Jaeyoung Cho

Multi-UAV Pre-Positioning and Routing for Power Network Damage Assessment

This paper presents a two-phase mathematical framework for efficient power network damage assessment using unmanned aerial vehicle (UAV). In the first phase, a two-stage stochastic integer programming optimization model is presented for damage assessment in which the first stage determines the optimal UAV locations anticipating an arrival of an extreme weather event, and the second stage is to adjust the UAV locations, if necessary, when the arrival time of the predicted extreme weather becomes closer with updated information. UAV paths to scan the power network are generated in the second phase to minimize operating costs and final damage assessment completion time of the UAVs. Computational techniques are developed to help reduce the solution time. Numerical experiments show that the proposed stochastic model outperforms the deterministic counterpart in terms of the total UAV pre-positioning setup cost. Additionally, sensitivity analysis discovered the relations among damage assessment time, UAV pre-positioning setup cost, and operating cost.

Drone-Aided Healthcare Services for Patients with Chronic Diseases in Rural Areas

This paper addresses the drone-aided delivery and pickup planning of medication and test kits for patients with chronic diseases who are required to visit clinics for routine health examinations and/or refill medicine in rural areas. For routine healthcare services, the work proposes two models: the first model is to find the optimal number of drone center locations using the set covering approach, and the second model is the multi-depot vehicle routing problem with pickup and delivery requests minimizing the operating cost of drones in which drones deliver medicine to patients and pick up exam kits on the way back such as blood and urine samples. In order to improve computational performance of the proposed models, a preprocessing algorithm, a Partition method, and a Lagrangian Relaxation (LR) method are developed as solution approaches. A cost-benefit analysis method is developed as a tool to analyze the benefits of drone-aided healthcare service. The work is tested on a numerical example to show its applicability.

Models and computational algorithms for maritime risk analysis: a review

Due to the undesirable implications of maritime mishaps such as ship collisions and the consequent damages to maritime property; the safety and security of waterways, ports and other maritime assets are of the utmost importance to authorities and researches. Terrorist attacks, piracy, accidents and environmental damages are some of the concerns. This paper provides a detailed literature review of over 180 papers about different threats, their consequences pertinent to the maritime industry, and a discussion on various risk assessment models and computational algorithms. The methods are then categorized into three main groups: statistical, simulation and optimization models. Corresponding statistics of papers based on year of publication, region of case studies and methodology are also presented.

Simulation modeling of Houston Ship Channel vessel traffic for optimal closure scheduling

Abstract This paper focuses on simulation and analysis of the Houston Ship Channel vessel traffic and operation, through a discrete event model in Arena. The model is applied to mitigate the consequences of the channel closure for constructing a new bridge over the waterway. To evaluate different closure scenarios, real world data is analyzed, and a single factor ANOVA is used to find significant vessel waiting time differences. In addition, Fisher pairwise comparison method is applied to specify the best closure alternative. The results reveal that the best closure scenario will decrease waiting time up to 70%. The model can be used for assessing the performance of the system under different decision making frameworks.

Drone flight scheduling under uncertainty on battery duration and air temperature

Abstract Commercial drones are expected to be widely used in the near future. They are generally powered by batteries to fly aerial areas. Flying time performance is known to change depending on air temperature. Hence, this paper proposes a robust optimization approach to find the optimal flight schedule (i.e., the number of drones and flight paths) in the flight network considering uncertain battery duration. A regression model is first developed to estimate battery duration as a function of air temperature. Three flight duration uncertainty sets (polyhedral, box, and ellipsoidal) are explored based on the regression model, and the robust optimization model is solved for each of the three sets. A decision tool is developed to analyze performance of the sets, and to help a decision maker select an appropriate uncertainty set that is most appropriate for a specific application of interest. The tool considers both minimizing the total operating cost and minimizing the probability of not completing the scheduled flights. Numerical results are presented to illustrate the proposed method.

Drone Relay Stations for Supporting Wireless Communication in Military Operations

This paper proposes a concept of drone relay stations for supporting wireless communications in military operations. Communications in military operations are crucial in completing desired missions under highly chaotic situations. Due to dynamic movements of troops, multiple units require wireless communications when they are separated from each other to coordinate the efforts for the mission. Relay stations are often used to enhance wireless signals. Current methods include ground vehicles, helicopters, or satellites to relay wireless signal in military operations. However, these methods have known limitations such as inaccessible areas (mountains, ice roads, etc.), potential exposure to the enemy force, and high operating costs. Hence, this work proposes the concept of drone relay stations to overcome these limitations and explore enabling technologies that have been developed.