Ce travail de doctorat aborde trois thématiques: (i) l’ordonnancement des systèmes de production à cheminements multiples et plus particulièrement le Job-shop soumis à un seuil de consommation énergétique ; (ii) la résolution d’un problème d’ordonnancement et d’affectation dans le contexte d’un système flexible de production sous la forme d’un Job-shop Flexible ; (iii) les méthodes de couplage entre la simulation et l’optimisation dans le cadre des problèmes de Job-shop avec incertitude. Différentes approches de résolutions sont appliquées pour chaque problème : une formalisation mathématique est proposée ainsi que plusieurs métaheuristiques (GRASP×ELS, VNS, MA, NSGA-II hybride et GRASP×ELS itéré) pour le Job-shop avec contrainte énergétique. Une extension du GRASP×ELS, notée GRASP-mELS, est ensuite proposée pour résoudre un problème de Job-shop Flexible ; différents systèmes de voisinages utilisés lors des phases de diversification et d’intensification des solutions sont également présentés. Les résultats montrent que les performances du GRASP-mELS sont comparables à celles de la littérature à la fois en terme de qualité et de temps de calcul. La dernière thématique concerne les méthodes de couplage entre optimisation et simulation avec deux problèmes étudiés : 1) un Job-shop Stochastique et 2) un Job-shop Flexible Réactif. Les méthodes de résolution reposent sur des métaheuristiques et sur le langage de simulation SIMAN intégré dans l’environnement ARENA. Les résultats montrent que les deux approches permettent de mieux prendre en compte les aspects aléatoires liés à la réalité des systèmes de production. / This doctoral work addresses three themes: (i) the scheduling of multi-path production systems and more specifically the Job-shop subjected to a power threshold; (ii) the resolution of a scheduling and assignment problem in the context of a flexible production system modelled as a Flexible Job-shop; (iii) the coupling methods between simulation and optimisation in the context of Job-shop problems with uncertainty. Different resolution approaches are applied for each problem: a mathematical formalisation is proposed as well as several metaheuristics (GRASP×ELS, VNS, MA, hybrid NSGA-II and iterated GRASP×ELS) for the Job-shop with power requirements. An extension of the GRASP×ELS, denoted GRASP-mELS, is then proposed to solve a Flexible Job-shop problem; different neighbourhood systems used during the diversification and intensification phases of solutions are also presented. The results show that the performances of the GRASP-mELS are comparable to the methods presented in the literature both in terms of quality of solutions and computation time. The last topic concerns the coupling methods between optimisation and simulation with two problems: 1) a Stochastic Job-shop and 2) a Reactive Flexible Job-shop. The resolution methods are based on metaheuristics and the SIMAN simulation language integrated in the ARENA environment. The results show that both approaches allow to better take into account the random aspects related to the reality of production systems.

This article seeks to enhance acceptance of the profile distance method (PDM) in decision support systems. The PDM is a multiple attributive based decision making as well as a multiple method approach to support complex decision making and uses a heuristic to avoid computationally complex global optimization. We elaborate on the usability of the method and question the heuristic used. We present a bisection algorithm, which efficiently supports the discovery of transition profiles needed in a user-friendly and practical application of the method. Additionally, we provide empirical evidence showing that the proposed heuristic is efficient and delivers results within 5% of the global optimizer for a wide range of data sets.

