Hamid R. Sayarshad | Cornell University (original) (raw)

Papers by Hamid R. Sayarshad

Electric Power Systems Research

Natural disasters, such as earthquakes, can cause significant disruptions in power systems, such ... more Natural disasters, such as earthquakes, can cause significant disruptions in power systems, such as loss of generations and loads. To evaluate the behavior of a generator to remain in synchronism, subjected to the large disturbance of an earthquake, we propose a dynamic generator resiliency model. The proposed approach models the effects of earthquake energy transfer to a generator considering the time-variant behavior of earthquake disturbance. In addition, the proposed model analyzes the transient behavior of generators impacted by an earthquake considering earthquake ground acceleration and generators' dynamic characteristics. Using this model, we can determine synchronism status of impacted generators and transient stability status of a power system in response to an earthquake. The proposed approach is tested using real-world data including the data of a real earthquake occurred on a real power plant. The obtained results illustrate the effectiveness of the proposed model to correctly predict the impact of an earthquake on a power plant, and to determine the effects of earthquake magnitude and generator robustness (in terms of generator inertia and damping torque) on the response of a generator to an earthquake.

Transportation Research Part D: Transport and Environment

With the increasing adoption of electric vehicles (EVs), optimizing charging operations has becom... more With the increasing adoption of electric vehicles (EVs), optimizing charging operations has become imperative to ensure efficient and sustainable mobility. This study proposes an optimization model for the charging and routing of electric vehicles between OD (Origin-Destination) demands. The objective is to develop an efficient and reliable charging plan that ensures the successful completion of trips while considering the limited range and charging requirements of electric vehicles. This paper presents an integrated model for optimizing electric vehicle (EV) charging operations, considering additional factors of setup time, charging time, bidding price estimation, and power availability from three sources: the electricity grid, solar energy, and wind energy. One crucial aspect addressed by the model is the estimation of bidding prices for both day-ahead and intraday electricity markets. The model also considers the total power availability from the electricity grid, solar energy, and wind energy. The alignment of charging operations with the capacity of the grid and prevailing bidding prices is essential. This ensures that the charging process is optimized and can effectively adapt to the available grid capacity and market conditions. The utilization of renewable energies led to a 42% decrease in the electricity storage capacity available in batteries at charging stations. Furthermore, this integration leads to a substantial cost reduction of approximately 69% compared to scenarios where renewable energy is not utilized. Hence, the proposed model can design renewable energy systems based on the required electricity capacity at charging stations. These findings highlight the compelling financial advantages associated with the adoption of sustainable power sources.

Journal of Advanced Transportation, 2016

SummaryArrival processes are important inputs to many transportation system functions, such as ve... more SummaryArrival processes are important inputs to many transportation system functions, such as vehicle prepositioning, taxi dispatch, bus holding strategies, and dynamic pricing. We conduct a comprehensive survey of the literature which shows that many transport systems employ basic homogeneous arrival process models or static nonhomogeneous processes. We conduct an empirical experiment to compare five state of the art arrival process short term prediction models using a common transportation system data set: New York taxi passenger pickups in 2013. Pickup data is split between 672 observations for model estimation and 96 observations for validation. From our experiment, we obtain evidence to support a recent model called FM‐IntGARCH, which is able to combine the benefits of both time series models and discrete count processes. Using a set of seven performance metrics from the literature, FM‐IntGARCH is shown to outperform the offline models—seasonal factor method, piecewise linear ...

Transportation Research Part E: Logistics and Transportation Review, 2017

ABSTRACT Availability of real time "Big data" in recent years has driven an inc... more ABSTRACT Availability of real time "Big data" in recent years has driven an increasing interest in dynamic/real-time/online/sequential network design models. Despite a growing number of studies in stochastic dynamic network optimization, the field remains less well defined and unified than other areas of network optimization. Due to the need for approximation methods like approximate dynamic programming, one of the most significant problems yet to be solved is the lack of adequate benchmarks. Common benchmark policies are inadequate; the value of the perfect information policy does not include random effects while the static and myopic policies are not sensitive to value of anticipation due to network structure. We propose a new class of network-sensitive reference policies using extreme value distributions to estimate theoretically consistent real option values based on sampled sequences. The reference policy is shown to fit known sequence policies well (particularly Weibull), and has sampling consistency for more than 200 samples. It is applied to sequential versions of the discrete network design and timing problem on the Sioux Falls network, the facility location and timing problem on the Simchi-Levi and Berman (1988) network, and Hyytiä et al.'s (2012) dial-a-ride problem.

