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The dynamic construction site layout planning (DCSLP) problem refers to the efficient placement and relocation of temporary construction facilities within a dynamically changing construction site environment considering the characteristics of facilities and work interrelationships, the shape and topography of the construction site, and the time-varying project needs. A multi-objective dynamic optimization model is developed for this problem that considers construction and relocation costs of facilities, transportation costs of resources moving from one facility to another or to workplaces, as well as safety and environmental considerations resulting from facilities’ operations and interconnections. The latter considerations are taken into account in the form of preferences or constraints regarding the proximity or remoteness of particular facilities to other facilities or work areas. The analysis of multiple project phases and the dynamic facility relocation from phase to phase highly increases the problem size, which, even in its static form, falls within the NP (for Nondeterministic Polynomial time)- hard class of combinatorial optimization problems. For this reason, a genetic algorithm has been implemented for the solution due to its capability to robustly search within a large solution space. Several case studies and operational scenarios have been implemented through the Palisade’s Evolver software for model testing and evaluation. The results indi­cate satisfactory model response to time-varying input data in terms of solution quality and computation time. The model can provide decision support to site managers, allowing them to examine alternative scenarios and fine-tune optimal solutions according to their experience by introducing desirable preferences or constraints in the decision process.

Recent technological advancements in distributed sensing, pervasive computing, context-awareness, machine learning and Digital Twins (DTs) allow the built environment to cope with upcoming challenges in a better way than before and achieve comfort and well-being in buildings. This paper takes a unique approach by not conducting a systematic and exhaustive review, that would require enormous effort to uncover intricate interdependencies among various subtopics. Instead, it proposes a framework leveraging Artificial Intelligence and Machine Learning (AI/ML) techniques to extract valuable insights from the existing literature. Adopting the Digital Twin high-level architecture as its foundation, the paper introduces a clustering approach to scrutinize Indoor Environmental Quality, Energy Efficiency, and Occupant Comfort—key facets influencing indoor building performance. This innovative methodology aims to provide a more nuanced understanding of the relationships within these critical aspects by harnessing the capabilities of AI/ML techniques and the conceptual framework of Digital Twin architecture.

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project’s size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects.

Electric Vehicles offer one of the most efficient solutions towards the direction of providing sustainable transportation systems. However, a broader market uptake of Electric Vehicle--based mobility is still missing. The lack of sufficient infrastructure (Electric Vehicle charging stations) in combination with the lack of information about their availability appears as a major limitation, leading to low user acceptance. Additional, technology based, assistance services provided to Electric Vehicle users is a key solution to unlock the full potential of their utilization. This paper presents a multi-factor dynamic optimization model using multi-criteria analysis to select the best alternatives for Electric Vehicle charging within a smart grid with the goal of supporting a larger uptake of Electric Vehicle -based mobility. The application provides assistance to the Electric Vehicle drivers through functionalities of energy price, cost and travel time of the electric vehicle to the charging station, the specifications of vehicles and stations, the status of the charging stations as well as the user's preferences. The proposed model is developed by incorporating PROMETHEE II and Analytic Hierarchy Process methodologies to provide the best charging solutions after considering all possible options for each Electric Vehicle user. The multi-criteria analysis algorithm is not only limited to comparing alternative charging options at a specific time but also looks at several starting times of charging. A simulated case study is implemented to examine the functionality of the proposed model. From the results, it is evident that by applying the findings of this work entrepreneurial community and industry can develop new services that will improve user satisfaction, electromobility, urban mobility, and sustainability of cities. At the same time, academia, leveraging the methodology and factors that influence the choice of charging station, can conduct further research on digital innovations that will contribute to the consolidation of e-mobility ensuring the sustainability of cities, while accelerating digital transformation in the transport sector.

Undergraduate students participating in the UCLA Undergraduate Research Consortium for Functional Genomics (URCFG) have conducted a two-phased screen using RNA interference (RNAi) in combination with fluorescent reporter proteins to identify genes important for hematopoiesis in Drosophila. This screen disrupted the function of approximately 3500 genes and identified 137 candidate genes for which loss of function leads to observable changes in the hematopoietic development. Targeting RNAi to maturing, progenitor, and regulatory cell types identified key subsets that either limit or promote blood cell maturation. Bioinformatic analysis reveals gene enrichment in several previously uncharacterized areas, including RNA processing and export and vesicular trafficking. Lastly, the participation of students in this course-based undergraduate research experience (CURE) correlated with increased learning gains across several areas, as well as increased STEM retention, indicating that authentic, student-driven research in the form of a CURE represents an impactful and enriching pedagogical approach.

