Logistics Engineering and Health

By Hayfa Zgaya and Slim Hammadi

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

• Language: English
• Publisher: Elsevier Science
• Release date: Sep 13, 2016
• ISBN: 9780081010686

Hayfa Zgaya

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.

Book preview

Logistics Engineering and Health

Hayfa Zgaya

Slim Hammadi

Table of Contents

Cover image

Title page

Copyright

Preface

List of Acronyms

Introduction

1: Logistics Engineering

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Abstract

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

Bibliography

List of Authors

Index

Copyright

First published 2016 in Great Britain and the United States by ISTE Press Ltd and Elsevier Ltd

Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms and licenses issued by the CLA. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned address:

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Notices

Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary.

Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein. In using such information or methods they should be mindful of their own safety and the safety of others, including parties for whom they have a professional responsibility.

To the fullest extent of the law, neither the Publisher nor the authors, contributors, or editors, assume any liability for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions, or ideas contained in the material herein.

For information on all our publications visit our website at http://store.elsevier.com/

© ISTE Press Ltd 2016

The rights of Hayfa Zgaya and Slim Hammadi to be identified as the author of this work have been asserted by them in accordance with the Copyright, Designs and Patents Act 1988.

British Library Cataloguing-in-Publication Data

A CIP record for this book is available from the British Library

Library of Congress Cataloging in Publication Data

A catalog record for this book is available from the Library of Congress

ISBN 978-1-78548-044-7

Printed and bound in the UK and US

Preface

Hayfa Zgaya; Slim Hammadi

Recently, the academic and industrial world has understood that, in order to be successful, it is no longer enough to manage an organization relying exclusively on local performance objectives, in a compartmentalized way, but it is rather preferable to do it transversally and considering all possible options. Hence, the term logistics, refers to all the activities that aim to select a set of resources at the lowest cost, for the purpose of conforming to customers’ requirements. Nowadays, health-related logistics is accompanying demographic, socioeconomic and regulatory changes, particularly due to the appearance of new organization and evaluation modes. In this way, there is increasing awareness about the importance of the management of healthcare systems and of the control of different hospital flows. Indeed, actors from the hospital and healthcare sector must master the problems associated with process flows (i.e. patients, information, products, equipment) and with the internal restructuring manifested by the pooling of resources, in particular, that of technical platforms. However, healthcare professionals are neither prepared nor trained to solve such problems. It appears that they are lacking methodologies and decision support tools adapted to the demands that come with these future operation models. In this context, the current research and development works (R&D) aim to conceive and integrate innovative optimization and modeling methods that can provide decision support for the management of logistics systems. Such systems are often dynamic, spread over large-scale networks and presented as autonomous entities in interaction. The solutions that we recommend in this book provide answers to problems related to the management of logistics flows and the study of systems incorporating these flows. Owing to their increasing complexity, as well as their quality and speed decision requirements, it is necessary to respond to these challenges through an organizational, economic, technological and informational innovative optimization process, which will allow us to generate and perpetuate the necessary synergy to respond to the problems.

The works that we introduce in this book result from the fruitful collaboration between the Ecole Centrale de Lille (OSL team, OPTIMA group, CRISTAL CNRS UMR 9189)¹ and the University of Lille 2 (EA2694 Public Health Laboratory)².

June 2016

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¹ http://www.cristal.univ-lille.fr.

² http://ea2694.univ-lille2.fr.