Networks and Spatial Economics, 2015

Transportation Research Part B: Methodological, 2015

Transportation Research Part B: Methodological, 2014

Power transmission lines are an essential component of the electricity distribution system, respo... more Power transmission lines are an essential component of the electricity distribution system, responsible for transporting electricity from power plants to homes and businesses. Power lines are often used in high-risk areas such as forest areas. A significant danger to power conductors can be posed by wildfires that cause considerable losses to the power grids. This study considers a fire growth model in heterogeneous landscapes concerning topography, weather elements, and fuel variables. We solve an optimal power flow problem that incorporates the cooling and heating process of power conductors. The current-temperature relationship of power conductors is determined based on the fire heating factor and power flows. The resilience of electrical power line outages is also studied that considering the relationship between fire behaviors and the physical locations of power lines. Moreover, the breakdown voltage probability with respect to power line height and wildfire distance from power lines is studied. The results show that the breakdown voltage reduces by up to 14% for an 11 m increase in line height. Similarly, the breakdown voltage decreases by up to 28% for a 15 m increase in wildfire distances from power lines. Lastly, the relationship between the loss of tensile strength, temperature, and time for an aluminum conductor is also evaluated. We observe that the aging failure probability increases over time by up to 7% when the conductors' temperature increases by up to 150 (• C).

Most wildfires started by electrical power are caused by the contact of trees and surface fuels w... more Most wildfires started by electrical power are caused by the contact of trees and surface fuels with power lines. We propose a preignition risk mitigation model to reduce the risk of crown fires and prevent power outages. An optimization formulation model considering power flow under wildfire conditions is proposed. The heating and cooling processes are formulated to find the conductor temperature. We investigate the heat energy of conductors' surface to fuels and trees. We determine the probability of wildfire risk using the non-stationary weather dynamics due to climate change and surface fuel variable. Moreover, we propose a fuel treatment strategy for managing vegetation and surface fuel variable that reduces the risk of wildfires around power transmission lines. Combining all these together, a mathematical model is proposed that minimizes the total expected costs associated with the generator cost and wildfire risks, consistent with conditions for the start and spread of a crown fire.

Looking for parking spaces in crowded areas can be a stressful and time-consuming challenge. With... more Looking for parking spaces in crowded areas can be a stressful and time-consuming challenge. With the arrival of technologies like autonomous vehicles (AVs), users can park far away from their destinations. AVs can move from the city center, which has high congestion, into a parking spot outside the area, and thus have more flexibility in choosing the parking location. This study aims to provide a rent bid for the daytime parking of AVs that considers urban land use to evaluate parking strategies possibly chosen by AV users. We determine an actual parking demand function by integrating individual preferences into a p-median problem that controls user optimality. Combining all these together, a novel dynamic optimization formulation is proposed to design the location of parking facilities for AVs that examines the driver's parking preference including rent bid, waiting time for searching parking lots, and travel costs. A Lagrangian relaxation (LR) algorithm is presented to solve the stochastic parking location problem that integrates a reliability strategy to balance demand and supply for parking spaces. The average gap between the exact and the LR solution is about 6% which is very reasonable. We provide a detailed case study of our model with real data generated from daily household travel in New York City. The results show that the average parking price is reduced by 34%, and the average empty trips by AVs decrease by 22% using the proposed public parking strategy.

