Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship betwe... more 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 compared the BN classifier model’s performance accuracy to that of the Naive Bayes (NB) and decision tree (DT) models to determine the effect of considering possible correlations between cost overrun risks on prediction accuracy. Moreover, the most critical risks and their relationships are identifi...
Journal of Industrial and Systems Engineering, 2016
In the context of public transportation system, improving the service quality and robustness thro... more In the context of public transportation system, improving the service quality and robustness through minimizing the average passengers waiting time is a real challenge. This study provides robust stochastic programming models for train timetabling problem in urban rail transit systems. The objective is minimization of the weighted summation of the expected cost of passenger waiting time, its variance and the penalty function including the capacity violation due to overcrowding. In the proposed formulations, the dynamic and uncertain travel demand is represented by the scenario-based multi-period arrival rates of passenger. Two versions of the robust stochastic programming models are developed and a comparative analysis is conducted to testify the tractability of the models. The effectiveness of the proposed stochastic programming model was demonstrated through the application to Tehran underground urban railway. The outcomes show the reductions in expected passenger waiting time of ...
It is scientifically challenging to determine the inventory level all through the supply chain in... more It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved by regular exact models such as mathematical ones due to their complexity. The present paper is aimed at simulating lean multi-product supply chain system as well as optimization of the objectives of supply chain. Variables of the simulation model include two types of Kanbans namely withdrawal, and production to determine the inventory level, and batch size of delivery parts for each stage of supply chain. So, in this paper simulation model was developed for supply chains, taking into consideration the different production scenarios and were modeled and compared. A production scenario is adopted for each level of the chain in order to achieve the objectives. The use of meta-heuristic techniqu...
Sea transportation has been progressively increasing over the past few decades. Maritime transpor... more Sea transportation has been progressively increasing over the past few decades. Maritime transport has been mentioned as the largest cargo for goods in transit and currently it is pertinent for supporting international trade. Currently, nearly 95% of freight in the world is transported by cargo vessels. With this increasing volume of marine cargo, the efficient transporting and handling of vessels and containers has been a more critical issue. Mandatory Procedures for Vessel Operations from entrance to departure at a container terminal have been simulated. The cyclic variation in tide levels is considered in the simulation model as an important cause for Vessel waiting times. Applying these new constraints makes the simulation model more realistic in Port Operations. Greedy heuristic for berth allocation is applied in this paper by which strategies are evaluated in proposed simulation model. As a real- world case, simulation model has been applied on Rajaee Port. The simulation resu...
Public transport is amongst critical infrastructures in modern cities, especially megacities, hom... more 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. Driver’s expertise and components’ quality are the most important factors for brake system reliability. This study aims to implement a hybrid machine learning and optimization model to minimize the total investment and reliability-related costs in a bus rapid transit (BRT) system. A regression analysis ...
International Journal of System Dynamics Applications, 2017
The process of privatization of the electricity market involves the serious needs of the industry... more The process of privatization of the electricity market involves the serious needs of the industry in order to increase productivity, attract private investment and partnerships. If current trends continue, the total investment needed for the industry is not to be able to be supplied from domestic sources. The participation of the private sector in capacity expansion is essential. In this study, a system dynamic approach is presented to study the pricing strategies in electricity market and also to analyze the rate of load consumption in Iranian electricity market. The proposed model enables the decision-maker to assess the effect of prices on the average rate of energy consumption as well as to simulate and predict the behavior of the model with/without taking into account subsidies to the electricity sales rate. The results show that capacity under construction is of high amplitude fluctuations. Moreover, the energy consumption increases with increasing capacity growth.
Cost overrun risks are declared to be dynamic and interdependent. Ignoring the relationship betwe... more 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 compared the BN classifier model’s performance accuracy to that of the Naive Bayes (NB) and decision tree (DT) models to determine the effect of considering possible correlations between cost overrun risks on prediction accuracy. Moreover, the most critical risks and their relationships are identifi...
Journal of Industrial and Systems Engineering, 2016
In the context of public transportation system, improving the service quality and robustness thro... more In the context of public transportation system, improving the service quality and robustness through minimizing the average passengers waiting time is a real challenge. This study provides robust stochastic programming models for train timetabling problem in urban rail transit systems. The objective is minimization of the weighted summation of the expected cost of passenger waiting time, its variance and the penalty function including the capacity violation due to overcrowding. In the proposed formulations, the dynamic and uncertain travel demand is represented by the scenario-based multi-period arrival rates of passenger. Two versions of the robust stochastic programming models are developed and a comparative analysis is conducted to testify the tractability of the models. The effectiveness of the proposed stochastic programming model was demonstrated through the application to Tehran underground urban railway. The outcomes show the reductions in expected passenger waiting time of ...