List of Acronyms

AFNOR French organization for standardization

AI Artificial Intelligence

AOA Agent-Oriented Approach

APS Advanced Planning and Scheduling

ASLOG French Logistics Association

BP Business Process

BPMN Business Process Modeling Notation

CAPM Computer-Assisted Production Management

CISIT International Campus for Transport Security and Intermodality

CMCG Consumption of Medical Care and Goods

DSS Decision Support System

DV Decision Variable

EA Evolutionary Algorithm

EC Equality Constraints

EDD Exchange of Digitized Data

ERP Enterprise Resource Planning

FL Flow Logistics

HES Hospital Emergency Services

HL 

Hospital Logistics

HSC Hierarchical Supply Chain

IC Inequality Constraints

ICP Industrial and Commercial Plan

IEA Integration and Evaluation Agent

ILP Integer Linear Programming

LN Logistics Network

LP Logistics Process

LS Logistics System

MAS Multi-Agent System

MPE Methods for Performance Evaluation

MSA Medical Staff Agent

NICT New Information and Communication Technologies

OF Objective Function

PES Pediatric Emergency Services

PIA Problem Identification Agent

PM Production Management

PMIS Program of Medical Information Systems

POA Process-Oriented Approach

RA Router Agent

RA Resource Agent

RDSO Regional Diagrams of Sanitary Organizations

RON Reception and Orientation Nurse

SA Scheduling Agent

SC Supply Chain

SCM Supply Chain Management

TA Tracing Agent

Introduction

Hayfa Zgaya; Slim Hammadi

There is increasing awareness about the importance of the management of healthcare production systems and the control of different hospital flows. This control aims to improve the quality of healthcare and is conditioned by health-related cost imperatives, risk management and quality. In recent years, this has been translated into different strategic and operational actions in the field of healthcare networks.

Numerous reports and studies describe the current state of hospitals and the healthcare system, which is undergoing a moral, demographic and financial crisis [COU 03a, MOL 05], a summary of which can be found in the work of Marcon et al. [MAR 08]. This situation is the cumulative result of new constraints and the strong rigidity of existing structures. In this way, hospital systems and emergency units are finding it more and more difficult to complete their missions.

Nevertheless, we observe that the French hospital and healthcare system is undergoing a global mutation. The healthcare culture, in particular public and private hospitals, is confronted with new concepts. Even though these initiatives may have a beneficial effect, the analysis of the necessary evolution of the healthcare system shows that a determining factor of this evolution consists of optimizing organizations, particularly their information systems. Indeed, many of the currently observed dysfunctions in hospitals and healthcare units result from an ill-adapted organization under the constraints and the evolution of their missions, as well as from the poor management of the flow of patients.

The logisticians of these systems are confronted with problems of increasing complexity, such as: How is it possible to improve, secure and optimize logistics flows? How should the flow synchronization of these naturally dispersed systems, namely healthcare networks, global supply chains of multimodal transport, multi-site production networks and multizone crisis management, be improved? Which NICTs¹ should be adopted and how should they be harmoniously implanted taking into account the specificities of these logistics systems?

Thus, the actors from the hospital and healthcare system must solve those problems associated with process flows (i.e. patients, information, products, equipment) and with the internal restructuring that is manifested by the pooling of resources, mainly that of technical platforms. However, healthcare professionals are neither prepared nor trained to deal with such problems. It appears that they are lacking methodologies and decision support tools adapted to the demands that come with these future operation models.

This book presents research resulting from a fruitful collaboration between many CNRS research laboratories, health establishments and industrialists. This research contributes to the study and the development of logistics systems, in particular health-oriented logistics systems, in order to manage and optimize physical, informational and financial flows. In this book, we approach the problems surrounding the modeling, optimization and implementation of tools that help evaluate production, transport and crisis management systems, with a particular focus on health logistics systems. Nowadays, in the field of health logistics, the study and evaluation of overcrowding in hospitals (particularly in the emergency services) is becoming a major step toward finding efficient solutions that could improve patient care. These solutions represent the logistics engine of the healthcare establishment, which could gain in efficiency from the point of view of care, human resources and material management, medical activity pricing system and risk anticipation. A health logistics system must interact in a dispersed, uncertain and dynamic environment, enabling us to simulate, in a true-to-life way, the spread of logistics flows, the activities of the medical staff as well as their behavior and movements within the health centers.

For example, the research works linked to the management of overcrowding in healthcare establishments have been supported and financed by the National Research Agency (NRA) in the context of the project NRA HOST² (2012–2015). This project HOST (Hospital: Optimization, Simulation and Tension Avoidance) proposes a solution for the implementation of a Decision Support System, making it possible to avoid the overcrowding that could take place at a healthcare establishment. More particularly, these research works deal with the question of how to better manage overcrowding at the Services des Urgences Pédiatriques³ (SUP), which is the research domain of the HOST project.