After a pandemic, all countries experience a shortage in vaccine supply due to limited vaccine st... more After a pandemic, all countries experience a shortage in vaccine supply due to limited vaccine stocks and production capacity globally. One particular problem is that it is hard to predict demands for vaccines during the global crisis. On the other hand, vaccines are usually made and packaged in different places, raising logistical issues and concerns that can further delay distribution. In this paper, we propose an optimization formulation model to link infectious disease dynamics and supply chain networks considering a one-to-one relationship between demand and supply for vaccines. We focus on designing a vaccine coordination system using government subsidy that considers the equilibrium behaviors of manufacturers under an actual demand for the vaccine. This study evaluates vaccine manufacturers and government behaviors that help the vaccine market to reach the socially optimal. Different decisions, such as vaccine demands and vaccine production and distribution are investigated. A study of the monkeypox pandemic in the U.S. is performed to validate our model and its results. The obtained results from testing the proposed system problem revealed that the vaccine coverage increased by up to 35%, while the unmet demand reduced by up to 60%, in comparison to when vaccine manufacturers act individually.

A global crisis such as a pandemic causes a decrease in the global trade of medical supplies. One... more A global crisis such as a pandemic causes a decrease in the global trade of medical supplies. One of the most significant issues healthcare workers and people face is the shortage of personal protective equipment (PPE) items. This study constructs the first international trade model to link infectious disease dynamics and global trade networks, considering the important relationship between government preparedness, domestic manufacturers, and consumers. We examine social welfare measures here in the presence of quantity controls and taxes on the global trade flows. An equilibrium coverage among countries is investigated that integrates net government revenue, purchasing cost, transportation cost, and the health cost caused by the shortage of PPE supply. We develop an optimisation model that balances domestic firms and the global trade network to satisfy the total demand for each traded PPE product. The proportional change in value-added on domestic production is also studied by considering the marginal manufacturing costs of a face mask. The results obtained from testing our model show that the average quantity coverage by the global trade networks among four countries decreased by up to 28% using the proposed trade policy. Hence, a large amount of demand is met by relying on domestic production.

When an outbreak starts spreading, policymakers have to make decisions that affect the health of ... more When an outbreak starts spreading, policymakers have to make decisions that affect the health of their citizens and the economy. Some might induce harsh measures, such as a lockdown. Following a long, harsh lockdown, the recession forces policymakers to rethink reopening. To provide an effective strategy, here we propose a control strategy model. Our model assesses the trade-off between social performance and limited medical resources by determining individuals' propensities. The proposed strategy also helps decision-makers to find optimal lockdown and exit strategies for each region. Moreover, the financial loss is minimized. We use the public sentiment information during the pandemic to determine the percentage of individuals with high-risk behavior and the percentage of individuals with low-risk behavior. Hence, we propose an online platform using fear-sentiment information to estimate the personal protective equipment (PPE) burn rate overtime for the entire population. In addition, a study of a COVID-19 dataset for Los Angeles County is performed to validate our model and its results. The total social cost reduces by 18% compared with a control strategy where susceptible individuals are assumed to be homogeneous. We also reduce the total social costs by 26% and 22% compared to other strategies that consider the health-care cost or the social performance cost, respectively.

During a pandemic, various resources, including personal protective equipment (PPE), are required... more During a pandemic, various resources, including personal protective equipment (PPE), are required to protect people and healthcare workers from getting infected. Due to the high demand and limited supply chain, countries experience a shortage in PPE products. This global crisis imposes a decline in the international trade of PPE supplies. In fact, most governments implement a localization strategy motivating domestic manufacturers to pivot their operations to respond to PPE demands. An oligopolistic market cannot reach the socially optimal coverage without government subsidies. On the other hand, the government subsidy pays the proportion of production costs to reach the socially optimal coverage, while the government's budget is limited. Therefore, the government collaborates with manufacturers via procurement contracts to increase the supply of PPE products. We propose the first supply chain model of PPE products that investigates manufacturer costs and government expenditure. We consider how different behavioral aspects of manufacturers and government can self-organize towards a system optimum. Additionally, we integrate the consumer surplus, producer surplus, and societal surplus into the game model to maximize social benefit. A cost-sharing contract under the system optimum between government and manufacturers is designed to increase the production of PPEs and hence, helps in reducing the number of infected individuals. We conducted our computational study on real data generated from the mask usage during the Covid-19 pandemic in Los Angeles (LA) County to respond to the reported PPE shortage. Under the socially optimal strategy, the PPE coverage increases by up to 33%, and the number of infected individuals reduces by up to 30% compared to other strategies.