Market penetration of electric vehicles is nowadays gaining considerable momentum and so is the move towards increasingly distributed clean and renewable electricity sources. The penetration rate varies among countries due to several factors, including the social and technical readiness of the community to adopt and use this technology. In addition, the increasing complexity of power grids, growing demand as well as environmental and energy sustainability concerns intensify the need for energy management solutions and energy demand reduction strategies. Hence, integration strategies for energy-efficiency in the building and transport sector are of increasing importance. The present study analyses key parameters leading to Electric Vehicle adoption, utilizing background data from countries where Electric Vehicles have already been introduced and adopted in everyday living, and presents a case study of an energy management scheme in Greece, where the penetration rate is still low. Based on the above, an optimization algorithm is proposed, where buildings, photovoltaic plants, storage systems, and Electric Vehicles (utilization of Vehicle to Grid technology) can efficiently meet the energy requirements and peak-hour energy demand, in both economic and sustainability terms. The study proposes a hybrid approach, based on Analytic Hierarchy Process methodology and Genetic algorithms, aiming to foster the diffusion of the Vehicle to Grid concept to support building energy demand.

This work studies the optimization of scheduling of chassis and container movements at the operational level for individual trucking companies when Chassis Processing Facilities (CPFs) are available for use in the vicinity of a container port within a major metropolitan area. A multi-objective optimization problem is formulated in which the weighted combination of the total travel time for the schedules of all vehicles in the company fleet and the maximum work span across all vehicle drivers during the day is minimized. Time-varying dynamic models for the movements of chassis and containers are developed to be used in the optimization process. The optimal solution is obtained through a genetic algorithm, and the effectiveness of the developed methodology is evaluated through a case study which focuses on the Los Angeles/Long Beach port complex. The case study uses a trucking company located in the Los Angeles region, which can utilize three candidate CPFs for exchange of chassis. The company assigns container movement tasks to its fleet of trucks, with warehouse locations spread across the region. In the simulation scenarios developed for the case study, the use of CPFs at the trucking company level, can provide improvements up to 13% (depending upon the specific scenario) over the cases of not using any CPFs. It was found in this work that for typical cases where the number of jobs is much larger than the number of vehicles in the company fleet, the greatest benefit from CPF use would be in the cases where there are some significant job to job differences with respect to chassis usage.View the NCST Project Webpage

Effective resource management constitutes a cornerstone of construction project success. This is a challenging combinatorial optimization problem with multiple and contradictory objectives whose complexity rises disproportionally with the project size and special characteristics (e.g., repetitive projects). While relevant work exists, there is still a need for thorough modeling of the practical implications of non-optimal decisions. This study proposes a multi-objective model, which can realistically represent the actual loss from not meeting the resource utilization priorities and constraints of a given project, including parameters that assess the cost of exceeding the daily resource availability, the cost of moving resources in and out of the worksite, and the cost of delaying the project completion. Optimization is performed using Genetic Algorithms, with problem setups organized in a spreadsheet format for enhanced readability and the solving is conducted via commercial software. A case study consisting of 16 repetitive projects, totaling 160 activities, tested under different objective and constraint scenarios is used to evaluate the algorithm effectiveness in different project management priorities. The main study conclusions emphasize the importance of conducting multiple analyses for effective decision-making, the increasing necessity for formal optimization as a project's size and complexity increase, and the significant support that formal optimization provides in customizing resource allocation decisions in construction projects. [ABSTRACT FROM AUTHOR]

