Cost-Effective, Time-Efficient Passenger Rail System for the Eastern United States

A program was developed using a genetic algorithm and automated lookup features to design an efficient passenger rail system for the eastern-half of the United States connecting large cities, metropolitan populations greater than two million, with overnight rail service. The results of the program predicted a passenger starting at the farthest point of the system boards the train at 16:02 on average and arrives at a different point of the system at 07:57 on average the following day, assuming the train travels an average speed of 70 mph. The design used actual distances by train track where possible. The system was modeled with six trains that meet at a hub and exchange passengers and continue on to their destination.The optimal solution had a total one-way minimum distance of 4334 km (2693 miles). Assuming the same ridership that currently exists on a popular train route, ticket prices would average $62 (USD) for a one-way ticket. For this system to be feasible, the government would need to own or lease one set of tracks for all the routes determined, build a hub for passengers to transfer trains near Charleston,WV, and ensure the trains are unimpeded by other trains. Installing tracks that go around cities that the trains do not stop at would be a great benefit also. With advances in communication, GPS, and train control technology, this article points out the benefits of publically available tracks to form a transportation network similar to that found in road, air, and water traffic

Applications of Artificial Intelligence Techniques in Optimizing Drilling

Artificial intelligence has transformed the industrial operations. One of the important applications of artificial intelligence is reducing the computational costs of optimization. Various algorithms based on their assumptions to solve problems have been presented and investigated, each of which having assumptions to solve the problems. In this chapter, firstly, the concept of optimization is fully explained. Then, an artificial bee colony (ABC) algorithm is used on a case study in the drilling industry. This algorithm optimizes the problem of study in combination with ANN modeling. At the end, various models are fully developed and discussed. The results of the algorithm show that by better understanding the drilling data, the conditions can be improved

Genetic Algorithm Optimization of Charging Infrastructure Locations for the Operation of Battery-Electric Trains in German Regional Passenger Rail.

Responding to global climate change and political aims at the European level, the German government aims to achieve carbon neutrality by 2045. Embedded in this aim lies a necessity for converting diesel multiple units to electric multiple units, especially on regional rail passenger routes where diesel multiple units are often used. Making great use of existing electrification and longer layover times, battery electric multiple units are particularly well suited for these routes. However, identifying the best the spatial allocation of charging infrastructure to allow for their operation constitutes a complex non-linear optimization problem. This thesis develops a standardized methodological concept based in a genetic algorithm to optimize this spatial distribution with the least infrastructure cost using vehicle circulation plans, simulated vehicle power at wheel, OpenStreetMap data, a digital terrain model, a timetable dataset, and route geometries. The approach is developed and tested on a circulation plan for the German subnetwork of Pfalznetz. The methodology successfully identifies optimal spatial distributions of charging infrastructure but suffers from shortcomings relating to the quality and availability of input data, the computational efficiency of the algorithm and the approaching of local minima. Specific minor alterations to the code may significantly improve the quality of the results. The flexibility and broad applicability of the method primes it for expansion accounting for a wider range of aspects relating to charging infrastructure placement

Development of transportation and supply chain problems with the combination of agent-based simulation and network optimization