L’optimisation des structures est un processus essentiel dans la conception des systèmes mécaniques et électroniques. Cette thèse s’intéresse à la résolution des problèmes mono-objectifs et multi-objectifs des structures mécaniques et mécatroniques. En effet, les industriels ne sont pas seulement préoccupés à améliorer les performances mécaniques des pièces qu’ils conçoivent, mais ils cherchent aussi à optimiser leurs poids, leurs tailles, ainsi que leurs coûts de production. Pour résoudre ce type de problème, nous avons fait appel à des métaheuristiques robustes qui nous permettent de minimiser le coût de production de la structure mécanique et de maximiser le cycle de vie de la structure. Alors que des méthodes inappropriées de l’évolution sont plus difficiles à appliquer à des modèles mécaniques complexes en raison de temps calcul exponentiel. Il est connu que les algorithmes génétiques sont très efficaces pour les problèmes NP-difficiles, mais ils sont très lourds et trop gourmands quant au temps de calcul, d’où l’idée d’hybridation de notre algorithme génétique par l’algorithme d’optimisation par essaim de particules (PSO) qui est plus rapide par rapport à l’algorithme génétique (GA). Dans notre expérimentation, nous avons obtenu une amélioration de la fonction objectif et aussi une grande amélioration de la minimisation de temps de calcul. Cependant, notre hybridation est une idée originale, car elle est différente des travaux existants. Concernant l’avantage de l’hybridation, il s’agit généralement de trois méthodes : l’hybridation en série, l’hybridation en parallèle et l’hybridation par insertion. Nous avons opté pour l’hybridation par insertion par ce qu’elle est nouvelle et efficace. En effet, les algorithmes génétiques se composent de trois étapes principales : la sélection, le croisement et la mutation. Dans notre cas, nous remplaçons les opérateurs de mutation par l’optimisation par essaim de particules. Le but de cette hybridation est de réduire le temps de calcul ainsi que l’amélioration la solution optimale. / This thesis focuses on solving single objective problems and multiobjective of mechanical and mechatronic structures. The optimization of structures is an essential process in the design of mechanical and electronic systems. Industry are not only concerned to improve the mechanical performance of the parts they design, but they also seek to optimize their weight, size and cost of production. In order to solve this problem we have used Meta heuristic algorithms robust, allowing us to minimize the cost of production of the mechanical structure and maximize the life cycle of the structure. While inappropriate methods of evolution are more difficult to apply to complex mechanical models because of exponential calculation time. It is known that genetic algorithms are very effective for NP-hard problems, but their disadvantage is the time consumption. As they are very heavy and too greedy in the sense of time, hence the idea of hybridization of our genetic algorithm optimization by particle swarm algorithm (PSO), which is faster compared to the genetic algorithm (GA). In our experience, it was noted that we have obtained an improvement of the objective function and also a great improvement for minimizing computation time. However, our hybridization is an original idea, because it is a different and new way of existing work, we explain the advantage of hybridization and are generally three methods : hybridization in series, parallel hybridization or hybridization by insertion. We opted for the insertion hybridization it is new and effective. Indeed, genetic algorithms are three main parts : the selection, crossover and mutation. In our case,we replace the operators of these mutations by particle swarm optimization. The purpose of this hybridization is to reduce the computation time and improve the optimum solution.

In nature animal species often exist in groups. We talk of insect swarms, flocks of birds, packs of lions, herds of wildebeest etc. These are characterised by individuals interacting by following their own rules, privy only to local information. Robotic swarms or simulations can be used explore such interactions. Mathematical formulations can be constructed that encode similar ideas and allow us to explore the emergent group behaviours. Some behaviours show characteristics reminiscent of the phenomena of criticality. A bird flock may show near instantaneous collective shifts in direction: velocity changes that appear to correlated over distances much larger individual separations. Here we examine swarm systems inspired by flocks of birds and the role played by criticality. The first system, Particle Swarm Optimisation (PSO), is shown to behave optimally when operating close to criticality. The presence of a critical point in the algorithm’s operation is shown to derive from the swarm’s properties as a random dynamical system. Empirical results demonstrate that the optimality lies on or near this point. A modified PSO algorithm is presented which uses measures of the swarm’s diversity as a feedback signal to adjust the behaviour of the swarm. This achieves a statistically balanced mixture of exploration and exploitation behaviours in the resultant swarm. The problems of stagnation and parameter tuning often encountered in PSO are automatically avoided. The second system, Swarm Chemistry, consists of heterogeneous particles combined with kinetic update rules. It is known that, depending upon the parametric configuration, numerous structures visually reminiscent of biological forms are found in this system. The parameter set discovered here results in a cell-division-like behaviour (in the sense of prokaryotic fission). Extensions to the swarm system produces a swarm that shows repeated cell division. As such, this model demonstrates a behaviour of interest to theories regarding the origin of life.

Usability heuristics have been established for different uses and applications as general guidelines for user interfaces. These can affect the implementation of industry solutions and play a significant role regarding cost reduction and process efficiency. The area of electronic workflow document management (EWDM) solutions, also known as workflow, lacks a formal definition of usability heuristics. With the advent of new technologies such as mobile devices, defining a set of usability heuristics contributes to the adoption and efficiency of an EWDM system. Workflow usability has been evaluated for various industries. Most significantly research has been done for electronic healthcare records (EHR). In other areas such as the financial sector and educational institutions there is also some literature available but not as abundant as for EHR. This was identified as a possible research limitation. The general purpose of this research was to establish and validate an overarching set of usability heuristics for EWDM in general. This was approached by conducting a literature review and a survey on 32 workflow consultants from Hyland Software, Inc. Quantitative and qualitative data was collected focusing on the study’s main research question: “what usability heuristics should be defined to ensure the adoption and efficiency of a workflow implementation?" Findings based on regression testing and expert opinions have suggested a proposed set of usability heuristics. The final list consists of: adaptability to diverse platforms, user control, system feedback, intuitive interfaces, visibility on mobile devices, error management, help, and documentation.