Hub-and-Spoke (H&S) network modeling is a form of transport topology optimization in which network joins are connected through intermediate hub nodes. The Short Sea Shipping (SSS) problem aims to efficiently disperse passenger flows involving multiple vessel routes and intermediary hubs through which passengers are transferred to their final destination. The problem contains elements of the Hub-and-Spoke and Travelling Salesman, with different levels of passenger flows among islands, making it more demanding than the typical H&S one, as the hub selection within nodes and the shortest routes among islands are internal optimization goals. This work introduces a multi-objective tri-level optimization algorithm for the General Network of Short Sea Shipping (GNSSS) problem to reduce travel distances and transportation costs while improving travel quality and user satisfaction, mainly by minimizing passenger hours spent on board. The analysis is performed at three levels of decisions: (a) the hub node assignment, (b) the island-to-line assignment, and (c) the island service sequence within each line. Due to the magnitude and complexity of the problem, a genetic algorithm is employed for the implementation. The algorithm performance has been tested and evaluated through several real and simulated case studies of different sizes and operational scenarios. The results indicate that the algorithm provides rational solutions in accordance with the desired sub-objectives. The multi-objective consideration leads to solutions that are quite scattered in the solution space, indicating the necessity of employing formal optimization methods. Typical Pareto diagrams present non-dominated solutions varying at a range of 30 percent in terms of the total distance traveled and more than 50 percent in relation to the cumulative passenger hours. Evaluation results further indicate satisfactory algorithm performance in terms of result stability (repeatability) and computational time requirements. In conclusion, the work provides a tool for assisting network operation and transport planning decisions by shipping companies in the directions of cost reduction and traveler service upgrade. In addition, the model can be adapted to other applications in transportation and in the supply chain.

The incorporation of electric vehicles into the transportation system is imperative in order to mitigate the environmental impact of fossil fuel use. This requires establishing methods for deploying the charging infrastructure in an optimal way. In this paper, an optimization model is developed to identify both the number of stations to be deployed and their respective locations that minimize the total cost by utilizing Genetic Algorithms. This is implemented by combining these components into a linear objective function aiming to minimize the overall cost of deploying the charging network and maximize service quality to users by minimizing the average travel distance between demand spots and stations. Several numerical and practical considerations have been analyzed to provide an in-depth study and a deeper understanding of the model’s capabilities. The optimization is done through commercial software that is appropriately parametrized to adjust to the specific problem. The model is simple yet effective in solving a variety of problem structures, optimization goals and constraints. Further, the quality of the solution seems to be marginally affected by the shape and size of the problem area, as well as the number of demand spots, and this may be considered one of the strengths of the algorithm. The model responds expectedly to variations in the charging demand levels and can effectively run at different levels of grid discretization.

The twin ports of Long Beach (POLB) and Los Angeles (POLA), consisting of fourteen individually gated terminals, combine to create the largest container port complex in the US. In 2015, the combined ports handled 15.4 million 20-foot equivalent units (TEUs), a 56% increase since 2000, expected to grow higher in the future. This large number of containers and the associated trips to/from the ports, result in traffic congestion, noise pollution, and greenhouse gas emissions in the vicinity of the ports. The current project studies the concept of “Centralized Processing of Chassis,” and the possibility of using it to mitigate some of these problems. This concept revolves around an off-dock terminal (or several off-dock terminals), referred to as Chassis Processing Facilities (CPFs). A CPF is located close to the port, where trucks will go to exchange chassis, thereby reducing traffic at the marine terminals, resulting in reduced travel times and reduced congestion. The current project develops the required analytical framework for modeling and optimization of the CPF use. The developed analytical model is applied to a case study in the Los Angeles/Long Beach port area. The case study identifies sixteen locations in the vicinity of the ports that can be potentially used as CPFs, and examines several scenarios of container pickup/drop-off transactions. The study presents comparisons between the case when chassis exchanges occur at the CPFs versus the case when chassis exchanges occur at the marine terminals. It is shown that a reduction of up to 20% in total travel time can be achieved when using the CPFs, as compared to using only the marine terminals. The study also shows that using up to three of the potential sixteen CPFs provides significant improvements to total travel time, but using more than three CPFs will have insignificant additional benefits. Moreover, a discrete event simulation model is developed and used for detailed simulation scenarios, as well as for examining and evaluating the performance of heuristic methods.View the NCST Project Webpage

The description of the functionality of a smart grid’s architectural concept, analyzing different Smart Grid (SG) scenarios without disrupting the smooth operation of the individual processes, is a major challenge. The field of smart energy grids has been increasing in complexity since there are many stakeholder entities with diverse roles. Electric Vehicles (EVs) can transform the stress on the energy grid into an opportunity to act as a flexible asset. Smart charging through an external control system can have benefits for the energy sector, both in grid management and environmental terms. A suitable model for analyzing and visualizing smart grid use cases in a technology-neutral manner is required. This paper presents a flexible architecture for the potential implementation of electromobility as a distributed storage asset for the grid’s capacity optimization by applying the Use Case and Smart Grid Architecture Model (SGAM) methodologies. The use case scenario of booking a charge session through a mobile application, as part of the TwinERGY Horizon 2020 project, is deployed to structure the SGAM framework layers and investigate the applicability of the SGAM framework in the integration of electromobility as a distributed storage asset into electricity grids with the objective of enhanced flexibility and decarbonization.

