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Estimation of highway capacity under environmental constraints vs. conventional traffic flow criteria: A case study of Tehran
, Article Journal of Traffic and Transportation Engineering (English Edition) ; 2021 ; 20957564 (ISSN) ; Safari, F ; Hassannayebi, E ; Sharif University of Technology
Chang'an University
2021
Abstract
In this paper, the concept of environmental capacity is developed to identify a convenient maximum traffic volume which will not reduce the life quality of residents. The presented method investigates the idea of traffic capacity under environmental constraints by calculating the maximum number of vehicles allowed on roads based on acceptable levels of air and noise pollutants. In this study, the permissible noise pollution level and permissible levels of CO and NOx pollution are considered for determining environmental capacity. Results show the significant difference between environmental capacity and functional traffic capacity, introduced by the highway capacity manual (HCM) as a...
Socio-Economical Analysis of a Green Reverse Logistics Network under Uncertainty: A Case Study of Hospital Constructions
, Article Urban Science ; Volume 8, Issue 4 , 2024 ; 24138851 (ISSN) ; Saffarian, A ; Hassannayebi, E ; Sharif University of Technology
2024
Abstract
This study addresses the critical issue of managing construction and demolition waste in urban environments. Effective waste management is not only essential for minimizing costs but also for enhancing sustainability and reducing environmental impact. In this context, the research introduces a green reverse logistics model designed for C&D waste management, integrating both sustainability considerations and current regulatory frameworks, such as LEED. A key innovation of this model is the incorporation of electric vehicles for waste collection, compared to traditional diesel vehicles, as part of the logistical process, as carbon emission is a significant concern. By evaluating the...
Transition to low-carbon vehicle market: characterization, system dynamics modeling, and forecasting
, Article Energies ; Volume 17, Issue 14 , 2024 ; 19961073 (ISSN) ; Moeini-Aghtaie, M ; Hassannayebi, E ; Hewitt, E ; Sharif University of Technology
2024
Abstract
Rapid growth in vehicle ownership in the developing world and the evolution of transportation technologies have spurred a number of new challenges for policymakers. To address these challenges, this study develops a system dynamics (SD) model to project the future composition of Iran’s vehicle fleet, and to forecast fuel consumption and CO2 emissions through 2040. The model facilitates the exploration of system behaviors and the formulation of effective policies by equipping decision-makers with predictive insights. Under various scenarios, this study simulates the penetration of five distinct vehicle types, highlighting that an increase in fuel prices does not constitute a sustainable...
Green inventory management in a multi-product, multi-vendor post-disaster construction supply chain
, Article Environment, Development and Sustainability ; 2023 ; 1387585X (ISSN) ; Alipour Vaezi, M ; Hassannayebi, E ; Sharif University of Technology
Springer Science and Business Media B.V
2023
Abstract
In the outcome of natural disasters, different factors, i.e., uncertain lead time and material quality, incur an additional cost, downgrading the supply chains’ efficiency. The optimal inventory decisions are challenging due to the complexity arising from the multi-product, multi-vendor consideration, uncertainty of supplies, and conflicting objectives in sustainable construction supply chains. To fill the existing research gaps, this research presents an operation research modeling framework to minimize the amount of carbon emitted by suppliers’ vehicles as well as ordering and holding costs in a post-disaster construction supply chain under the epistemic uncertainty of quality and cost...
Bi-objective optimization approaches to many-to-many hub location routing with distance balancing and hard time window
, Article Neural Computing and Applications ; Volume 32, Issue 17 , 2020 , Pages 13267-13288 ; Akbari Jokar, M. R ; Hassannayebi, E ; Sharif University of Technology
Springer
2020
Abstract
This study addresses a many-to-many hub location-routing problem where the best-found locations of hubs and the best-found tours for each hub are determined with simultaneous pickup and delivery within the hard time window. To find practical solutions, the hubs and transportation fleet have constrained capacity, in which every node can be serviced by multiple allocations with the hard time window and limited tour length. First, a bi-objective optimization model is proposed to balance travel costs among different routes and to minimize the total sum of fixed costs of locating hubs, the costs of handling, traveling, assigning, and transportation costs. The problem is then solved using an...
