A Hybrid Dynamic System Assessment Methodology for Multi-Modal Transportation-Electrification

In recent years, electrified transportation, be it in the form of buses, trains, or cars have become an emerging form of mobility. Electric vehicles (EVs), especially, are set to expand the amount of electric miles driven and energy consumed. Nevertheless, the question remains as to whether EVs will be technically feasible within infrastructure systems. Fundamentally, EVs interact with three interconnected systems: the (physical) transportation system, the electric power grid, and their supporting information systems. Coupling of the two physical systems essentially forms a nexus, the transportation-electricity nexus (TEN). This paper presents a hybrid dynamic system assessment methodology for multi-modal transportation-electrification. At its core, it utilizes a mathematical model which consists of a marked Petri-net model superimposed on the continuous time microscopic traffic dynamics and the electrical state evolution. The methodology consists of four steps: (1) establish the TEN structure; (2) establish the TEN behavior; (3) establish the TEN Intelligent Transportation-Energy System (ITES) decision-making; and (4) assess the TEN performance. In the presentation of the methodology, the Symmetrica test case is used throughout as an illustrative example. Consequently, values for several measures of performance are provided. This methodology is presented generically and may be used to assess the effects of transportation-electrification in any city or area; opening up possibilities for many future studies

Recent advances in petri nets and concurrency

CEUR Workshop Proceeding

Land-Cover and Land-Use Study Using Genetic Algorithms, Petri Nets, and Cellular Automata

Recent research techniques, such as genetic algorithm (GA), Petri net (PN), and cellular automata (CA) have been applied in a number of studies. However, their capability and performance in land-cover land-use (LCLU) classification, change detection, and predictive modeling have not been well understood. This study seeks to address the following questions: 1) How do genetic parameters impact the accuracy of GA-based LCLU classification; 2) How do image parameters impact the accuracy of GA-based LCLU classification; 3) Is GA-based LCLU classification more accurate than the maximum likelihood classifier (MLC), iterative self-organizing data analysis technique (ISODATA), and the hybrid approach; 4) How do genetic parameters impact Petri Net-based LCLU change detection; and 5) How do cellular automata components impact the accuracy of LCLU predictive modeling. The study area, namely the Tickfaw River watershed (711mi²), is located in southeast Louisiana and southwest Mississippi. The major datasets include time-series Landsat TM / ETM images and Digital Orthophoto Quarter Quadrangles (DOQQ’s). LCLU classification was conducted by using the GA, MLC, ISODATA, and Hybrid approach. The LCLU change was modeled by using genetic PN-based process mining technique. The process models were interpreted and input to a CA for predicting future LCLU. The major findings include: 1) GA-based LCLU classification is more accurate than the traditional approaches; 2) When genetic parameters, image parameters, or CA components are configured improperly, the accuracy of LCLU classification, the coverage of LCLU change process model, and/or the accuracy of LCLU predictive modeling will be low; 3) For GA-based LCLU classification, the recommended configuration of genetic / image parameters is generation 2000-5000, population 1000, crossover rate 69%-99%, mutation rate 0.1%-0.5%, generation gap 25%-50%, data layers 16-20, training / testing data size 10000-20000 / 5000-10000, and spatial resolution 30m-60m; 4) For genetic Petri nets-based LCLU change detection, the recommended configuration of genetic parameters is generation 500, population 300, crossover rate 59%, mutation rate 5%, and elitism rate 4%; and 5) For CA-based LCLU predictive modeling, the recommended configuration of CA components is space 6025 * 12993, state 2, von Neumann neighborhood 3 * 3, time step 2-3 years, and optimized transition rules

The American Multi-modal Energy System: Model Development with Structural and Behavioral Analysis using Hetero-functional Graph Theory