Risk management has become an important concern in the light of current developments in the home energy management sector as well as within the broader considerations regarding the building sector’s energy production and consumption paradigm. The current multi-parameter energy ecosystem structure raises a number of new challenges that require a reliable and robust risk management framework to assist in building management decision making. This paper presents a multi asset risk assessment algorithm, which is part of a risk management application developed for residential buildings within the framework of energy communities and digital energy markets. It describes the logic, principles, and operation of the algorithm, as well as the functionalities related to risk analysis and result visualization. This underpins the necessary means to monitor elements of a home energy system as well as tools for risk prevention and mitigation. The proposed application provides accurate, detailed, and easy to use information to assist decision makers and stakeholders in the context of smart home energy management systems.

Newly poured concrete opposing hot and windy conditions is considerably susceptible to plastic shrinkage cracking. Crack-free concrete structures are essential in ensuring high level of durability and functionality as cracks allow harmful instances or water to penetrate in the concrete resulting in structural damages, e.g. reinforcement corrosion or pressure application on the crack sides due to water freezing effect. Among other factors influencing plastic shrinkage, an important one is the concrete surface humidity evaporation rate. The evaporation rate is currently calculated in practice by using a quite complex Nomograph, a process rather tedious, time consuming and prone to inaccuracies. In response to such limitations, three analytical models for estimating the evaporation rate are developed and evaluated in this paper on the basis of the ACI 305R-10 Nomograph for “Hot Weather Concreting”. In this direction, several methods and techniques are employed including curve fitting via Genetic Algorithm optimization and Artificial Neural Networks techniques. The models are developed and tested upon datasets from two different countries and compared to the results of a previous similar study. The outcomes of this study indicate that such models can effectively re-develop the Nomograph output and estimate the concrete evaporation rate with high accuracy compared to typical curve-fitting statistical models or models from the literature. Among the proposed methods, the optimization via Genetic Algorithms, individually applied at each estimation process step, provides the best fitting result.

Seismic risk assessment in bridge networks is of great interest in reducing earthquake adverse effects in structures and the society. Among existing methods for risk and loss assessment, Hazus methodology is widely accepted and used. The method includes several steps and calculations, being thus rather complex and time-consuming, especially if several bridges with diverse characteristics are to be examined. The present study investigates the potential of using soft computing techniques for determining the seismic hazard and expected losses as an alternative to the Hazus methodology. In particular, a number of representative datasets have been developed on the basis of Hazus and a number of methods have been formulated and tested regarding their capacity to effectively simulate the process. The methods that are examined range from regression curve-fitting to artificial neural networks of different size and characteristics. Alternative statistical tests have been used to evaluate the developed model capabilities. Evaluation results indicate that appropriately designed ANN methods can attain high accuracy and stable results among different datasets (training, testing, and validation). The analysis indicates the potential of using similar techniques to simulate seismic risk assessment based on actual data from earthquake events that have taken place in the past. [ABSTRACT FROM AUTHOR]

The time-cost trade-off problem pertains to the assessment of the best method of activity construction so that a project is completed within a given deadline and at least cost. Although several evolutionary-type of algorithms have been reported over the last two decades to solve this NP-hard combinatorial problem, there are not many comparative studies independently evaluating several methods. Such studies can provide support to project managers regarding the selection of the appropriate method. The objective of this work is to comparatively evaluate the performance potential of a number of evolutionary algorithms, each one with its own variations, for the time-cost trade-off problem. The evaluation is based on two measures of effectiveness, the solution quality (accuracy) and the processing time to obtain the solution. The solution is sought via a general purpose commercial optimization software without much interference in algorithm parameter setting and fine-tuning in an attempt to follow the anticipated project manager approach. The investigation has been based on case studies from the literature with varying project size and characteristics. Results indicate that certain structures of genetic algorithms, particle swarm optimization, and differential evolution method present the best performance.