It is scientifically challenging to determine the inventory level all through the supply chain in... more It is scientifically challenging to determine the inventory level all through the supply chain in such a way that the desired objectives such as effectiveness and responsiveness of the supply chain system can be obtained. Simulation is a means for solving various problems which cannot be solved by regular exact models such as mathematical ones due to their complexity. The present paper is aimed at simulating lean multi-product supply chain system as well as optimization of the objectives of supply chain. Variables of the simulation model include two types of Kanbans namely withdrawal, and production to determine the inventory level, and batch size of delivery parts for each stage of supply chain. So, in this paper simulation model was developed for supply chains, taking into consideration the different production scenarios and were modeled and compared. A production scenario is adopted for each level of the chain in order to achieve the objectives. The use of meta-heuristic techniqu...
Sea transportation has been progressively increasing over the past few decades. Maritime transpor... more Sea transportation has been progressively increasing over the past few decades. Maritime transport has been mentioned as the largest cargo for goods in transit and currently it is pertinent for supporting international trade. Currently, nearly 95% of freight in the world is transported by cargo vessels. With this increasing volume of marine cargo, the efficient transporting and handling of vessels and containers has been a more critical issue. Mandatory Procedures for Vessel Operations from entrance to departure at a container terminal have been simulated. The cyclic variation in tide levels is considered in the simulation model as an important cause for Vessel waiting times. Applying these new constraints makes the simulation model more realistic in Port Operations. Greedy heuristic for berth allocation is applied in this paper by which strategies are evaluated in proposed simulation model. As a real- world case, simulation model has been applied on Rajaee Port. The simulation resu...
Public transport is amongst critical infrastructures in modern cities, especially megacities, hom... more 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. Driver’s expertise and components’ quality are the most important factors for brake system reliability. This study aims to implement a hybrid machine learning and optimization model to minimize the total investment and reliability-related costs in a bus rapid transit (BRT) system. A regression analysis ...
International Journal of System Dynamics Applications, 2017
The process of privatization of the electricity market involves the serious needs of the industry... more The process of privatization of the electricity market involves the serious needs of the industry in order to increase productivity, attract private investment and partnerships. If current trends continue, the total investment needed for the industry is not to be able to be supplied from domestic sources. The participation of the private sector in capacity expansion is essential. In this study, a system dynamic approach is presented to study the pricing strategies in electricity market and also to analyze the rate of load consumption in Iranian electricity market. The proposed model enables the decision-maker to assess the effect of prices on the average rate of energy consumption as well as to simulate and predict the behavior of the model with/without taking into account subsidies to the electricity sales rate. The results show that capacity under construction is of high amplitude fluctuations. Moreover, the energy consumption increases with increasing capacity growth.
Advances in Modelling and Data-Driven Optimisation of Urban Transport and Logistics
Dear colleag... more Advances in Modelling and Data-Driven Optimisation of Urban Transport and Logistics
Dear colleagues, We invite you to submit high-quality research papers to the special issue of "Advances in Modelling and Data-Driven Optimisation of Urban Transport and Logistics" in the Journal of Advanced Transportation. https://lnkd.in/dswsPx6
This Special Issue aims to lay the groundwork for state-of-the-art urban transport with simulation, optimisation, and data analytics in the fields of big data, smart cities, and smart logistics. We support the submission of outstanding research papers on new applications and methods for incorporating emerging technologies into data-driven optimisation, big data analytics, large-scale traffic simulation, and real-world case studies.
Submission deadline: 05 Mar 2021 Publishing date: 01 Jul 2021
Uploads
Dear colleagues,
We invite you to submit high-quality research papers to the special issue of "Advances in Modelling and Data-Driven Optimisation of Urban Transport and Logistics" in the Journal of Advanced Transportation.
https://lnkd.in/dswsPx6
This Special Issue aims to lay the groundwork for state-of-the-art urban transport with simulation, optimisation, and data analytics in the fields of big data, smart cities, and smart logistics. We support the submission of outstanding research papers on new applications and methods for incorporating emerging technologies into data-driven optimisation, big data analytics, large-scale traffic simulation, and real-world case studies.
Submission deadline: 05 Mar 2021
Publishing date: 01 Jul 2021
Guest Editors:
Ehsan Nikbakhsh, Tarbiat Modares University, nikbakhsh@modares.ac.ir
Saeid Saidi, University of Calgary, Calgary, ssaidi@ucalgary.ca
Majid Eskandarpour, IESEG School of Management, m.eskandarpour@ieseg.fr
Please contact Lead editor hassannayebi@sharif.edu with any questions.
Thanks.