Investigating congestion mitigation scenarios to reduce truck turn time at Port of Montreal using Discrete Event Simulation

Container ports are facing the growing problem of congestion due to the high volume of container trucks entering the terminal. Globalization, growth of trade and increasing consumer demand have further added to this complexity which has resulted in increased greenhouse gas emissions at the ports. Several measures are being undertaken by the ports to reduce this problem and improve port sustainability. Examples of these measures are implementing advanced technology equipment, implementing extended gate hours, changing the arrival patterns of trucks, and implementing variable gate lane policies. The objective of the thesis is to develop a discrete event simulation (DES) model to investigate the congestion mitigation scenarios to improve terminal productivity and reduce truck turn times at the Port of Montreal. A case study with the Montreal Port Authority is conducted. The results of our simulation study yield upgrade of technology at the terminals as the best solution followed by managing the arrival patterns, changing gate lanes and extended gating hours. The proposed work is novel and one of the very few to be conducted in the context of Port of Montreal. The generated results can be used by decision makers at Port of Montreal in developing strategies to mitigate congestion and reduce truck turn times at terminals

Real-time Traffic Flow Detection and Prediction Algorithm: Data-Driven Analyses on Spatio-Temporal Traffic Dynamics

Traffic flows over time and space. This spatio-temporal dependency of traffic flow should be considered and used to enhance the performance of real-time traffic detection and prediction capabilities. This characteristic has been widely studied and various applications have been developed and enhanced. During the last decade, great attention has been paid to the increases in the number of traffic data sources, the amount of data, and the data-driven analysis methods. There is still room to improve the traffic detection and prediction capabilities through studies on the emerging resources. To this end, this dissertation presents a series of studies on real-time traffic operation for highway facilities focusing on detection and prediction.First, a spatio-temporal traffic data imputation approach was studied to exploit multi-source data. Different types of kriging methods were evaluated to utilize the spatio-temporal characteristic of traffic data with respect to two factors, including missing patterns and use of secondary data. Second, a short-term traffic speed prediction algorithm was proposed that provides accurate prediction results and is scalable for a large road network analysis in real time. The proposed algorithm consists of a data dimension reduction module and a nonparametric multivariate time-series analysis module. Third, a real-time traffic queue detection algorithm was developed based on traffic fundamentals combined with a statistical pattern recognition procedure. This algorithm was designed to detect dynamic queueing conditions in a spatio-temporal domain rather than detect a queue and congestion directly from traffic flow variables. The algorithm was evaluated by using various real congested traffic flow data. Lastly, gray areas in a decision-making process based on quantifiable measures were addressed to cope with uncertainties in modeling outputs. For intersection control type selection, the gray areas were identified and visualized

Simulation Modeling and Analysis of Adjustable Service-Rate Queueing Models that Incorporate Feedback Control

Research shows that in a system model, when the production rate is adjusted based on the number of items in queue, the nature of the model changes from an open-loop queueing system to a closed-loop feedback control system. Service-rate adjustment can be implemented in a discrete event simulation model, but the effect of this adjustment has not been thoroughly analyzed in the literature. This research considers the design of feedback signals to generate realistic simulation models of production system behavior. A series of simulation experiments is conducted to provide practical guidance for simulation modelers on how adding a service-rate adjustment feedback loop to a queueing system affects system performance

Data-Driven Simulation Modeling of Construction and Infrastructure Operations Using Process Knowledge Discovery

Within the architecture, engineering, and construction (AEC) domain, simulation modeling is mainly used to facilitate decision-making by enabling the assessment of different operational plans and resource arrangements, that are otherwise difficult (if not impossible), expensive, or time consuming to be evaluated in real world settings. The accuracy of such models directly affects their reliability to serve as a basis for important decisions such as project completion time estimation and resource allocation. Compared to other industries, this is particularly important in construction and infrastructure projects due to the high resource costs and the societal impacts of these projects. Discrete event simulation (DES) is a decision making tool that can benefit the process of design, control, and management of construction operations. Despite recent advancements, most DES models used in construction are created during the early planning and design stage when the lack of factual information from the project prohibits the use of realistic data in simulation modeling. The resulting models, therefore, are often built using rigid (subjective) assumptions and design parameters (e.g. precedence logic, activity durations). In all such cases and in the absence of an inclusive methodology to incorporate real field data as the project evolves, modelers rely on information from previous projects (a.k.a. secondary data), expert judgments, and subjective assumptions to generate simulations to predict future performance. These and similar shortcomings have to a large extent limited the use of traditional DES tools to preliminary studies and long-term planning of construction projects. In the realm of the business process management, process mining as a relatively new research domain seeks to automatically discover a process model by observing activity records and extracting information about processes. The research presented in this Ph.D. Dissertation was in part inspired by the prospect of construction process mining using sensory data collected from field agents. This enabled the extraction of operational knowledge necessary to generate and maintain the fidelity of simulation models. A preliminary study was conducted to demonstrate the feasibility and applicability of data-driven knowledge-based simulation modeling with focus on data collection using wireless sensor network (WSN) and rule-based taxonomy of activities. The resulting knowledge-based simulation models performed very well in properly predicting key performance measures of real construction systems. Next, a pervasive mobile data collection and mining technique was adopted and an activity recognition framework for construction equipment and worker tasks was developed. Data was collected using smartphone accelerometers and gyroscopes from construction entities to generate significant statistical time- and frequency-domain features. The extracted features served as the input of different types of machine learning algorithms that were applied to various construction activities. The trained predictive algorithms were then used to extract activity durations and calculate probability distributions to be fused into corresponding DES models. Results indicated that the generated data-driven knowledge-based simulation models outperform static models created based upon engineering assumptions and estimations with regard to compatibility of performance measure outputs to reality