Demand drives a different range of supply chain and logistics location decisions, and agent-based modeling (ABM) introduces innovative solutions to address supply chain and logistics problems. This dissertation focuses on an agent-based and network optimization approach to resolve those problems and features three research projects that cover prevalent supply chain management and logistics problems. The first case study evaluates demographic densities in Norway, Finland, and Sweden, and covers how distribution center (DC) locations can be established using a minimizing trip distance approach. Furthermore, traveling time maps are developed for each scenario. In addition, the Nordic area consisting of those three countries is analyzed and five DC location optimization results are presented. The second case study introduces transportation cost modelling in the process of collecting tree logs from several districts and transporting them to the nearest collection point. This research project presents agent-based modelling (ABM) that incorporates comprehensively the key elements of the pick-up and delivery supply chain model and designs the components as autonomous agents communicating with each other. The modelling merges various components such as GIS routing, potential facility locations, random tree log pickup locations, fleet sizing, trip distance, and truck and train transportation. The entire pick-up and delivery operation are modeled by ABM and modeling outcomes are provided by time series charts such as the number of trucks in use, facilities inventory and travel distance. In addition, various scenarios of simulation based on potential facility locations and truck numbers are evaluated and the optimal facility location and fleet size are identified. In the third case study, an agent-based modeling strategy is used to address the problem of vehicle scheduling and fleet optimization. The solution method is employed to data from a real-world organization, and a set of key performance indicators are created to assess the resolution's effectiveness. The ABM method, contrary to other modeling approaches, is a fully customized method that can incorporate extensively various processes and elements. ABM applying the autonomous agent concept can integrate various components that exist in the complex supply chain and create a similar system to assess the supply chain efficiency.Tuotteiden kysyntä ohjaa erilaisia toimitusketju- ja logistiikkasijaintipäätöksiä, ja agenttipohjainen mallinnusmenetelmä (ABM) tuo innovatiivisia ratkaisuja toimitusketjun ja logistiikan ongelmien ratkaisemiseen. Tämä väitöskirja keskittyy agenttipohjaiseen mallinnusmenetelmään ja verkon optimointiin tällaisten ongelmien ratkaisemiseksi, ja sisältää kolme tapaustutkimusta, jotka voidaan luokitella kuuluvan yleisiin toimitusketjun hallinta- ja logistiikkaongelmiin. Ensimmäinen tapaustutkimus esittelee kuinka käyttämällä väestötiheyksiä Norjassa, Suomessa ja Ruotsissa voidaan määrittää strategioita jakelukeskusten (DC) sijaintiin käyttämällä matkan etäisyyden minimoimista. Kullekin skenaariolle kehitetään matka-aikakartat. Lisäksi analysoidaan näistä kolmesta maasta koostuvaa pohjoismaista aluetta ja esitetään viisi mahdollista sijaintia optimointituloksena. Toinen tapaustutkimus esittelee kuljetuskustannusmallintamisen prosessissa, jossa puutavaraa kerätään useilta alueilta ja kuljetetaan lähimpään keräyspisteeseen. Tämä tutkimusprojekti esittelee agenttipohjaista mallinnusta (ABM), joka yhdistää kattavasti noudon ja toimituksen toimitusketjumallin keskeiset elementit ja suunnittelee komponentit keskenään kommunikoiviksi autonomisiksi agenteiksi. Mallinnuksessa yhdistetään erilaisia komponentteja, kuten GIS-reititys, mahdolliset tilojen sijainnit, satunnaiset puunhakupaikat, kaluston mitoitus, matkan pituus sekä monimuotokuljetukset. ABM:n avulla mallinnetaan noutojen ja toimituksien koko ketju ja tuloksena saadaan aikasarjoja kuvaamaan käytössä olevat kuorma-autot, sekä varastomäärät ja ajetut matkat. Lisäksi arvioidaan erilaisia simuloinnin skenaarioita mahdollisten laitosten sijainnista ja kuorma-autojen lukumäärästä sekä tunnistetaan optimaalinen toimipisteen sijainti ja tarvittava autojen määrä. Kolmannessa tapaustutkimuksessa agenttipohjaista mallinnusstrategiaa käytetään ratkaisemaan ajoneuvojen aikataulujen ja kaluston optimoinnin ongelma. Ratkaisumenetelmää käytetään dataan, joka on peräisin todellisesta organisaatiosta, ja ratkaisun tehokkuuden arvioimiseksi luodaan lukuisia keskeisiä suorituskykyindikaattoreita. ABM-menetelmä, toisin kuin monet muut mallintamismenetelmät, on täysin räätälöitävissä oleva menetelmä, joka voi sisältää laajasti erilaisia prosesseja ja elementtejä. Autonomisia agentteja soveltava ABM voi integroida erilaisia komponentteja, jotka ovat olemassa monimutkaisessa toimitusketjussa ja luoda vastaavan järjestelmän toimitusketjun tehokkuuden arvioimiseksi yksityiskohtaisesti.fi=vertaisarvioitu|en=peerReviewed

A first approach to the optimization of Bogotá's TransMilenio BRT system

Bus rapid transit (BRT) systems are massive transport systems with medium/high capacity, high quality service and low infrastructure and operating costs. TransMilenio is Bogotá's most important mass transportation system and one of the biggest BRT systems in the world, although it only has completed its third construction phase out of a total of eight. In this paper we review the proposals in the literature to optimize BRT system operation, with a special emphasis on TransMilenio, and propose a mathematical model that adapts elements of the above proposals and incorporates novel elements accounting for the features of TransMilenio system

Multi Objective Ant Colony Optimisation to obtain efficient metro speed profiles

[EN] Obtaining efficient speed profiles for metro trains is a multi- objective optimisation problem where energy consumption and travel time must be balanced. Automatic Train Operation (ATO) systems may handle a great number of possible speed profiles; hence optimisation algorithms are required find efficient ones in a timely manner. This paper aims to assess the performance of a particular meta-heuristic optimisation algorithm, a variation of the traditional Ant Colony (ACO) modified to deal with multi-objective problems with continuous variables: MOACOr. This algorithm is used to obtain efficient speed profiles in up to 32 interstation sections in the metro network of Valencia (Spain), and the convergence and diversity of these solution sets is evaluated through metrics such as Inverse Generational Distance (GD) and Normalised Hypervolume (NH). The results are then compared to those obtained with a conventional genetic algorithm (NSGA-II), including a statistical analysis to identify significant differences. It has been found that MOACOr shows a better performance than NSGA-II in terms of convergence, regularity and diversity of the solution. These results indicate that MOACOr is a good alternative to the widely used genetic algorithm and could be a better tool for rail operation managers trying to improve energy efficiency.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Spanish Ministry of Economy and Competitiveness grant number TRA2011-26602.Martínez Fernández, P.; Font Torres, JB.; Villalba Sanchis, I.; Insa Franco, R. (2023). Multi Objective Ant Colony Optimisation to obtain efficient metro speed profiles. Proceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit. 237(2):232-242. https://doi.org/10.1177/09544097221103351232242237

