Algorithms for Optimizing Fleet Scheduling of Air Ambulances

• URL: http://arxiv.org/abs/2002.11710v1
• Date: Tue, 25 Feb 2020 21:49:46 GMT
• Title: Algorithms for Optimizing Fleet Scheduling of Air Ambulances
• Authors: Joseph Tassone and Salimur Choudhury
• Abstract summary: Proper scheduling of air assets can be the difference between life and death for a patient. These issues are amplified in the case of an air emergency medical service (EMS) system where populations are dispersed, and resources are limited. For this research, known coordinates of air and health facilities were used in conjunction with a formulated integer linear programming model. This was programmed through Gurobi so that performance could be compared against custom algorithmic solutions.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Proper scheduling of air assets can be the difference between life and death for a patient. While poor scheduling can be incredibly problematic during hospital transfers, it can be potentially catastrophic in the case of a disaster. These issues are amplified in the case of an air emergency medical service (EMS) system where populations are dispersed, and resources are limited. There are exact methodologies existing for scheduling missions, although actual calculation times can be quite significant given a large enough problem space. For this research, known coordinates of air and health facilities were used in conjunction with a formulated integer linear programming model. This was the programmed through Gurobi so that performance could be compared against custom algorithmic solutions. Two methods were developed, one based on neighbourhood search and the other on Tabu search. While both were able to achieve results quite close to the Gurobi solution, the Tabu search outperformed the former algorithm. Additionally, it was able to do so in a greatly decreased time, with Gurobi actually being unable to resolve to optimal in larger examples. Parallel variations were also developed with the compute unified device architecture (CUDA), though did not improve the timing given the smaller sample size.

Related papers

• A Schedule of Duties in the Cloud Space Using a Modified Salp Swarm Algorithm [0.0]
  One of the most important NP-hard issues in the cloud domain is scheduling. One of the collective intelligence algorithms, called the Salp Swarm Algorithm (SSA), has been expanded, improved, and applied. Results show that our algorithm has generally higher performance than the other algorithms.
  arXiv  Detail & Related papers  (2023-09-18T02:48:41Z)
• A Hierarchical Temporal Planning-Based Approach for Dynamic Hoist Scheduling Problems [11.66506213335498]
  Hoist scheduling has become a bottleneck in electroplating industry applications with the development of autonomous devices. We formulate the hoist scheduling problem as a new temporal planning problem in the form of adapted PDDL. We provide a collection of real-life benchmark instances that can be used to evaluate solution methods for the problem.
  arXiv  Detail & Related papers  (2022-12-11T05:30:44Z)
• Genetic algorithms for the resource-constrained project scheduling problem in aircraft heavy maintenance [0.0]
  This article proposes genetic algorithms for solving the resource-constrained project scheduling problem (RCPSP) in AHM. The objective of the study was to minimise the makespan of the maintenance plan.
  arXiv  Detail & Related papers  (2022-06-16T13:41:20Z)
• Machine Learning for Online Algorithm Selection under Censored Feedback [71.6879432974126]
  In online algorithm selection (OAS), instances of an algorithmic problem class are presented to an agent one after another, and the agent has to quickly select a presumably best algorithm from a fixed set of candidate algorithms. For decision problems such as satisfiability (SAT), quality typically refers to the algorithm's runtime. In this work, we revisit multi-armed bandit algorithms for OAS and discuss their capability of dealing with the problem. We adapt them towards runtime-oriented losses, allowing for partially censored data while keeping a space- and time-complexity independent of the time horizon.
  arXiv  Detail & Related papers  (2021-09-13T18:10:52Z)
• Efficient Algorithms for Finite Horizon and Streaming Restless Multi-Armed Bandit Problems [30.759279275710078]
  We propose a new and scalable approach to computing index-based solutions. We provide algorithms designed to capture index decay without having to solve the costly finite horizon problem. Our algorithms achieve an over 150x speed-up over existing methods in these tasks without loss in performance.
  arXiv  Detail & Related papers  (2021-03-08T13:10:31Z)
• Towards Optimally Efficient Tree Search with Deep Learning [76.64632985696237]
  This paper investigates the classical integer least-squares problem which estimates signals integer from linear models. The problem is NP-hard and often arises in diverse applications such as signal processing, bioinformatics, communications and machine learning. We propose a general hyper-accelerated tree search (HATS) algorithm by employing a deep neural network to estimate the optimal estimation for the underlying simplified memory-bounded A* algorithm.
  arXiv  Detail & Related papers  (2021-01-07T08:00:02Z)
• Community detection using fast low-cardinality semidefinite programming [94.4878715085334]
  We propose a new low-cardinality algorithm that generalizes the local update to maximize a semidefinite relaxation derived from Leiden-k-cut. This proposed algorithm is scalable, outperforms state-of-the-art algorithms, and outperforms in real-world time with little additional cost.
  arXiv  Detail & Related papers  (2020-12-04T15:46:30Z)
• Resource Allocation in Multi-armed Bandit Exploration: Overcoming Sublinear Scaling with Adaptive Parallelism [107.48538091418412]
  We study exploration in multi-armed bandits when we have access to a divisible resource that can be allocated in varying amounts to arm pulls. We focus in particular on the allocation of distributed computing resources, where we may obtain results faster by allocating more resources per pull.
  arXiv  Detail & Related papers  (2020-10-31T18:19:29Z)
• Combining Deep Learning and Optimization for Security-Constrained Optimal Power Flow [94.24763814458686]
  Security-constrained optimal power flow (SCOPF) is fundamental in power systems. Modeling of APR within the SCOPF problem results in complex large-scale mixed-integer programs. This paper proposes a novel approach that combines deep learning and robust optimization techniques.
  arXiv  Detail & Related papers  (2020-07-14T12:38:21Z)
• FedPD: A Federated Learning Framework with Optimal Rates and Adaptivity to Non-IID Data [59.50904660420082]
  Federated Learning (FL) has become a popular paradigm for learning from distributed data. To effectively utilize data at different devices without moving them to the cloud, algorithms such as the Federated Averaging (FedAvg) have adopted a "computation then aggregation" (CTA) model.
  arXiv  Detail & Related papers  (2020-05-22T23:07:42Z)
• Algorithms for Optimizing Fleet Staging of Air Ambulances [0.0]
  This research structured an optimal coverage problem with integer linear programming. A Gurobi was programmed with the developed model and timed for performance. A solution implementing base ranking followed by both local and Tabu search-based algorithms was created.
  arXiv  Detail & Related papers  (2020-01-10T04:32:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.