Application–Based Statistical Approach for Identifying Appropriate Queuing Model

Queuing theory is a mathematical study of queues or waiting lines. It is used to model many systems in different fields in our life, whether simple or complex systems. The key idea in queuing theory of a mathematical model is to improve performance and productivity of the applications. Queuing models are constructed in order to compute the performance measures for the applications and to predict the waiting times and queue lengths. This thesis is depended on previous papers of queuing theory for varies application which analyze the behavior of these applications and shows how to calculate the entire queuing statistic determined by measures of variability (mean, variance and coefficient of variance) for variety of queuing systems in order to define the appropriate queuing model. Computer simulation is an easy powerful tool to estimate approximately the proper queuing model and evaluate the performance measures for the applications. This thesis presents a new simulation model for defining the appropriate models for the applications and identifying the variables parameters that affect their performance measures. It depends on values of mean, variance and coefficient of the real applications, comparing them to the values for characteristics of the queuing model, then according to the comparison the appropriate queuing model is approximately identified.The simulation model will measure the effectiveness performance of queuing models A/B/1 where A is inter arrival distribution, B is the service time distributions of the type Exponential, Erlang, Deterministic and Hyper-exponential. The effectiveness performance of queuing model are: *L : The expected number of arrivals in the system. *Lq : The expected number of arrivals in the queue. *W : The expected time required a customer to spend in the system. *Wq : The expected time required a customer to spend in Queue. *U : the server utilization

Stream Processing in the Context of CTS

The recent development of innovative technologies related to mobile computing combined with smart city infrastructures is generating massive, heterogeneous data and creating opportunities for novel applications in transportational computation science. The heterogeneous data sources provide streams of information that can be used to create smart cities. The knowledge on stream analysis is thus crucial and requires collaboration of people working in logistics, city planning, transportation engineering and data science. We provide a list of materials for a course on stream processing for computational transportation science. The objectives of the course are: Motivate data stream and event processing, its model and challenges. Acquire basic knowledge about data stream processing systems. Understand and analyze their application in the transportation domain..

Joint location and inventory models and algorithms for deployment of hybrid electric vehicle charging stations.

This thesis describes a study of a novel concept of hybrid electric vehicle charging stations in which two types of services are offered: battery swapping and fast level-3 DC charging. The battery swapping and fast-charging service are modeled by using the M/G/s/s model and the M/G/s/$\infty$ model, respectively. In particular, we focus on the operations of joint battery swapping and fast charging services, develop four joint locations and inventory models: two for the deployment of battery swapping service, two for the deployment of hybrid electric vehicle charging service. The first model for each deployment system considers a service-level constraint for battery swapping and hybrid charging service, whereas the second for each deployment system considers total sojourn time in stations. The objective of all four models is to minimize total facility setup cost plus battery and supercharger purchasing cost. The service level, which is calculated by the Erlang loss function, depends on the stockout probability for batteries with enough state of charge (SOC) for the battery swapping service and the risk of running out of superchargers for the quick charging service. The total sojourn time is defined as the sum of the service time and the waiting time in the station. Metaheuristic algorithms using a Tabu search are developed to tackle the proposed nonlinear mixed-integer optimization model. Computational results on randomly generated instances and on a real-world case comprised of 714,000 households show the efficacy of proposed models and algorithms

Ecotruck: An Agent System for Paper Recycling

Abstract. Recycling has been gaining ground, thanks to the recent progress made in the related technology. However, a limiting factor to its wide adoption, is the lack of modern tools for managing the collection of recyclable resources. In this paper, we present EcoTruck, a management system for the collection of recyclable paper products. EcoTruck is modelled as a multi-agent system and its implementation employs Erlang, a distribution-oriented declarative language. The system aims to automate communication and cooperation of parties involved in the collection process, as well as optimise vehicle routing. The latter have the effect of minimising vehicle travel distances and subsequently lowering transportation costs. By speeding up the overall recycling process, the system could increase the service throughput, eventually introducing recycling methods to a larger audience

An analysis of the passenger vehicle interface of street transit systems with applications to design optimization

