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Multi-resource scheduling and routing for emergency recovery operations.

ABSTRACT: Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, Scheduling. Monte Carlo, and Clustering, are developed and benchmarked against the exact method (for small instances) and the genetic algorithm (for large instances). Results reveal that all six heuristics are valid and generate near or actual optimal solutions for small instances. With respect to large instances, the developed methods can generate near-optimal solutions within an acceptable computational time frame. The Monte Carlo algorithm, however, emerges as the most effective method. Findings of comprehensive comparative analysis suggest that the proposed MRSRP model and the Monte Carlo method can serve as a useful tool for decision-makers to better deploy resources during emergency recovery operations.

SUBMITTER: Bodaghi B

PROVIDER: S-EPMC7456293 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Multi-resource scheduling and routing for emergency recovery operations.

Bodaghi Behrooz B Shahparvari Shahrooz S Fadaki Masih M Lau Kwok Hung KH Ekambaram Palaneeswaran P Chhetri Prem P

International journal of disaster risk reduction : IJDRR 20200829

Efficient delivery of multiple resources for emergency recovery during disasters is a matter of life and death. Nevertheless, most studies in this field only handle situations involving single resource. This paper formulates the Multi-Resource Scheduling and Routing Problem (MRSRP) for emergency relief and develops a solution framework to effectively deliver expendable and non-expendable resources in Emergency Recovery Operations. Six methods, namely, Greedy, Augmented Greedy, k-Node Crossover, ...[more]

PMID: 32904513

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Project description:BackgroundEvery week, radiotherapy centers face the complex task of scheduling hundreds of treatment sessions amongst the available linear accelerators. With the increase in cancer patient numbers, manually creating a feasible and efficient schedule has shown to be a difficult, time-consuming task. Although operations research models have been increasingly reported upon to optimize patient care logistics, there is almost no scientific evidence of implementation in practice.MethodsA mathematical operations research model was adapted to generate radiotherapy treatment schedules in two Dutch centers. The model was iteratively adjusted to fulfill the technical and medical constraints of each center until a valid model was attained. Patient data was collected for the planning horizon of one week, and the feasibility of the obtained schedules was verified by the staff of each center. The resulting optimized solutions are compared with the ones manually developed in practice.ResultsThe weekly schedule was improved in both centers by decreasing the average standard deviation between sessions' starting times from 103.0 to 50.4 minutes (51%) in one center, and the number of gaps in the schedule from 18 to 5 (72%) in the other. The number of patients requiring linac switching between sessions has also decreased from 71 to 0 patients in one center, and from 43 to 2 in the other. The automated process required 5 minutes and 1.5 hours of computation time to find an optimal weekly patient schedule, respectively, as opposed to approximately 1.5 days when performed manually for both centers.ConclusionsThe practical application of a theoretical operations research model for radiotherapy treatment scheduling has provided radiotherapy planners a feasible, high-quality schedule in an automated way. Iterative model adaptations performed in small steps, early engagement of stakeholders, and constant communication proved to facilitate the implementation of operations research models into clinical practice.

Project description:BackgroundAcademic and non-academic emergency departments (EDs) are regularly compared in clinical operations benchmarking despite suggestion that the two groups may differ in their clinical operations characteristics. and outcomes. We sought to describe and compare clinical operations characteristics of academic versus non-academic EDs.MethodsWe performed a descriptive, comparative analysis of academic and non-academic adult and general EDs with 40,000+ annual encounters, using the Academy of Academic Administrators of Emergency Medicine (AAAEM)/Association of Academic Chairs of Emergency Medicine (AACEM) and Emergency Department Benchmarking Alliance (EDBA) survey results. We defined academic EDs as primary teaching sites for emergency medicine (EM) residencies and non-academic EDs as sites with minimal resident involvement. We constructed the academic and non-academic cohorts from the AAAEM/AACEM and EDBA surveys, respectively, and analyzed metrics common to both surveys.ResultsEighty and 454 EDs met inclusion criteria for academic and non-academic EDs, respectively. Academic EDs had more median annual patient encounters (73,001 vs 54,393), lower median proportion of pediatric patients (6.3% vs 14.5%), higher median proportion of EMS patients (27% vs 19%), and were more commonly designated as Level I or II Trauma Centers (94% vs 24%). Median patient arrival-to-provider times did not differ (26 vs 25 min). Median length-of-stay was longer (277 vs 190 min) for academic EDs, and left-before-treatment-complete was higher (5.7% vs 2.9%). MRI utilization was higher for academic EDs (2.2% patients with at least one MRI vs 1.0 MRIs performed per 100 patients). Patients-per-hour of provider coverage was lower for academic EDs with and without consideration for advanced practice providers and residents.ConclusionsDemographic and operational performance measures differ between academic and non-academic EDs, suggesting that the two groups may be inappropriate operational performance comparators. Causes for the differences remain unclear but the differences appear not to be attributed solely to the academic mission.

Dataset Information

Multi-resource scheduling and routing for emergency recovery operations.

Publications

Multi-resource scheduling and routing for emergency recovery operations.

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