The present book is organized into five chapters, as follows:

Chapter 1 presents logistics engineering by detailing the approaches of modeling, optimization and decision support to manage logistics flows.

Chapter 2 introduces the study of real cases of transport, management crisis and warehouse management logistics systems, specifying (for each field of application) the context, the problems studied and some possible solutions.

Chapter 3 is devoted to the study of hospital systems and emergency services. Here, we describe the healthcare sector: the context (international, national and regional), the hospital information systems as well as the hospital emergency systems. This helps us to describe the position of the hospital emergency services in the French healthcare system. Later, we will describe the functioning of pediatric emergency services.

Chapter 4 underlines the operational aspect of the hospital system, thanks to an innovative modeling approach, which allows the patient journey to be represented under the form of a collaborative workflow.

Chapter 5 introduces mathematical and algorithmic models of scheduling and dynamic orchestration of the collaborative workflow presented in the previous chapter by a multi-agent system. Many scenarios issued from real databases are tested and evaluated in order to anticipate and manage overcrowding of the pediatric emergency service.

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¹ New Information and Communication Technologies.

² http://www.agence-nationale-recherche.fr/?Projet=ANR-11-TECS-0010.

³ Pediatric Emergency Services (PES).

1

Logistics Engineering

Hayfa Zgaya; Slim Hammadi

Abstract

Today, in order to be successful, it is no longer enough to manage an organization relying exclusively on local performance objectives and in a compartmentalized way. Rather, organizations have to be managed in a transversal way, considering all possible options. Hence, the term logistics which has mainly been used in military language to refer to the art of combining all means of transport, supplies and housing facilities so as to conveniently manage the physical and information flows throughout a chain, called a logistics chain (LC), which starts at the provider’s supplier and finishes at the client’s customer. This is translated in terms of reliability and transfer speed of physical and information flows in order to attain the aims of the organization. In other words, logistics is interested in the planning and efficient follow-up of physical and information flows in order to satisfy a need. It consists of all the activities that aim to select a set of resources at the lowest cost, for the purpose of conforming to customers’ requirements.

Keywords

Decision-making; ISL; Logistics engineering; Modeling; Optimization; Origin; Process; Quality/logistics convergence; SC and LN; Supply Chain; System

1.1 Introduction

Today, in order to be successful, it is no longer enough to manage an organization relying exclusively on local performance objectives and in a compartmentalized way [LAU 04]. Rather, organizations have to be managed in a transversal way, considering all possible options. Hence, the term logistics [SOH 11] which has mainly been used in military language to refer to the art of combining all means of transport, supplies and housing facilities so as to conveniently manage the physical and information flows throughout a chain, called a logistics chain (LC), which starts at the provider’s supplier and finishes at the client’s customer. This is translated in terms of reliability and transfer speed of physical and information flows in order to attain the aims of the organization. In other words, logistics is interested in the planning and efficient follow-up of physical and information flows in order to satisfy a need. It consists of all the activities that aim to select a set of resources at the lowest cost, for the purpose of conforming to customers’ requirements.

Logistics comes down to the following five questions:

− What? Which products to deliver?

− How many? In what quantities?

− Where? In which places?

− When? At which moments?

− How? With what means?

The need for a logistics administration emerges from economic and social evolution. Today, this evolution needs innovative modeling, optimization and implementation techniques. The problem with the usual representation models of Logistics Systems (LS) derives from their inability to gather the structural and functional dimensions on the same diagram, which are by essence, transversal and complex [JOU 05]. These usual models exclusively juxtapose structural or functional bricks, but never both at the same time. For example, actors are never represented together with activities. Today, in order to be competitive, an organization must have a precise and comprehensive view of not only its internal distribution but also the complete logistics environment in which it exists (suppliers, customers, competitors, etc.). All the elements must therein be present in order to take into consideration all of the parameters for local optimization of the structure and