Operating Room Planning under Surgery Type and Priority Constraints

AbstractOperating room (OR) planning is critical in healthcare systems to reduce cost and improve the efficiency of OR scheduling. The OR planning problem is complicated, involving many conflicting factors, such as overtime and idle time, both of which affect OR utilization and consequently affect cost to a hospital. Allocating different types of surgeries into OR blocks affects the setup cost, whereas priorities of surgeries affect OR block scheduling. Surgery durations affect both OR utilization and OR block scheduling. Traditionally, one important method for OR block scheduling is the bin packing model, and the longest processing time (LPT) rule is the most commonly used method to generate the initial sequence for bin packing. In this study. We propose a multistep approach and a priority-type-duration (PTD) rule to generate the initial sequence for bin packing. The results of our case studies show that our PTD rule outperforms the LPT rule based on the cost to OR scheduling

Dispatching Fire Trucks under Stochastic Driving Times

In this paper we discuss optimal dispatching of fire trucks, based on a particular dispatching problem that arises at the Amsterdam Fire Department, where two fire trucks are send to the same incident location for a quick response. We formulate the dispatching problem as a Markov Decision Process, and numerically obtain the optimal dispatching decisions using policy iteration. We show that the fraction of late arrivals can be significantly reduced by deviating from current practice of dispatching the closest available trucks, with a relative improvement of on average about $20\%$, and over $50\%$ for certain instances. We also show that driving-time correlation has a non-negligible impact on decision making, and if ignored may lead to performance decrease of over $20\%$ in certain cases. As the optimal policy cannot be computed for problems of realistic size due to the computational complexity of the policy iteration algorithm, we propose a dispatching heuristic based on a queueing approximation for the state of the network. We show that the performance of this heuristic is close to the optimal policy, and requires significantly less computational effort.Comment: Submitted to Computers and Operations Research (December 08, 2018

Emergency Resource Layout with Multiple Objectives under Complex Disaster Scenarios

Effective placement of emergency rescue resources, particularly with joint suppliers in complex disaster scenarios, is crucial for ensuring the reliability, efficiency, and quality of emergency rescue activities. However, limited research has considered the interaction between different disasters and material classification, which are highly vital to the emergency rescue. This study provides a novel and practical framework for reliable strategies of emergency rescue under complex disaster scenarios. The study employs a scenario-based approach to represent complex disasters, such as earthquakes, mudslides, floods, and their interactions. In optimizing the placement of emergency resources, the study considers government-owned suppliers, framework agreement suppliers, and existing suppliers collectively supporting emergency rescue materials. To determine the selection of joint suppliers and their corresponding optimal material quantities under complex disaster scenarios, the research proposes a multi-objective model that integrates cost, fairness, emergency efficiency, and uncertainty into a facility location problem. Finally, the study develops an NSGA-II-XGB algorithm to solve a disaster-prone province example and verify the feasibility and effectiveness of the proposed multi-objective model and solution methods. The results show that the methodology proposed in this paper can greatly reduce emergency costs, rescue time, and the difference between demand and suppliers while maximizing the coverage of rescue resources. More importantly, it can optimize the scale of resources by determining the location and number of materials provided by joint suppliers for various kinds of disasters simultaneously. This research represents a promising step towards making informed configuration decisions in emergency rescue work

Extreme Heat Vulnerability among Older Adults: A Multi-level Risk Index for Portland, Oregon

Background and Objectives Extreme heat is an environmental health equity concern disproportionately impacting low-income older adults and people of color. Exposure factors, such as living in rental housing and lack of air conditioning, and sensitivity factors, such as chronic disease and social isolation, increase mortality risk among older adults. Older persons face multiple barriers to adaptive heat mitigation, particularly for those living in historically temperate climates. This study measures two heat vulnerability indices to identify areas and individuals most vulnerable to extreme heat and discusses opportunities to mitigate vulnerability among older adults. Research Design and Methods We constructed two heat vulnerability indices for the Portland, Oregon metropolitan area: one using area scale proxy measures extracted from existing regional data and another at the individual scale using survey data collected following the 2021 Pacific Northwest Heat Dome event. These indices were analyzed using principal component analysis (PCA) and Geographic Information Systems (GIS). Results Results indicate that the spatial distribution of areas and individuals vulnerable to extreme heat are quite different. The only area found among the most vulnerable on both indices has the largest agglomeration of age- and income-restricted rental housing in the metropolitan area

Analysis of Staff Scheduling Effect on Hospitality Staffing Service Efficiency During High-Occupancy Conditions Using Discrete Event Simulation (DES)

This study utilized discrete event simulation (DES) in order to optimize the staff scheduling within the housekeeping department for hotel operation during high-occupancy conditions. High-occupancy situations within hotels occur during peak season times in which guests occupy a greater number of rooms than throughout the year. A literature review showed that DES has been used to optimize various types of schedules. This study was unique in the case that it incorporates computer modeling into the staffing portion of lodging establishments; an area with limited amount of research. Data was collected from historical records and through actual observations. A validated computerized model of the hotel was constructed using Arena 13 to determine an optimal staff schedule, which would decrease guests\u27 waiting times as well as payroll costs in the housekeeping department. A sensitivity analysis was conducted in order to determine the number housekeepers to employ and their length of schedule depending on the acceptable wait time allowed for guests and the number of checkouts. The product of this study was a model that could be used by other lodging establishments to determine how many housekeepers to employ based on the number of rooms checking out and staying over. Future studies could incorporate the rate to stay at the establishment and how that could have an effect on the arrival rate of guests and their willingness to stay at the hotel

Urban Air Mobility Market Study

The Booz Allen Team explored market size and potential barriers to Urban Air Mobility (UAM) by focusing on three potential markets – Airport Shuttle, Air Taxi, and Air Ambulance. We found that the Airport Shuttle and Air Taxi markets are viable, with a significant total available market value in the U.S. of $500 billion, for a fully unconstrained scenario. In this unconstrained best-case scenario, passengers would have the ability to access and fly a UAM at any time, from any location to any destination, without being hindered by constraints such as weather, infrastructure, or traffic volume. Significant legal and regulatory, weather, certification, public perception, and infrastructure constraints exist, which reduce the market potential for these applications to only about 0.5$2.5 billion, in the near term. However, we determined that these constraints can be addressed through ongoing intra-governmental partnerships, government and industry collaboration, strong industry commitment, and existing legal and regulatory enablers. We found that the Air Ambulance market is not a viable market if served by electric vertical takeoff and landing (eVTOL) vehicles due to technology constraints but may potentially be viable if a hybrid VTOL aircraft are utilized

Organizational Complexity, Plan Adequacy, and Nursing Home Resiliency: A Contingency Perspective

Some social and organizational behavior scientists measure resiliency through anecdotal qualitative research, i.e. personality analyses and stories of life experience. Empirical evidence remains limited for identifying measurable indicators of resiliency. Therefore, a testable contingency model was needed to clarify resiliency factors pertinent to organizational performance. Two essential resiliency factors were: 1) a written plan and 2) affiliation with a disaster network. This contingency study demonstrated a quantifiable, correlational effect between organizational complexity, disaster plan adequacy and organizational resiliency. The unit of analysis, the skilled nursing facility proved vulnerable, therefore justifying the need for a written emergency management plan and affiliation with a disaster network. The main purpose of this research was to verify the significance of emergency management plans within a contingency framework of complexity theory, resource dependency, systems theory, and network theory. Distinct sample moments quantified causal relationships between organizational complexity (A), plan adequacy (B) and resiliency (C). Primary and secondary research data were collected from within the context of public health and emergency management sectors within the State of Florida