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This book presents the research that resulted from a fruitful collaboration between many CNRS research laboratories, health establishments and industrialists. This research contributes to the study and the development of logistical systems, in particular health-oriented logistical systems, in order to manage and optimize physical, informational and financial flows.
The authors examine optimization and modeling methods to facilitate decision support for the management of logistics systems in the health field, including solutions to problems encountered in the management of logistics flows and the study of systems incorporating these flows.
In the first chapter, logistics engineering is presented whilst the second chapter introduces the study of real cases of transport, management crisis and warehouse management logistics systems.
The third chapter is devoted to the study of hospital systems and emergency services and in the fourth chapter, the authors highlight the operational aspect of the hospital system thanks to an innovative modeling approach.
Finally, mathematical and algorithmic models of scheduling, and dynamic orchestration of the collaborative workflow by a multi-agent system, are introduced.
- Presents innovative optimization and modeling methods to provide decision support for the management of logistics systems
- Provides guidance to healthcare and hospital workers who must control the flow of process issues (i.e. patient information, products, equipment) and the restructuring that results internally in the pooling of resources, especially technical platforms
- Includes answers to problems encountered in the management of logistics flows and the study of systems incorporating these flows
- Addresses the challenges of quality and speed in an innovative approach to organizational, economic, technological, and informational optimization
Academic communities of healthcare and biomedical engineering. Health establishments, executives, line managers, managers in a private or public health facility.
- List of Acronyms
- 1: Logistics Engineering
- 1.1 Introduction
- 1.2 Logistics: origins and evolution
- 1.3 Logistics Network: definitions, characteristics and complexity
- 1.4 Logistics typology
- 1.5 Quality/logistics convergence
- 1.6 Infologistics: information systems for logistics
- 1.7 Possible resolution methods in favor of logistics
- 1.8 Conclusion
- 2: Case Studies and Contributions to the Resolution of Logistics System-related Problems
- 2.1 Introduction
- 2.2 Analogies between logistics systems
- 2.3 Transport logistics
- 2.4 Crisis management logistics
- 2.5 Warehouse logistics
- 2.6 Conclusion
- 3: Health Logistics: Toward Collaborative Approaches and Tools
- 3.1 Introduction
- 3.2 The health sector
- 3.3 Emergence of new needs
- 3.4 Health logistics
- 3.5 Hospital emergency services
- 3.6 Hospital and healthcare information systems
- 3.7 Analogy between conventional and healthcare logistics
- 3.8 Conclusion
- 4: Collaborative Workflow for Patient Pathway Modeling at Pediatric Emergency Services
- 4.1 Introduction
- 4.2 Definition of workflow
- 4.3 Why use a “workflow approach” in health?
- 4.4 Description of a workflow diagram type
- 4.5 Health collaborative workflow
- 4.6 Inter-operability concepts for health collaborative workflows
- 4.7 Patient pathway description for PES (CHRU de Lille)
- 4.8 PES infrastructure
- 4.9 Collaborative workflow for modeling patient pathway in the steady state
- 4.10 Agent-oriented approach for collaborative workflow
- 4.11 Agent coalition for executing collaborative workflow
- 4.12 Negotiation protocol between agents controlling a workflow instance
- 4.13 Global coherence and periodic behavior of collaborative workflow
- 4.14 Treatment of generated collaborative workflow decision points
- 4.15 Summary
- 4.16 Agent activities for collaborative workflow
- 4.17 Conclusion
- 5: Agent-based Architecture for Task Scheduling and Dynamic Orchestration Support
- 5.1 Introduction
- 5.2 Mathematical formulation of the scheduling problem at PES
- 5.3 Multiple competence task
- 5.4 Agent-based modeling
- 5.5 Description of a SA’s behavior
- 5.6 Dynamic aggregative approach for evaluating fitness function
- 5.7 Workflow orchestration
- 5.8 Simulation and results
- 5.9 Simulation and scheduling results: the SA’s behavior
- 5.10 Conclusion
- General Conclusion and Perspectives
- List of Authors
- No. of pages:
- © ISTE Press - Elsevier 2017
- 25th August 2016
- ISTE Press - Elsevier
- Hardcover ISBN:
- eBook ISBN:
Hayfa Zgaya is Associate Professor of Logistics and Health Informatics at the Institute of Engineering in Health of Lille, France. Her main research areas are optimization, artificial intelligence and logistics issues.
Institute of Engineering in Health of Lille, France
Slim HAMMADI is Professor of Industrial Engineering (Industrial Automation and Computer Science) at the Ecole Centrale de Lille (France). He obtained his PhD at the University of Lille I, on the subject of optimizing the scheduling flexible production workshops. In 1999, Mr. HAMMADI obtained his HDR at the University of Lille I, on the theme of modeling and optimization of complex systems. He is leader of the research group OPTIMA "Optimization, Models and Algorithms" and responsible for the OSL team "Optimizing Logistics Systems" CRISTAL laboratory CNRS UMR 9189. He is Senior Member of IEEE and IEEE player for several journals / SMC. Professor Hammadi co-chaired and chaired several international conferences in the field of logistics and transport and in the field of health logistics. His area of teaching for production management (including scheduling), computers, dynamic programming and advanced techniques (soft computing) in combinatorial optimization in transport and health. His research concerns the optimization methods highly combinatorial systems with implementation of algorithms evolutionary strategy, fuzzy set theory, the AI techniques, multi-agent application systems and the problems of hospital logistics, transport, crisis management and production systems.
Slim HAMMADI is Full Professor of modelling, optimization and control of complex systems at the Ecole Centrale de Lille in France. He is a director of the OSL “Logistic Optimization Systems” research team of the CRISTAL Laboratory. His teaching and research interests focus on the areas of production control, optimization, computer science, discrete and dynamic programming and complex logistic systems