Optimization and modeling of Zn2SnO4 sensitivity as gas sensor for detection benzene in the air by using the response surface methodology
, Article Journal of Saudi Chemical Society ; Volume 25, Issue 12 , 2021 ; 13196103 (ISSN) ; Tohidi, G ; Movahedi, F ; Hassannayebi, E ; Sharif University of Technology
Elsevier B.V
2021
Abstract
In this paper, the performance of the benzene gas detection sensor in the air is optimized by an experimental design method. So in this work, Nanostructured thin films of ZnO and Zn2SnO4 were prepared in wurtzite form via a facile atmospheric pressure chemical vapor deposition (CVD) method, using metallic zinc and tin precursors. Characterization of the gas sensor was performed by using Powder X-ray diffraction (PXRD), scanning electron microscopy (SEM) and surface area analysis (using BET method). The results show that Zn2SnO4 nanowire network exhibited good sensitivity at 299 °C temperature to low concentrations (100 ppb) of Benzene which can be potentially used as a resistive gas sensor....
Cost overrun risk assessment and prediction in construction projects: a bayesian network classifier approach
, Article Buildings ; Volume 12, Issue 10 , 2022 ; 20755309 (ISSN) ; Ansari, R ; Hassannayebi, E ; Jeong, J ; Sharif University of Technology
MDPI
2022
Abstract
Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship between cost overrun risks during the risk assessment process is one of the primary reasons construction projects go over budget. Conversely, recent studies have failed to account for potential interrelationships between risk factors in their machine learning (ML) models. Additionally, the presented ML models are not interpretable. Thus, this study contributes to the entire ML process using a Bayesian network (BN) classifier model by considering the possible interactions between predictors, which are cost overrun risks, to predict cost overrun and assess cost overrun risks. Furthermore, this study...
Diagnostic clinical decision support based on deep learning and knowledge-based systems for psoriasis: From diagnosis to treatment options
, Article Computers and Industrial Engineering ; Volume 187 , 2024 ; 03608352 (ISSN) ; Ijadi Maghsoodi, A ; Hassannayebi, E ; Aickelin, U ; Sharif University of Technology
2024
Abstract
Psoriasis is an acute immuno-dermatological disease, affecting people of all ages, which significantly decreases quality of life. While the standard approach to identification and diagnosis of psoriasis is based on dermatologist decisions, various Deep Learning (DL) methods have been utilized to create Computer-Aided Diagnosis (CAD) systems to detect and classify psoriasis cases. In response to the knowledge gap of an existing practical and functional DL-based solution to psoriasis diagnosis, this study proposed an ensemble Convolutional Neural Network (CNN) model using Residual Network 50 Version 2 (ResNet50V2), ResNet101V2, and ResNet152V2 networks to create a CAD system for detecting and...
Smart transportation behavior through the COVID-19 pandemic: A ride-hailing system in Iran
, Article Sustainability (Switzerland) ; Volume 15, Issue 5 , 2023 ; 20711050 (ISSN) ; Ramezani, M ; Khazaei, M ; Roohparvar, V ; Hassannayebi, E ; Sharif University of Technology
MDPI
2023
Abstract
During the COVID-19 pandemic, significant changes occurred in customer behavior, especially in traffic and urban transmission systems. In this context, there is a need for more scientific research and managerial approaches to develop behavior-based smart transportation solutions to deal with recent changes in customers, drivers, and traffic behaviors, including the volume of traffic and traffic routes. This research has tried to find a comprehensive view of novel travel behavior in different routes using a new social network analysis method. Our research is rooted in graph theory/network analysis and application of centrality concepts in social network analysis, particularly in the...
