Planification et affectation de ressources dans les réseaux de soin : analogie avec le problème du bin packing, proposition de méthodes approchées

The presented work is about optimization of the hospital system. An existing solution is the pooling of resources within the same territory. This may involve different forms of cooperation between several hospitals. Various problems are defined at the decision level : strategic, tactical or operational ; and at the modeling level : macroscopic, mesoscopic and microscopic. Problems of sizing, planning and scheduling may be considered. We define the problem of activities planning with resource allocation. Several cases are dissociated : either human resources are under infinite capacity, or they are under limited capacity and their assignment on a place is given, or they are under limited capacity and their assignment is a variable. These problems are specified and mathematically formalized. All thes problems are compared to a bin packing problem : the classical problem of bin packing is used for the problem where human resources are under infinite capacity, the bin packing problem with interdependencies is used in the two other cases. The bin packing problem with incompatibilities is defined. Many resolution methods have been proposed for the bin packing problem. We make several propositions including a hierarchical coupling between heuristic and metaheuristic. Single based metaheuristics and a population based metaheuristic, the particle swarm optimization, are used. This proposition requires a new encoding inspired by permutation problems. This method gives very good results to solve instances of the bin packing problem. It is easy to apply : it combines already known methods. With the proposed coupling, the new constraints to be considered need to be integrated only on the heuristic level. The running of the metaheuristic is the same. Thus, our method is easily adaptable to the problem of activities planning with resource allocation. For big instances, the solver used as a reference returns only an interval of solutions. The results of our method are once again very promising : the obtained solutions are better than the upper limit returned by the solver. It is possible to adapt our method on more complex issues through integration into the heuristic of the new constraints to consider. It would be particularly interesting to test these methods on real hospital authorities to assess their significance.Les travaux de thèse présentés s’intéressent à l’optimisation des systèmes hospitaliers. Une solution existante est la mutualisation de ressources au sein d’un même territoire. Cela peut passer par différentes formes de coopération dont la Communauté Hospitalière de Territoire. Différents problèmes sont définis en fonction du niveau de décision : stratégique, tactique ou opérationnel ; et du niveau de modélisation : macroscopique, mesoscopique et microscopique. Des problèmes de dimensionnement, de planification et d’ordonnancement peuvent être considérés. Nous définissons notamment le problème de planification d’activités avec affectation de ressources. Plusieurs cas sont dissociés : soit les ressources humaines sont à capacité infinie, soit elles sont à capacité limitée et leur affectation sur site est une donnée, soit elles sont à capacité limitée et leur affectation sur site est une variable. Ces problèmes sont spécifiés et formalisés mathématiquement. Tous ces problèmes sont comparés à un problème de bin packing : le problème du bin packing de base pour le problème où les ressources humaines sont à capacité infinie, le problème du bin packing avec interdépendances dans les deux autres cas. Le problème du bin packing avec incompatibilités est ainsi défini. De nombreuses méthodes de résolution ont déjà été proposées pour le problème du bin packing. Nous faisons plusieurs propositions dont un couplage hiérarchique entre une heuristique et une métaheuristique. Des métaheuristiques basées individu et une métaheuristique basée population, l’optimisation par essaim particulaire, sont utilisées. Cette proposition nécessite un nouveau codage inspiré des problèmes de permutation d’ordonnancement. Cette méthode donne de très bons résultats sur les instances du problème du bin packing. Elle est simple à appliquer : elle couple des méthodes déjà connues. Grâce au couplage proposé, les nouvelles contraintes à considérer nécessitent d’être intégrées uniquement au niveau de l’heuristique. Le fonctionnement de la métaheuristique reste le même. Ainsi, notre méthode est facilement adaptable au problème de planification d’activités avec affectation de ressources. Pour les instances de grande taille, le solveur utilisé comme référence ne donne qu’un intervalle de solutions. Les résultats de notre méthode sont une fois encore très prometteurs : les solutions obtenues sont meilleures que la borne supérieure retournée par le solveur. Il est envisageable d’adapter notre méthode sur d’autres problèmes plus complexes par intégration dans l’heuristique des nouvelles contraintes à considérer. Il serait notamment intéressant de tester ces méthodes sur de réelles instances hospitalières afin d’évaluer leur portée

Medical Imaging : Exams Planning and Resource Assignment : Hybridization of a Metaheuristic and a List Algorithm

The presented work is about optimization of the hospital system. An existing solution is the pooling of resources within the same territory. This may involve different forms of cooperation between several hospitals. Problems of sizing, planning and scheduling may be considered. We define the problem of activities planning with resource assignment. To solve this problem, we propose a hybridization between a metaheuristic and a list algorithm. Single based metaheuristics are used. This proposition requires a new encoding inspired by permutation problems. This method is easy to apply: it combines already known methods. With the proposed hybridization, the constraints to be considered only need to be integrated into the list algorithm. For big instances, the solver used as a reference returns only lower and upper bounds. The results of our method are very promising. It is possible to adapt our method on more complex issues through integration into the list algorithm of the constraints. It would be particularly interesting to test these methods on real hospital authorities to assess their significance

Solving a multi-periods job-shop scheduling problem using a generic decision support tool

