tidsstegets slut, vilket möjliggjorde informationsutbyte med AnyLogic. Modellen analyserar realtidshastigheten och den önskade hastigheten för att sedan avgöra om agenten ska accelerera eller bromsa. Den använder "Bang-Bang"-reglermetoden för elmotorn. Men denna reglermetod resulterar i en frekvent och stor förändring av elmotorns uteffekt, vilket leder till hög energiförbrukning och i verkligheten avsevärt förkortad motorlivslängd. Därför introducerades en PI-regulator i den tredje Simulink-modellen. PI-regulatorn är inbäddad i elmotorn och ersätter "Bang-Bang"-reglermetoden. "PID"-regulatorn ger mer stabil effekt, så att lastbilens realtidshastighet mjukare kan närma sig målhastigheten. Detta reglersystem kan anpassas till en mängd olika begynnelsehastigheter och kan bestämma om full eller deleffekt ska matas ut beroende på skillnaden mellan realtidshastighet och målhastighet. Den tredje versionen av Simulink-modellen med PI-regulator har verifierats som en acceptabel modell genom olika begynnelsehastigheter och konverterats till ett C-program för att bäddas in i AnyLogic för trafiksimulering.

ENGLISH ABSTRACT: It is very seldom that a decision-making problem concerns only a single value or objective. The process of simultaneously optimising two or more con icting objectives is known as multi-objective optimisation (MOO). A number of metaheuristics have been successfully adapted for MOO. The aim of this study was to investigate the feasibility of applying an agent-based modelling approach to MOO. The (s; S) inventory problem was chosen as the application eld for this approach and Anylogic used as model platform. Agents in the model were responsible for inventory and sales management, and had to negotiate with each other in order to nd optimal reorder strategies. The introduction of concepts such as agent satisfaction indexes, aggression factors, and recollection ability guided the negotiation process between the agents. The results revealed that the agents had the ability to nd good strategies. The Pareto front generated from their proposed strategies was a good approximation to the known front. The approach was also successfully applied to a recognised MOO test problem proving that it has the potential to solve a variety of MOO problems. Future research could focus on further developing this approach for more practical applications such as complex supply chain systems, nancial models, risk analysis and economics. / AFRIKAANSE OPSOMMING: Daar is weinig besluitnemingsprobleme waar slegs 'n enkele waarde of doelwit ter sprake is. Die proses waar twee of meer doelwitte, wat in konflik staan met mekaar, gelyktydig optimiseer word, staan bekend as multi-doelwit optimisering (MOO). 'n Aantal metaheuristieke is al suksesvol aangepas vir MOO. Die doelwit van hierdie studie was om ondersoek in te stel na die lewensvatbaarheid van die toepassing van 'n agent gebasseerde modelerings benadering tot MOO. As toepassingsveld vir hierdie benadering was die (s; S) voorraad probleem gekies en Anylogic was gebruik as model platform. In die model was agente verantwoordelik vir voorraad- en verkope bestuur. Hulle moes onderling met mekaar onderhandel om die optimale bestelling strategiee te verkry. Konsepte soos agentbevrediging, aggressie faktore en herinneringsvermoens is ingestel om die onderhandeling tussen die agente te bewerkstellig. Die resultate het gewys dat die agente oor die vermoe beskik om met goeie strategiee vorendag te kom. Die Pareto fronte wat gegenereer is deur hul voorgestelde strategiee was 'n goeie benadering tot die bekende front. Die benadering was ook suksesvol toegepas op 'n erkende MOO toets-probleem wat bewys het dat dit oor die potensiaal beskik om 'n verskeidenheid van MOO probleme op te los. Toekomstige navorsing kan daarop fokus om hierdie benadering verder te ontwikkel vir meer praktiese toepassings soos komplekse voorsieningskettingstelsels, finnansiele modelle, risiko-analises en ekonomie.

Cette thèse a pour objectif principal de développer un simulateur multi-agents pour concevoir un système d'aide à la maitrise des risques des activités de maintenance. Le but est d'explorer la complexité de la maintenance et de spécifier les interactions entre la fonction maintenance, l'analyse et l'évaluation des risques. Nous nous intéressons d’une part, à concevoir un modèle systémique permettant d’identifier et de modéliser un système industriel, à montrer les différentes interactions entre ses éléments, à analyser et évaluer les risques des activités de maintenance. Nous proposons la méthode MOSAR et le langage UML pour concevoir un modèle cognitif de référence. Ce modèle a servi de point de départ pour la conception d’une base de données à l’aide du langage SQL, qui est exploitée par le modèle Multi-Agents afin d’acquérir les informations nécessaires à son fonctionnement.D’autre part, nous développons une architecture d’un Système Multi-Agents qui a pour vocation d’anticiper les situations de défaillances et la prise de décisions à l’aide de la simulation du comportement du système étudié. Une comparaison entre les plateformes existantes dédiées aux Systèmes Multi-Agents est effectuée pour choisir la plateforme adéquate à notre problématique pour la réalisation de la simulation.Finalement, les modèles développés sont appliqués dans le cadre d’une chaîne logistique pour le chargement et le déchargement de GPL (Gaz de Pétrole Liquéfié). Un simulateur a été développé à l’aide de la plateforme AnyLogic dans le but d’étudier le comportement du système et de simuler les scénarios de défaillances choisis par l’industriel pour le calcul de la criticité à partir de trois paramètres (fréquence, gravité, détectabilité) et l’obtention d’un Tableau de Bord contenant un ensemble d’indicateurs de performance de la maintenance. Les modèles de simulation proposés permettent d’orienter les industries vers les bonnes décisions pour éviter les situations à risques pouvant déclencher des événements perturbateurs dommageables. / The main objective of this thesis is to develop a multi-agent approach to designing a model to overcome risks of maintenance activities. The aim is to explore the maintenance complexity and to indicate the interactions between the maintenance function and the risk assessment.Firstly, we are interested in designing a systemic model to identify and model the industrial system, to show the different interactions between its elements, to analyze and to evaluate risks of maintenance activities. We propose the MOSAR method and the UML language to design a cognitive reference model. This model served as a starting point for designing a database using the SQL language, which is operated by Multi-Agent model to acquire the necessary information for its operation.On the other hand, we develop a framework of a multi-agent system that aims to anticipate failures scenarios and the decision-making by simulating the studied system behaviour. A comparison between the existing platforms dedicated to Multi-Agent Systems is performed to choose the appropriate platform for the simulation.Finally, the developed models are applied in the LPG supply chain (Liquefied Petroleum Gas). A simulator was developed using the AnyLogic platform in order to study the system behaviour and to simulate the failure scenarios chosen by the industry, for the calculation of the criticality from three parameters (Frequency, severity, detectability), and for obtaining a Dashboard containing a set of maintenance performance indicators. The proposed simulation models help to guide the industries toward good decisions to avoid risky situations that may trigger disruptive events damaging.

