@article{atik_hajibabai_2023, title={Joint column generation and Lagrangian relaxation technique for incident respondent location and allocation}, volume={5}, ISSN={["1467-8667"]}, DOI={10.1111/mice.13016}, abstractNote={Abstract Incident response operations require effective planning of resources to ensure timely clearance of roadways and avoidance of secondary incidents. This study formulates a mixed‐integer linear program to minimize the total expected travel time and maximize the demand covered. The model accounts for the location, severity, frequency of incidents, dispatching locations, and availability of incident respondents. An integrated methodology that includes column generation and Lagrangian relaxation with a density‐based clustering technique that defines incident hot spots is proposed. The hybrid approach is applied to an empirical case study in Raleigh, NC. A network instance with 10,672 incident sites, clustered with a search distance (ε) of 5 min, is solved efficiently with an optimality gap of 1.37% in 2 min. A Benders decomposition technique is implemented to conduct benchmark analyses. The numerical results suggest that the proposed algorithm can solve the problem efficiently and outperform the benchmark solutions.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Atik, Asya and Hajibabai, Leila}, year={2023}, month={May} }
 @article{hajibabai_atik_mirheli_2022, title={Joint power distribution and charging network design for electrified mobility with user equilibrium decisions}, volume={6}, ISSN={["1467-8667"]}, DOI={10.1111/mice.12854}, abstractNote={Abstract Rapid adoption of electric vehicles (EVs) requires the development of a highly flexible charging network. The design and management of the charging infrastructure for EV‐dominated transportation systems are intertwined with power grid operations both economically and technically. High penetration of EVs in the future can increase the charging loads and cause a wide range of operational issues in power distribution networks (PDNs). This paper aims to design an EV charging network with an embedded PDN layout to account for energy dispatch and underlying traffic flows in urban transportation networks supporting electric mobility in the near future. A mixed‐integer bilevel model is proposed with the EV charging facility location and PDN energy decisions in the upper level and user equilibrium traffic assignment in the lower level considering an uncertain charging demand. The objective is to minimize the cost of PDN operations, charging facility deployments, and transportation. The proposed problem is solved using a column and constraint generation (C&CG ) algorithm, while a macroscopic fundamental diagram concept is implemented to estimate the arc travel times. The methodology is applied to a hypothetical and two real‐world case study networks, and the solutions are compared to a Benders decomposition benchmark. The east‐coast analysis results indicate a 77.3% reduction in the computational time. Additionally, the benchmark technique obtains an optimality gap of 1.15%, while the C&CG algorithm yields a 0.61% gap. The numerical experiments show the robustness of the proposed methodology. Besides, a series of sensitivity analyses has been conducted to study the impact of input parameters on the proposed methodology and draw managerial insights.}, journal={COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING}, author={Hajibabai, Leila and Atik, Asya and Mirheli, Amir}, year={2022}, month={Jun} }
 @article{atik_hajibabai_2021, title={Location and Allocation of Incident Respondents under Severity Levels and Capacity Constraints: Formulation, Methodology, and Application}, ISSN={["2153-0009"]}, DOI={10.1109/ITSC48978.2021.9565010}, abstractNote={This study investigates an optimal incident response plan to cover the demand considering the location and availability of respondents. A multi-objective mixed-integer linear formulation is proposed that aims to minimize the expected travel time to incidents and maximize the expected demand coverage under resource constraints. The proposed model is applied to an empirical case study network with one year of incident data in North Carolina and solved by Benders Decomposition. The numerical experiments indicate the performance of the proposed methodology based on various problem sizes. While CPLEX Optimization Studio fails to obtain solutions for large-scale instances, the proposed algorithm can provide near-optimal solutions to the largest instance in this paper (i.e., 1,024-node network) with a 2.4% optimality gap.}, journal={2021 IEEE INTELLIGENT TRANSPORTATION SYSTEMS CONFERENCE (ITSC)}, author={Atik, Asya and Hajibabai, Leila}, year={2021}, pages={2181–2186} }