Efficiency of the rail sections in Brazilian railway system, using TOPSIS and a genetic algorithm to analyse optimized scenarios

A railway system plays a significant role in countries with large territorial dimensions. The Brazilian rail cargo system (BRCS), however, is focused on solid bulk for export. This paper investigates the extreme performances of BRCS through a new hybrid model that combines TOPSIS with a genetic algorithm for estimating the weights in optimized scenarios. In a second stage, the significance of selected variables was assessed. The transport of any type of cargo, a centralized control of the operation, and sharing the railway track pushing competition, and the diversification of services are significant for high performance. Public strategies are discussed.Indisponível

Aerodynamic Optimization and Wind Load Evaluation Framework for Tall Buildings

Wind is the governing load case for majority of tall buildings, thus requiring a wind responsive design approach to control and assess wind-induced loads and responses. The building shape is one of the main parameters that affects the aerodynamics that creates a unique opportunity to control the wind load and consequently building cost without affecting the structural elements. Therefore, aerodynamic mitigation has triggered many researchers to investigate various building shapes that can be categorized into local (e.g. corners) and global mitigations (e.g. twisting). Majority of the previous studies compare different types of mitigations based on a single set of dimensions for each mitigation types. However, each mitigation can produce a wide range of aerodynamic performances by changing the dimensions. Thus, the first millstone of this thesis is developing an aerodynamic optimization procedure (AOP) to reduce the wind load by coupling Genetic Algorithm, Computational Fluid Dynamics (CFD) and an Artificial Neural Network surrogate model. The proposed procedure is adopted to optimize building corners (i.e. local) using three-dimensional CFD simulations of a two-dimensional turbulent flow. The AOP is then extended to examine global mitigations (i.e. twisting and opening) by conducting CFD simulations of three dimensional turbulent wind flow. The procedure is examined in single- and multi-objective optimization problems by comparing the aerodynamic performance of optimal shapes to less optimal ones. The second milestone is to develop accurate numerical wind load evaluation model to validate the performance of the optimized shapes. This is primary achieved through the development of a robust inflow generation technique, called the Consistent Discrete Random Flow Generation (CDRFG). The technique is capable of generating a flow field that matches the target velocity and turbulence profiles in addition to, maintaining the coherency and the continuity of the flow. The technique is validated for a standalone building and for a building located at a city center by comparing the wind pressure distributions and building responses with experimental results (wind tunnel tests). In general, the research accomplished in this thesis provides an advancement in numerical climate responsive design techniques, which enhances the resiliency and sustainability of the urban built environment

New approach for Arabic named entity recognition on social media based on feature selection using genetic algorithm

Many features can be extracted from the massive volume of data in different types that are available nowadays on social media. The growing demand for multimedia applications was an essential factor in this regard, particularly in the case of text data. Often, using the full feature set for each of these activities can be time-consuming and can also negatively impact performance. It is challenging to find a subset of features that are useful for a given task due to a large number of features. In this paper, we employed a feature selection approach using the genetic algorithm to identify the optimized feature set. Afterward, the best combination of the optimal feature set is used to identify and classify the Arabic named entities (NEs) based on support vector. Experimental results show that our system reaches a state-of-the-art performance of the Arab NER on social media and significantly outperforms the previous systems

BIM-based Generative Modular Housing Design and Implications for Post-Disaster Housing Recovery

The adverse social and financial impacts of catastrophic disasters are increasing as population centers grow. After disastrous events, the government agencies must respond to post-disaster housing issues quickly and efficiently and provide sufficient resources for the reconstruction of destroyed and damaged houses for full rehabilitation. However, post-disaster housing reconstruction is a highly complex process because of the large number of projects, shortage of resources, and heavy pressure for delivery of the projects after a disastrous event. This complexity and lack of an inconsistent, systematic approach for planning lead to an ad-hoc decision-making process and inefficient recovery. This research explored modular construction as a highly time-efficient approach to tackle the abovementioned challenges and facilitate the housing reconstruction process. Firstly, this research investigated the feasibility of using the modular construction method for rapid post-disaster housing reconstruction through a targeted literature review and survey of subject matter experts to broaden the understanding of modular construction-based post-disaster housing reconstruction, benefits, and barriers. Second, this research focused on improving the design and pre-planning phase of modular construction that can facilitate the successful implementation of modular construction in a post-disaster situation. To this end, a BIM-based generative modular housing design system was developed by using Generative Adversarial Networks (GANs) to automate the entire design process by incorporating manufacturing and construction constraints to fit the needs of the modular construction method. The framework was further extended by developing an optimization model to optimize the modularization strategy in the early design phase which was capable of reflecting the entire multi-stage process of modular construction (production, transportation, and assembly), and considering both individual project’s requirements and post-disaster housing reconstruction portfolio’s requirements. The outcomes of this study fit the MC industry that may be used by designers and modular housing companies looking to automate their design process. It is also expected to provide critical benchmarks for planners, decision-makers, and community developers to facilitate their decision-making process on considering modular construction as an efficient way for mass post-disaster housing reconstruction and addressing communities’ housing needs following a disastrous event