A hybrid machine learning and optimization model to minimize the total cost of BRT brake components
, Article Journal of Advanced Transportation ; Volume 2021 , 2021 ; 01976729 (ISSN) ; Shams Gharneh, N ; Arjomandi Nezhad, A ; Hassannayebi, E ; Sharif University of Technology
Hindawi Limited
2021
Abstract
Public transport is amongst critical infrastructures in modern cities, especially megacities, home to millions of people. The reliability of these systems is highly crucial for both citizens and service providers. If service providers overlook system reliability, a considerable amount of expenses will be wasted. Several factors such as vehicle failure, accident, lack of budget weather factors, and traffic congestion cause unreliability, among which vehicle failure plays a prominent role. The brake system is the most vulnerable and vital component of a public transportation bus. Brake reliability depends on driver's expertise, component quality, passenger loading, line situation, etc....
Simulation-optimization framework for train rescheduling in rapid rail transit
, Article Transportmetrica B ; 2020 ; Sajedinejad, A ; Kardannia, A ; Shakibayifar, M ; Jafari, H ; Mansouri, E ; Sharif University of Technology
Taylor and Francis Ltd
2020
Abstract
One of the primary challenges of re-planning in high-speed urban railways is the randomness of disruptive events. In this study, an integrated disturbance recovery model presented in which short-turn and stop-skip service operations are optimized together to minimize the average of passengers’ waiting times. This study develops a discrete-event simulation model that employs a variable neighborhood search algorithm to maintain the service level under infrastructure elements’ unavailability. Due to the unpredictable nature of the incidents, the uncertainty associated with obstruction duration is experimentally analyzed through probabilistic scenarios. The computational experiments are...
Simulation-optimization framework for train rescheduling in rapid rail transit
, Article Transportmetrica B ; Volume 9, Issue 1 , 2021 , Pages 343-375 ; 21680566 (ISSN) ; Sajedinejad, A ; Kardannia, A ; Shakibayifar, M ; Jafari, H ; Mansouri, E ; Sharif University of Technology
Taylor and Francis Ltd
2021
Abstract
One of the primary challenges of re-planning in high-speed urban railways is the randomness of disruptive events. In this study, an integrated disturbance recovery model presented in which short-turn and stop-skip service operations are optimized together to minimize the average of passengers’ waiting times. This study develops a discrete-event simulation model that employs a variable neighborhood search algorithm to maintain the service level under infrastructure elements’ unavailability. Due to the unpredictable nature of the incidents, the uncertainty associated with obstruction duration is experimentally analyzed through probabilistic scenarios. The computational experiments are...
An efficient heuristic method for joint optimization of train scheduling and stop planning on double-track railway systems
, Article INFOR ; Volume 58, Issue 4 , 2020 , Pages 652-679 ; Ramezani, S ; Farahani, N. V ; Hassannayebi, E ; Abolmaali, S ; Shakibayifar, M ; Sharif University of Technology
University of Toronto Press
2020
Abstract
In this study, a new mathematical programming approach for solving the joint timetabling and train stop planning problem in a railway line with double-track segments is proposed. This research aims to design an optimized train timetable subject to the station- capacity and time-dependent dwell time constraints. The objective function is to maximize the railway infrastructure capacity by minimizing the schedule makespan. The problem is formulated as a particular case of blocking permutation flexible flow shop scheduling model with dynamic time window constraints. Due to the combinatorial nature of the problem, heuristic algorithm and bound tightening methods are proposed that generate...
A Train Sequencing and Stop Scheduling Model inDouble Track Railway Lines by hHybrid GRASP-VNS Meta-Heuristic
,
M.Sc. Thesis
Sharif University of Technology
;
Kianfar, Fereydoon
(Supervisor)
Abstract
The train scheduling problem is one of the most important scheduling problems in the transportation systems. The goal of train scheduling problem is generating a feasible timetable which consists of train departure times and determining the best station to stop. Optimizing the railway capacity is one of the most important goals in train scheduling phase. The sequence of dispatching trains and stopping schedule are the main factors that can affect railway capacity on double track lines. In this thesis, a double-track train sequencing problem is studied in order to maximize the railway capacity, subject to a set of operational requirements. This research proposes a flexible flow shop...