Transportation Research Part C: Emerging Technologies, 2018

We propose a competitive on-demand mobility model using a multi-server queue system under infinit... more We propose a competitive on-demand mobility model using a multi-server queue system under infinite-horizon look-ahead. The proposed approach includes a novel dynamic optimization algorithm which employs a Markov decision process (MDP) and provides opportunities to revolutionize conventional transit services that are plagued by high cost, low ridership, and general inefficiency, particularly in disadvantaged communities and low-income areas. We use this model to study the implications it has for such services and investigate whether it has a distinct cost advantage and operational improvement. We develop a dynamic pricing scheme that utilizes a balking rule that incorporates socially efficient level and the revenue-maximizing price, and an equilibrium-joining threshold obtained by imposing a toll on the customers who join the system. Results of numerical simulations based on actual New York City taxicab data indicate that a competitive on-demand mobility system supported by the proposed model increases the social welfare by up to 37% on average compared to the single-server queuing system. The study offers a novel design scheme and supporting tools for more effective budget/resource allocation, planning, and operation management of flexible transit systems.

Transportation Research Part E: Logistics and Transportation Review, 2017

A new framework for the design of a dynamic non-myopic inventory and delivery network between sup... more A new framework for the design of a dynamic non-myopic inventory and delivery network between suppliers and retailers under the assumption of elastic demand—one that simultaneously incorporates inventory, routing, and pricing—is proposed. The developed queuing approximation method is based on optimal tolling of queues. We propose a dynamic approach for a supplier who has to deliver products to a number of retailers while maximizing social welfare through dynamic pricing that accounts for customer waiting times, inventory holding, lost-sales costs, and delivery costs. The proposed non-myopic model increases the social welfare by up to 17% compared to the marginal pricing case.

Electric Power Systems Research

Transportation Research Part D: Transport and Environment

Journal of Advanced Transportation, 2016

Transportation Research Part E: Logistics and Transportation Review, 2017

Networks and Spatial Economics, 2015

Transportation Research Part B: Methodological, 2015

Transportation Research Part B: Methodological, 2014

Transportation Research Part C: Emerging Technologies, 2018

Transportation Research Part E: Logistics and Transportation Review, 2017

Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore th... more Non-myopic dial-a-ride problem and other related dynamic vehicle routing problems often ignore the need for non-myopic pricing under the assumption of elastic demand, which leads to an overestimation of the benefits in level of service and resulting inefficiencies. To correct this problem, a new dynamic dial-a-ride policy is introduced, one that features non-myopic pricing based on optimal tolling of queues to fit with the multi-server queueing approximation method proposed by Hyytiä et al (2012) for large-scale systems. By including social optimal pricing, the social welfare of the resulting system outperforms the marginal pricing assumed for previous approaches over a range of test instances. In the examples tested, improvements in social welfare of the non-myopic pricing over the myopic pricing were in the 20% - 31% range. For a given demand function, we can derive the optimal fleet size to maximize social welfare. Sensitivity tests to the optimal price confirm that it leads to an optimal social welfare while the marginal pricing policy does not. A comparison of single passenger taxis to shared-taxis shows that system cost may reduce at the expense of decreased social welfare, which agrees with the results of Jung et al. (2013).

Availability of real time “Big data” in recent years has driven an increasing interest in dynamic... more Availability of real time “Big data” in recent years has driven an increasing interest in dynamic/real-time/online/sequential network design models. Despite a growing number of studies in stochastic dynamic network optimization, the field remains less well defined and unified than other areas of network optimization. Due to the need for approximation methods like approximate dynamic programming, one of the most significant problems yet to be solved is the lack of adequate benchmarks. Common benchmark policies are inadequate; the value of the perfect information policy does not include random effects while the static and myopic policies are not sensitive to value of anticipation due to network structure. We propose a new class of network-sensitive reference policies using extreme value distributions to estimate theoretically consistent real option values based on sampled sequences. The reference policy is shown to fit known sequence policies well (particularly Weibull), and has sampling consistency for more than 200 samples. It is applied to sequential versions of the discrete network design and timing problem on the Sioux Falls network, the facility location and timing problem on the Simchi-Levi and Berman (1988) network, and Hyytiä et al.’s (2012) dial-a-ride problem.