No description available.

This paper describes efforts to optimize the parameters for a platoon identification and accommodation algorithm that minimizes vehicle emissions. The algorithm was developed and implemented in the AnyLogic framework, and was validated by comparing it to the results of prior research. A four-module flowchart was developed to analyze the traffic data and to identify platoons. The platoon end time was obtained from the simulation and used to calculate the offset of the downstream intersection. The simulation calculates vehicle emissions with the aid of the VT-Micro microscopic emission model. Optimization experiments were run to determine the relationship between platoon parameters and minimum- and maximum-emission scenarios. Optimal platoon identification parameters were found from these experiments, and the simulation was run with these parameters. The total time of all vehicles in the simulation was also found for minimum and maximum emissions scenarios. Time-space diagrams obtained from the simulations demonstrate that optimized parameters allow all cars to travel through the downstream intersection without waiting, and therefore cause a decrease in emissions by as much as 15.5%. This paper also discusses the outcome of efforts to leverage high resolution data obtained from WV-705 corridor in Morgantown, WV. The proposed model was developed for that purpose and implemented in the AnyLogic framework to simulate this particular road network with four coordinated signal-controlled intersections. The simulation was also used to calculate vehicle CO, HC, NOx emissions with the aid of the VT-Micro microscopic emission model. Offset variation was run to determine the optimal offsets for this particular road network with traffic volume, signal phase diagram and vehicle characteristics. A classifier was developed by discriminant analysis based on significant attributes of HRD. Equation of this classifier was developed to distinguish between set of timing plans that produce maximum emission from set of timing plans that produce maximum emission. Also, current work investigates the potential use of the GPS-based and similar priority systems by giving preemption through signalized intersections. Two flowcharts are developed to consider presence of emergency vehicle (EV) in the system so called EV life cycle and EV preemption (EVP). Three scenarios are implemented, namely base case scenario when no EV is involved, EV scenario when EV gets EVP only, and EV scenario when EV gets preemption by signals and right-of-way by other vehicles. Research makes an attempt to compare emission results of these scenarios to find out whether EV effects vehicle emission in the road network and what is the level of this influence if any. / Master of Science

This master's thesis is focused on modern methods and tools to support managerial decision-making. The first part of this thesis introduces the basic characteristics related to the management and managerial decision-making that are subsequently extended to the area of modeling, simulation, optimization and multi-criteria decision making. It also outlines the relationship between the managerial decision-making tasks. The second part introduces practical examples which show the connection of these areas. Specifically, they are a colony of ants, traveling salesman problem, a tool AnyLogic, analytic hierarchy process and simulation HealthBound. The thesis is further supplemented by an appropriate software tools to support multi-criteria decision making.

This dissertation investigates the value of deep reinforcement learning (DRL) within an agent-based model (ABM) of a large open-air venue. The intent is to reduce civilian casualties in an active shooting incident (ASI). There has been a steady increase of ASIs in the United States of America for over 20 years, and some of the most casualty-producing events have been in open spaces and open-air venues. More research should be conducted within the field to help discover policies that can mitigate the threat of a shooter in extremis. This study uses the concept of dynamic signage, controlled by a DRL policy, to guide civilians away from the threat and toward a safe exit in the modeled environment. It was found that a well-trained DRL policy can significantly reduce civilian casualties as compared to baseline scenarios. Further, the DRL policy can assist decision makers in determining how many signs to use in an environment and where to place them. Finally, research using DRL in the ASI space can yield systems and policies that will help reduce the impact of active shooters during an incident.

<p>This dissertation examines the impact of active shooters upon patrons attending large outdoor events. There has been a spike in shooters targeting densely populated spaces in recent years, to include open-air venues. The 2019 Gilroy Garlic Festival was selected for modeling replication using AnyLogic software to test various experiments designed to reduce casualties in the event of an active shooter situation. Through achievement of validation to produce identical outcomes of the real-world Gilroy Garlic Festival shooting, the researcher established a reliable foundational model for experimental purposes. This active shooter research project identifies the need for rapid response efforts to neutralize the shooter(s) as quickly as possible to minimize casualties. Key findings include the importance of armed officers patrolling event grounds to reduce response time, the need for adequate exits during emergency evacuations, incorporation of modern technology to identify the shooter’s location, and applicability of a 1:548 police to patron ratio.</p>