In the 21st century, infrastructure is playing an ever greater role in our daily lives. Presidential Policy Directive 21 emphasizes that infrastructure is critical to public confidence, the nation\u27s safety, and its well-being. With global climate change demanding a host of changes across at least four critical energy infrastructures: the electric grid, the natural gas system, the oil system, and the coal system, it is imperative to study models of these infrastructures to guide future policies and infrastructure developments. Traditionally these energy systems have been studied independently, usually in their own fields of study. Therefore, infrastructure datasets often lack the structural and dynamic elements to describe the interdependencies with other infrastructures. This thesis refers to the integration of the aforementioned energy infrastructures into a singular system-of-systems within the context of the United States of America as the American Multi-modal Energy System (AMES). This work develops an open-source structural and behavioral model of the AMES using Hetero-functional Graph Theory (HFGT), a data-driven approach, and model-based systems engineering practices in the following steps. First, the HFGT toolbox code is made available on GitHub and advanced to produce HFGs of systems on the scale of the AMES using the languages Python and Julia. Second, the analytical insights that HFGs can provide relative to formal graphs are investigated through structural analysis of the American Electric Power System which demonstrates how HFGs are better equipped to describe changes in system behavior. Third, a reference architecture of the AMES is developed, providing a standardized foundation to develop future models of the AMES. Fourth, the AMES reference architecture is instantiated into a structural model from which structural properties are investigated. Finally, a physically informed Weighted Least Squares Error Hetero-functional Graph State Estimation analysis of the AMES\u27 socio-economic behavior is implemented to investigate the behavior of the AMES with asset level granularity. These steps provide a reproducible and reusable structural and behavioral model of the AMES for guiding future policies and infrastructural developments to critical energy infrastructures

GIS in Healthcare

The landscape of healthcare is dynamic, gradually becoming more complicated with factors beyond simple supply and demand. Similar to the diversity of social, political and economic contexts, the practical utilization of healthcare resources also varies around the world. However, the spatial components of these contexts, along with aspects of supply and demand, can reveal a common theme among these factors. This book presents advancements in GIS applications that reveal the complexity of and solutions for a dynamic healthcare landscape

Simulation-Based Control of Complex Material Handling Systems

2008-2009Material Handling (MH) consists in the movement and storage of parts, in a manufacturing or distribution process, from one location to another. Material Handling Systems (MHSs) are everywhere in production plants, assembly lines, product distribution, logistics, intermodal activities (railways, road transportation, container ships, etc..). They usually are distributed, sometimes itinerant and often mixed manned and automated. Although not adding value in the manufacturing process, MH usually influences great part of a company’s operation costs, especially, for example, in the food distribution chain. Due to the increasing demand for a high variety of products, flexibility and efficiency are two important keywords in MHSs. Optimizing MH activities means having shorter response times and an increased throughput of the plant. The importance of this optimization process is very high in today’s companies. Nowadays, the interest in this process is growing rapidly since several new technologies, like the Radio Frequency Identification (RFID) are available which finally allow to introduce an automation level to operating MHSs, almost without stopping operations and at a very low cost. In MHSs control iusses involve the problem of the optimal sequencing and scheduling of short-term activities. The so-called problem of "Dispatching” consists in defining a procedure to assign resources to missions. This is often made by using heuristic rules called Dispaching rules. For control purposes, a model of the system is necessary. Due to the complex and heterogeneous nature of MHSs, modeling approaches proposed in the literature are typically very specific and context-dependent. Moreover, the strong combinatorial nature of the control problem, and the presence of a great number of constraints to be considered, usually make the design of a control solution very tough. To devise a closed form analytical control action can require a great computational effort and could result not so convenient. Indeed, turbulence and variations in the input set of the system can suddenly make not more adequate a hardly designed control action. Thus, the choice of Dispatching rules as control actions, despite producing only local optimum solutions, is very usual for MHSs. Dispatching rules, indeed, result in a more reasonable and robust way to control MHSs since they are effective and computationally inexpensive. In the absence of a closed form control solution, Simulation is fundamental to evaluate the effects of a control action which cannot be analytically predicted. The outcome of the application of a rule or another can be easily tested via simulation and this is the reason why having a good model assumes a further major importance. In this thesis a unique arcchitecture for the modeling and the control of complex MHSs has been proposed.VIII n.s