This research analyzes the Passenger Vehicle Interface of the street transit systems and presents applications for design optimization. The Passenger Vehicle Interface (PVI) is defined as the interaction between the passenger and vehicle elements of the street transit system. Human observer and photographic studies were conducted in 17 cities in the United States and Canada to measure the time for queues of passengers to board various transit vehicles. The data were analyzed by considering seven factors that affect the Passenger Vehicle Interface: Human Factor, Modal Factor, Operating Practices, Operating Policies, Mobility, Climate and Weather, and Other System Elements. Those effects which could be quantified were divided into the categories of direction of flow, method of fare collection, and door characteristics and use. A series of equations for each of these categories was developed to predict passenger service time when the number of alighting or boarding passengers is known or estimated. A range of values was developed for the parameters of each equation to reflect the effects of unquantifiable factors such as the type of passenger, physical characteristics of the passenger, passenger preferences, baggage carried, seating configuration, and congestion. The use of Passenger Influence Zones has indicated that passenger service time can range from approximately six to 14 percent of total trip time, depending upon vehicle type, door use, and method of fare collection. These zones have also been used to indicate how vehicle door use and characteristics can increase berth requirements by up. to 200 percent, and New different methods of fare collection can increase berth productivity in terms of passengers per hour by 87 percent. Distributions of passenger service times through the vehicle doors were identified based on the analysis of photographic studies and determined to be represented by an Erlang function. The analysis also inferred that the K value in the Erlang function is equal to the number of doors on the vehicle and that the minimum service time is approximately equal to half the average service time. The validity of the Erlang functions was determined by using the special purpose simulation programming language, GPSS, and the Erlang functions to estimate the time requirements for queues of passengers to board vehicles. The simulated times were compared with observed times, and the differences were found to be not statistically significant at the 95 percent level. A GPSS model was used to simulate the operations of a street tran sit loading area and to evaluate the effects of method of fare collection upon queue length and average waiting time under varying rates of passenger arrivals. This research provides sufficient information to perform sub- optimizations of several operations within the Passenger Vehicle Interface. Although not directed toward an optimization of street transit systems, it does provide the necessary information about the Passenger Vehicle Interface for others to perform this optimization after they have assembled comparable information on system elements and other interactions

Applications of stochastic modeling in air traffic management:Methods, challenges and opportunities for solving air traffic problems under uncertainty

In this paper we provide a wide-ranging review of the literature on stochastic modeling applications within aviation, with a particular focus on problems involving demand and capacity management and the mitigation of air traffic congestion. From an operations research perspective, the main techniques of interest include analytical queueing theory, stochastic optimal control, robust optimization and stochastic integer programming. Applications of these techniques include the prediction of operational delays at airports, pre-tactical control of aircraft departure times, dynamic control and allocation of scarce airport resources and various others. We provide a critical review of recent developments in the literature and identify promising research opportunities for stochastic modelers within air traffic management

A new reinforcement learning algorithm with fixed exploration for semi-Markov decision processes

Artificial intelligence or machine learning techniques are currently being widely applied for solving problems within the field of data analytics. This work presents and demonstrates the use of a new machine learning algorithm for solving semi-Markov decision processes (SMDPs). SMDPs are encountered in the domain of Reinforcement Learning to solve control problems in discrete-event systems. The new algorithm developed here is called iSMART, an acronym for imaging Semi-Markov Average Reward Technique. The algorithm uses a constant exploration rate, unlike its precursor R-SMART, which required exploration decay. The major difference between R-SMART and iSMART is that the latter uses, in addition to the regular iterates of R-SMART, a set of so-called imaging iterates, which form an image of the regular iterates and allow iSMART to avoid exploration decay. The new algorithm is tested extensively on small-scale SMDPs and on large-scale problems from the domain of Total Productive Maintenance (TPM). The algorithm shows encouraging performance on all the cases studied --Abstract, page iii

Network Congestion Control of Airport Surface Operations

The reduction of taxi-out times at airports has the potential to substantially reduce delays and fuel consumption on the airport surface, and to improve the air quality in surrounding communities. The taxiway and runway systems at an airport determine its maximum possible departure throughput, or the number of aircraft departures that it can handle per unit time. Current air traffic control procedures allow aircraft to push from their gates and enter the taxiway system as soon as they are ready. As this pushback rate approaches the maximum departure throughput of the airport, runway queues grow longer and surface congestion increases, resulting in increased taxi-out times