More Like this

1. A Capacity Allocation Planning Model for Integrated Care and Access Management

   https://doi.org/10.1111/poms.12941  

   Deglise‐Hawkinson, Jivan ; Helm, Jonathan E. ; Huschka, Todd ; Kaufman, David L. ; Van Oyen, Mark P. ( October 2018 , Production and Operations Management)

   The prevailing first‐come‐first‐served approach to outpatient appointment scheduling ignores differing urgency levels, leading to unnecessarily long waits for urgent patients. In data from a partner healthcare organization, we found in some departments that urgent patients were inadvertently waiting longer for an appointment than non‐urgent patients. This study develops a capacity allocation optimization methodology that reserves appointment slots based on urgency in a complicated, integrated care environment where multiple specialties serve multiple types of patients. This optimization reallocates network capacity to limit access delays (indirect waiting times) for initial and downstream appointments differentiated by urgency. We formulate this problem as a queueing network optimization and approximate it via deterministic linear optimization to simultaneously smooth workloads and guarantee access delay targets. In a case study of our industry partner we demonstrate the ability to (i) reduce urgent patient mean access delay by 27% with only a 7% increase in mean access delay for non‐urgent patients, and (ii) increase throughput by 31% with the same service levels and overtime.

    

   more » « less
2. Persuading Risk-Conscious Agents: A Geometric Approach

   https://doi.org/10.1007/978-3-030-35389-6  

   Anunrojwong, Jerry ; Iyer, Krishnamurthy ; Lingenbrink, David. ( December 2019 , Lecture notes in computer science)

   Motivated by practical concerns in applying information design to markets and service systems, we consider a persuasion problem between a sender and a receiver where the receiver may not be an expected utility maximizer. In particular, the receiver’s utility may be non-linear in her belief; we deem such receivers as risk-conscious. Such utility models arise, for example, when the receiver exhibits sensitivity to the variability and the risk in the payoff on choosing an action (e.g., waiting time for a service). In the presence of such non-linearity, the standard approach of using revelation-principle style arguments fails to characterize the set of signals needed in the optimal signaling scheme. Our main contribution is to provide a theoretical framework, using results from convex analysis, to overcome this technical challenge. In particular, in general persuasion settings with risk-conscious agents, we prove that the sender’s problem can be reduced to a convex optimization program. Furthermore, using this characterization, we obtain a bound on the number of signals needed in the optimal signaling scheme. We apply our methods to study a specific setting, namely binary per-suasion, where the receiver has two possible actions (0 and 1), and the sender always prefers the receiver taking action 1. Under a mild convexity assumption on the receiver’s utility and using a geometric approach,we show that the convex program can be further reduced to a linear program. Furthermore, this linear program yields a canonical construction of the set of signals needed in an optimal signaling mechanism. In particular, this canonical set of signals only involves signals that fully reveal the state and signals that induce uncertainty between two states.We illustrate our results in the setting of signaling wait time information in an unobservable queue with customers whose utilities depend on the variance of their waiting times. 

   more » « less
3. Rate Maximization in a UAV Based Full-Duplex Multi-User Communication Network Using Multi-Objective Optimization

   https://doi.org/10.3390/electronics11030401  

   Hashir, Syed Muhammad ; Gupta, Sabyasachi ; Megson, Gavin ; Aryafar, Ehsan ; Camp, Joseph ( February 2022 , Electronics)

   In this paper, we study an unmanned-aerial-vehicle (UAV) based full-duplex (FD) multi-user communication network, where a UAV is deployed as a multiple-input–multiple-output (MIMO) FD base station (BS) to serve multiple FD users on the ground. We propose a multi-objective optimization framework which considers two desirable objective functions, namely sum uplink (UL) rate maximization and sum downlink (DL) rate maximization while providing quality-of-service to all the users in the communication network. A novel resource allocation multi-objective-optimization-problem (MOOP) is designed which optimizes the downlink beamformer, the beamwidth angle, and the 3D position of the UAV, and also the UL power of the FD users. The formulated MOOP is a non-convex problem which is generally intractable. To handle the MOOP, a weighted Tchebycheff method is proposed, which converts the problem to the single-objective-optimization-problem (SOOP). Further, an alternative optimization approach is used, where SOOP is converted in to multiple sub-problems and optimization variables are operated alternatively. The numerical results show a trade-off region between sum UL and sum DL rate, and also validate that the considered FD system provides substantial improvement over traditional HD systems. 

   more » « less
4. UAV Assisted Cellular Networks With Renewable Energy Charging Infrastructure: A Reinforcement Learning Approach

   https://doi.org/10.1109/MILCOM52596.2021.9653034  

   Sherman, Michelle ; Shao, Sihua ; Sun, Xiang ; Zheng, Jun ( November 2021 , MILCOM 2021 - 2021 IEEE Military Communications Conference (MILCOM))

   Deploying unmanned aerial vehicle (UAV) mounted base stations with a renewable energy charging infrastructure in a temporary event (e.g., sporadic hotspots for light reconnaissance mission or disaster-struck areas where regular power-grid is unavailable) provides a responsive and cost-effective solution for cellular networks. Nevertheless, the energy constraint incurred by renewable energy (e.g., solar panel) imposes new challenges on the recharging coordination. The amount of available energy at a charging station (CS) at any given time is variable depending on: the time of day, the location, sunlight availability, size and quality factor of the solar panels used, etc. Uncoordinated UAVs make redundant recharging attempts and result in severe quality of service (QoS) degradation. The system stability and lifetime depend on the coordination between the UAVs and available CSs. In this paper, we develop a reinforcement learning time-step based algorithm for the UAV recharging scheduling and coordination using a Q-Learning approach. The agent is considered a central controller of the UAVs in the system, which uses the ϵ -greedy based action selection. The goal of the algorithm is to maximize the average achieved throughput, reduce the number of recharging occurrences, and increase the life-span of the network. Extensive simulations based on experimentally validated UAV and charging energy models reveal that our approach exceeds the benchmark strategies by 381% in system duration, 47% reduction in the number of recharging occurrences, and achieved 66% of the performance in average throughput compared to a power-grid based infrastructure where there are no energy limitations on the CSs. 

   more » « less
5. DYNAMIC OPTIMIZATION OF DRONE DISPATCH FOR SUBSTANCE OVERDOSE RESCUE

   Gao, X ( October 2020 , Proceedings of the 2020 Winter Simulation Conference)

   Opioid overdose rescue is very time-sensitive. Hence, drone-delivered naloxone has the potential to be a transformative innovation due to its easily deployable and flexible nature. We formulate a Markov Decision Process (MDP) model to dispatch the appropriate drone after an overdose request arrives and to relocate the drone to its next waiting location after having completed its current task. Since the underlying optimization problem is subject to the curse of dimensionality, we solve it using ad-hoc state aggregation and evaluate it through a simulation with higher granularity. Our simulation-based comparative study is based on emergency medical service data from the state of Indiana. We compare the optimal policy resulting from the scaled-down MDP model with a myopic policy as the baseline. We consider the impact of drone type and service area type on outcomes, which offers insights into the performance of the MDP suboptimal policy under various settings. 

   more » « less