In this paper a generic and modular decision support tool developed to solve different planning, assignment or scheduling problems is presented. The utilization of this tool is illustrated by solving a real world multi-period job-shop scheduling problem proposed by a case study company which produces refrigerated foodservice equipment. The case study company problem and a list algorithm developed to integrate the proposed tool for this particular problem are presented. Preliminary results show that the proposed tool can be effectively used to solve the company problem. Besides the problem described in this paper, the proposed tool was used in the past to solve two other problems. Thus, it is demonstrated that the proposed tool can be easily adapted to several different planning or scheduling problems variants, overcoming the lack of flexibility generally associated to more problem-tailored methods proposed in the literature

Développement d’un outil d’aide à la décision générique pour les systèmes de production

Les systèmes de production actuels sont de plus en plus complexes : les produits fabriqués sont de plus en plus techniques, les moyens de production de plus en plus précis, les règles de gestion de plus en plus élaborées. Les outils d’aide à la décision sont indispensables afin de guider le pilotage de ces systèmes, que ce soit au niveau stratégique pour dimensionner le système, au niveau tactique pour le piloter et/ou planifier les activités avec affectation de ressources ou au niveau opérationnel pour ordonnancer les activités. Nous proposons de développer un outil d’aide à la décision générique et modulaire

Bin Packing Problem with priorities and incompatibilities using PSO: application in a health care community

The present work deals with the Hospital Group of Territory problem. The objective of the cooperation between these health institutions is to provide a better treatment other. To do so, these entities pool their means together. Our goal is to propose efficient methods to assign the different operations to the periods and resources, considering resources compatibilities and due dates. We consider this problem as an extension of the classical Bin Packing Problem. We propose a Particle Swarm Optimization to solve this problem using a hybridization proposed by Klement et al. (2017). The results show the interest of the proposed PSO for this kind of problem

Tabu Search Algorithm for Single and Multi-model Line Balancing Problems

This paper deals with the assembly line balancing issue. The considered objective is to minimize the weighted sum of products’ cycle times. The originality of this objective is that it is the generalization of the cycle time minimization used in single-model lines (SALBP) to the multi-model case (MALBP). An optimization algorithm made of a heuristic and a tabu-search method is presented and evaluated through an experimental study carried out on several and various randomly generated instances for both the single and multiproduct cases. The returned solutions are compared to optimal solutions given by a mathematical model from the literature and to a proposed lower bound inspired from the classical SALBP bound. The results show that the algorithm is high performing as the average relative gap between them is quite low for both problems

Process optimisation using collaborative robots - comparative case study

Human Robot Collaboration is seen as a significant feature of Industry 4.0 implementation. Collaborative robots (cobots) are supposed to deliver superior process performance, which was so far achieved through the application of Lean Manufacturing techniques. The following case study built around the assembly process of a pneumatic cylinder, tends to analyse not only the actual benefits of cobot implementation, but also the success factors, in conjunction with Lean Manufacturing usage. Finally, this paper suggests a draft method towards the successful integration of cobot

A generic decision support tool to planning and assignment problems: Industrial application & Industry 4.0

Decision support tools are essential to help the management of industrial systems at different levels: strategic to size the system; tactical to plan activities or assign resources; operational to schedule activities. We present a generic and modular decision support tool to solve different problems of planning, assignment, scheduling or lot-sizing. Our tool uses a hybridization between a metaheuristic and a list algorithm. The specification of the considered problem is considered into the list algorithm. Several tactical and operational problems have been solved with our tool: a problem of planning activities with resources assignment for hospital systems, a lot-sizing and scheduling problem taking into account the setup time for plastic injection, and a scheduling problem with precedence constraints. At the strategic level, this tool can also be used as part of the Industry 4.0 to design reconfigurable production systems. This paper summarizes some problems solved with the proposed tool, and presents the evolution of our tool

Un outil d’aide à la décision générique pour des problèmes de planification et d’ordonnancement : Applications industrielles & Usine du futur

Les outils d’aide à la décision sont essentiels pour aider au management des systèmes industriels à différents niveaux : stratégique pour dimensionner le système, tactique pour planifier les activités ou affecter les ressources, opérationnel pour ordonnancer les activités. Nous présentons un outil d’aide à la décision générique et modulaire pour résoudre différents problèmes de planification, d’affectation, d’ordonnancement ou de lot-sizing. Plusieurs problèmes tactiques et opérationnels ont déjà été résolus avec notre outil : un problème de planification d’activités avec affectation de ressources pour les systèmes hospitaliers, un problème d’ordonnancement et de lot-sizing avec prise en compte des temps de set-up pour l’injection plastique et un problème d’ordonnancement avec contraintes de précédence. Au niveau stratégique, cet outil peut aussi être utilisé dans le cadre de l’usine du futur pour concevoir des systèmes de production reconfigurables

Causality learning approach for supervision in the context of Industry 4.0

In order to have a full control on their processes, companies need to ensure real time monitoring and supervision using Key performance Indicators (KPI). KPIs serve as a powerful tool to inform about the process flow status and objectives’ achievement. Although experts are consulted to analyze, interpret, and explain KPIs’ values in order to extensively identify all influencing factors; this does not seem completely guaranteed if they only rely on their experience. In this paper, the authors propose a generic causality learning approach for monitoring and supervision. A causality analysis of KPIs’ values is hence presented, in addition to a prioritization of their influencing factors in order to provide a decision support. A KPI prediction is also suggested so that actions can be anticipated