Stochastic Estimation and Control of Queues within a Computer Network

Captain Nathan C. Stuckey implemented the idea of the stochastic estimation and control for network in OPNET simulator. He used extended Kalman filter to estimate packet size and packet arrival rate of network queue to regulate queue size. To validate stochastic theory, network estimator and controller is designed by OPNET model. These models validated the transient queue behavior in OPNET and work of Kalman filter by predicting the queue size and arrival rate. However, it was not enough to verify a theory by experiment. So, it needed to validate the stochastic control theory with other tools to get high validity. Our goal was to make a new model to validate Stuckey’s simulation. For this validation, NS-2 was studied and modified the Kalman filter to cooperate with MATLAB. Moreover, NS-2 model was designed to predict network characteristics of queue size with different scenarios and traffic types. Through these NS-2 models, the performance of the network state estimator and network queue controller was investigated and shown to provide high validity for Stuckey’s simulations

System Identification for the design of behavioral controllers in crowd evacuations

Behavioral modification using active instructions is a promising interventional method to optimize crowd evacuations. However, existing research efforts have been more focused on eliciting general principles of optimal behavior than providing explicit mechanisms to dynamically induce the desired behaviors, which could be claimed as a significant knowledge gap in crowd evacuation optimization. In particular, we propose using dynamic distancekeeping instructions to regulate pedestrian flows and improve safety and evacuation time. We investigate the viability of using Model Predictive Control (MPC) techniques to develop a behavioral controller that obtains the optimal distance-keeping instructions to modulate the pedestrian density at bottlenecks. System Identification is proposed as a general methodology to model crowd dynamics and build prediction models. Thus, for a testbed evacuation scenario and input?output data generated from designed microscopic simulations, we estimate a linear AutoRegressive eXogenous model (ARX), which is used as the prediction model in the MPC controller. A microscopic simulation framework is used to validate the proposal that embeds the designed MPC controller, tuned and refined in closed-loop using the ARX model as the Plant model. As a significant contribution, the proposed combination of MPC control and System Identification to model crowd dynamics appears ideally suited to develop realistic and practical control systems for controlling crowd motion. The flexibility of MPC control technology to impose constraints on control variables and include different disturbance models in the prediction model has confirmed its suitability in the design of behavioral controllers in crowd evacuations. We found that an adequate selection of output disturbance models in the predictor is critical in the type of responses given by the controller. Interestingly, it is expected that this proposal can be extended to different evacuation scenarios, control variables, control systems, and multiple-input multiple-output control structures.Ministerio de Economía y Competitivida

Analysis and improvement of a bottling line using a simulation modelling approach

This project thesis is focused on the study of a bottling production line using a modelling simulation method, through which we analyse the inefficiencies and then improve their performance. Moreover, the line is also analysed thorugh an analytic approach applying a formula to optimize the buffer sizing. The two approachs are compared to highlight the differences

Assembly Line

An assembly line is a manufacturing process in which parts are added to a product in a sequential manner using optimally planned logistics to create a finished product in the fastest possible way. It is a flow-oriented production system where the productive units performing the operations, referred to as stations, are aligned in a serial manner. The present edited book is a collection of 12 chapters written by experts and well-known professionals of the field. The volume is organized in three parts according to the last research works in assembly line subject. The first part of the book is devoted to the assembly line balancing problem. It includes chapters dealing with different problems of ALBP. In the second part of the book some optimization problems in assembly line structure are considered. In many situations there are several contradictory goals that have to be satisfied simultaneously. The third part of the book deals with testing problems in assembly line. This section gives an overview on new trends, techniques and methodologies for testing the quality of a product at the end of the assembling line