Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques
Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index us...
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| Published in | International journal of production research Vol. 63; no. 2; pp. 628 - 650 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Taylor & Francis
17.01.2025
Taylor & Francis LLC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0020-7543 1366-588X 1366-588X |
| DOI | 10.1080/00207543.2023.2267680 |
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| Abstract | Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims. |
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| AbstractList | Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover efficiency of medical supplies and unbalanced supply and demand point scheduling in public health emergencies. We construct a fairness index using an improved Gini coefficient by considering the demand for emergency medical supplies (EMS), actual distribution, and the degree of emergency at disaster sites. We developed a bi-objective optimisation model with a minimum Gini index and scheduling time. We employ a heterogeneous ant colony algorithm to solve the Pareto boundary based on reinforcement learning. A reinforcement learning mechanism is introduced to update and exchange pheromones among populations, with reward factors set to adjust pheromones and improve algorithm convergence speed. The effectiveness of the algorithm for a large EMSS problem is verified by comparing its comprehensive performance against a super-large capacity evaluation index. Results demonstrate the algorithm's effectiveness in reducing convergence time and facilitating escape from local optima in EMSS problems. The algorithm addresses the issue of demand differences at each disaster point affecting fair distribution. This study optimises early-stage EMSS schemes for public health events to minimise losses and casualties while mitigating emotional distress among disaster victims. Full Article Figures & data References Citations Metrics Reprints & Permissions Read this articleABSTRACTDrawing on the marketing aspects of customer satisfaction, this paper provides insights into the relationship between students’ and lecturers’ satisfaction in a higher education institution. The study critically adopts the logic of relationship marketing, arguing that students’ satisfaction affects lecturers’ job satisfaction. The relationship between students’ and lecturers’ satisfaction is explored using Balance Theory and Herzberg’s Motivation Theory. The Critical Incident Technique is used for data collection through interviews with student-lecturer dyads. The findings indicate that lecturers’ job satisfaction increases when students’ holistic feedback is explicitly shared with lecturers. Hence, investing in the communication of students’ satisfaction enhances lecturers’ job satisfaction. The current work adds a critical perspective to the relationship between lecturers’ and students’ satisfaction. It highlights a reversal of the accepted logic of relationship marketing, arguing that students’ satisfaction affects lecturers’ satisfaction. The methodological contribution of this work offers a new approach to using critical incident analysis techniques in dyads of students and lecturers. |
| Author | Islam, Nazrul Xia, Huosong Wang, Yuan Jasimuddin, Sajjad M. Kamal, Muhammad Mustafa Sun, Zelin Zhang, Justin Zuopeng |
| Author_xml | – sequence: 1 givenname: Huosong surname: Xia fullname: Xia, Huosong organization: Research Institute of Management and Economics, Wuhan Textile University – sequence: 2 givenname: Zelin surname: Sun fullname: Sun, Zelin organization: Wuhan Textile University – sequence: 3 givenname: Yuan surname: Wang fullname: Wang, Yuan organization: Wuhan Textile University – sequence: 4 givenname: Justin Zuopeng orcidid: 0000-0002-4074-9505 surname: Zhang fullname: Zhang, Justin Zuopeng organization: Department of Management, Coggin College of Business, University of North Florida – sequence: 5 givenname: Muhammad Mustafa surname: Kamal fullname: Kamal, Muhammad Mustafa email: ad2802@coventry.ac.uk organization: School of Strategy and Leadership, Coventry University – sequence: 6 givenname: Sajjad M. orcidid: 0000-0003-2627-9241 surname: Jasimuddin fullname: Jasimuddin, Sajjad M. organization: Kedge Business School – sequence: 7 givenname: Nazrul surname: Islam fullname: Islam, Nazrul organization: Royal Docks School of Business and Law, University of East London |
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| Keywords | algorithms artificial heuristics emergency Scheduling evolutionary medical supply public health intelligence |
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| Snippet | Based on AI technology, this study proposes a novel large-scale emergency medical supplies scheduling (EMSS) algorithm to address the issues of low turnover... Full Article Figures & data References Citations Metrics Reprints & Permissions Read this articleABSTRACTDrawing on the marketing aspects of customer... |
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| SubjectTerms | Algorithms Ant colony optimization artificial intelligence Casualties Convergence Disasters Effectiveness evolutionary algorithms heuristics Humanities and Social Sciences Machine learning Medical supplies medical supply Public health public health emergency Scheduling |
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| Title | Emergency medical supplies scheduling during public health emergencies: algorithm design based on AI techniques |
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