A Process Mining Approach to Analyze Customer Journeys to Improve Customer Experience
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
With the growth of the number of online service providers and the need to innovate in these services, in this study, the processes and the journeys taken by visitors of a website that provides employment services and employment insurance has been analyzed. In this research, process mining techniques and predictive process monitoring were implemented. With the use of a supervised and unsupervised learning algorithm, it attempted to identify the customer journeys' output and the existing patterns that lead to the complaint. In the first step, the website event log is extracted. Afterward, by using frequency-based encoding methods, the journeys traveled by users were clustered based on the...
Predictive Business Process Monitoring Using Machine Learning Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
In order to survive in today's business world, which is changing at a very fast pace, organizations can detect deviations even before they occur, quickly and with a high percentage of confidence, by analyzing their processes, in order to prevent disruptions in the processes. by monitoring the information systems that automatically execute business processes, it is possible to ensure the correct implementation of the existing processes. For this purpose, various techniques for monitoring business processes have been presented so that managers have a comprehensive and real view of how implement processes and be able to identify possible deviations in the future and try to fix them because the...
Data-Driven Prediction for Monitoring Business Process Pperformances Based on Classification Algorithms
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
In recent years, several studies have been conducted in the field of data mining techniques in the field of process mining with the aim of improving the performance of organizations. Predictive process monitoring is a data-driven approach that helps business managers to improve the status and conditions of their organization. In this approach, the event log, which includes a set of completed examples of a process, is received as input, and machine learning methods are used to predict the outcome and results of the organization's processes before the process is completed. This prediction can include the prediction of the final result, the next event, the time remaining until the completion of...
Green Supplier Selection under Supply Risks with Respect to Supplier’s Financial Performance using Integrated Fuzzy MCDM Methods
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
The supplier selection problem is considered as one of the most strategic and critical issues for any organization. This issue is mainly relevant to traditional and manufacturing businesses. However, if we examine its relationship with the emerging concept of Vendor Acquisition, which has become increasingly significant in modern businesses and startups, the importance of supplier selection becomes even greater. Today, choosing the right supplier or vendor determines the level of success organizations achieve in any new project, and the performance of vendors plays a crucial role in shaping and directing these projects. This study aims to present a comprehensive framework for supplier...
A simulation optimization approach to inventory optimization in supply chain networks
, Article IFIP Advances in Information and Communication Technology ; Volume 691 AICT , 2023 , Pages 374-384 ; 18684238 (ISSN); 978-303143669-7 (ISBN) ; Eshghi, A ; Basirati, M ; Hassannayebi, E ; Alfnes E ; Romsdal A ; Strandhagen J. O ; von Cieminski G ; Romero D ; Sharif University of Technology
Springer Science and Business Media Deutschland GmbH
2023
Abstract
This study presents a simulation-optimization approach to inventory optimization in supply chain networks. The aim of this study is to obtain an optimal inventory policy at the distribution center for a three-level supply chain. The supply chain considered consists of a manufacturer, a distribution center, and a retailer, with a product flowing between the three members of the chain. In this model, customers visit the retail store and request to buy a product. Based on the retail store’s inventory, they receive the desired product. If the demand exceeds the inventory, the excess amount is considered a backlog in the system. At the beginning of each day, the retail store and the distribution...
Predictive Process Monitoring Based on Optimized Deep Learning Methods
, M.Sc. Thesis Sharif University of Technology ; Hassannayebi, Erfan (Supervisor)
Abstract
Business processes are an essential part of every business as they provide insights on how to optimize and make them more efficient. Predictive Business Process Monitoring has garnered significant attention in recent years due to its capability to forecast process outcomes and predict the next activity within an ongoing process. In the last few years, there have been works that focused on deep learning and its applications in predicting the next activity. Some research used Long Term Short Memory, while others used Convolutional Neural Networks. However, long term short term memory models have the constraint of relatively slow training, while Convolutional Neural Networks are fast but may...