L'objectif de ce travail consiste à réaliser un système dynamique d'aide aux déplacements multimodal pour les voyageurs équipés d'un système d'information tout en prenant en considération les usagers non équipés de ce type de système. Le travail est alors divisé en deux parties: Une partie conception et développement et une partie étude. La partie développement consiste à construire l'outil informatique d'aide aux déplacements grâce à une modélisation multi-agent et qui renvoie à l'usager un itinéraire qui satisfait ces besoins et ceux du réseau. La partie étude quant à elle, consiste en une approche plus théorique qui consiste à déterminer l'impact de l'information sur les coûts des itinéraires, l'impact de la réorientation des usagers vers les transports en commun sur le réseau routier ainsi que l'intérêt de passer vers des véhicules autonomes / The objective of this work consists on the realization of a dynamic guidance system in a multimodal network for users equipped with an information device while taking into account users that are not equipped with such devices. The work is organized into parts: a conception part and a theoretical study part. The conception part consists on the development of the guidance tool using a multi agent architecture. This tool assists users in their daily travels by giving them the itinerary that suits best not only their needs but also the overall network. The theoretical study emphasizes on how the performance of the network can be enhanced. To do so, three main studies will be presented: the impact of the information on the cost of the itineraries, the impact of the reorientation of users towards transportation systems on the road network and finally the benefits of introducing autonomous vehicles

Cette thèse s’intéresse aux problèmes d’emploi du temps d’universités. Ces problèmes sont rencontrés chaque année par les utilisateurs. Nous proposons des bornes inférieures, des méthodes heuristiques et des modèles de programmation mixte en nombres entiers et de programmation par contraintes. Nous traitons le problème d’emploi du temps d’examens et celui d’affectation des étudiants. Nous proposons de nouvelles méthodes et formulations et les comparons aux approches existantes. Nous proposons, pour le problème d’emploi du temps d’examens, une amélioration d’un modèle mathématique en nombres entiers qui permettra d’obtenir des solutions optimales. Ensuite, des bornes inférieures, une formulation plus compacte des contraintes et un modèle de programmation par contraintes sont proposés. Pour le problème d’emploi du temps d’examens à l’Université de Technologie de Compiègne, nous proposons une approche mémétique. Enfin, nous présentons un modèle mathématique pour le problème d’affectation des étudiants et nous étudions sa performance sur un ensemble d’instances réelles. / This thesis investigates university timetabling problems. These problems occur across universities and are faced each year by the practitioners. We propose new lower bounds, heuristic approaches, mixed integer and constraint programming models to solve them. We address the exam timetabling and the student scheduling problem. We investigate new methods and formulations and compare them to the existing approaches. For exam timetabling, we propose an improvement to an existing mixed integer programming model that makes it possible to obtain optimal solutions. Next, lower bounds, a more compact reformulation for constraints and a constraint programming model are proposed. For the exam timetabling problem at Université de Technologie de Compiègne, we designed a memetic approach. Finally, we present a new formulation for the student scheduling problem and investigate its performance on a set of real-world instances.

In den vergangenen Jahrzehnten konzentrierte sich das Operations Management auf Optimierungsstrategien, insbesondere wurden Meta-Heuristiken für das komplexe, kombinatorische Problem der ressourcenbegrenzten Ablaufplanung erforscht. In einfachen Worten gehört dieses Problem zu den NP-schweren Problemen, die einen derart großen Lösungsraum besitzen, der mittels Enumerationverfahren rechnerisch unlösbar ist. Daher erfordert die Exploration von optimalen Lösungen andere Methoden als Zufallssuchverfahren. Solche Suchalgorithmen in Meta-Heuristik starten mit einer oder mehreren Ausgangslösung und erkunden den Suchraum nach optimalen Lösungen. Jedoch stellen die existierenden Forschungsansätze zur Lösungssuche nur diejenigen Lösungen bereit, die ausschließlich unter den gegebenen Eingangsbedingungen optimal sind. Diese Eingabebedingungen definieren einen Lösungsraum, in dem alles nach Plan geht. Jedoch ist das in der Praxis sicherlich nicht der Fall. Wie wir sagen, der Wandel ist die einzige Konstante in dieser Welt. Risiken und Unsicherheiten begegnen stets im täglichen Leben. Die vorliegende Dissertation untersucht Optimierungsansätze unter Unsicherheit. Der Forschungsbeitrag ist zweigeteilt. Wie bereits gesagt, wurden Optimierungsstrategien zum Durchsuchen des Lösungsraums in den letzten Jahren stark erforscht. Obwohl es eine anerkannte Tatsache ist, dass die Verbesserung und die Leistung von Optimierungsstrategien stark mit den Initiallösungen korreliert, scheint die Literatur diesbezüglich inexistent, während zumeist auf die Entwicklung von meta-heuristischen Algorithmen wie Genetische Algorithmen und Particle-Swarm-Optimierung fokussiert wird. Die Initiallösungen werden durch simulationsbasierte Strategien entwickelt, die typischerweise gierige Regeln und ereignisbasierte Simulation nutzen. Allerdings verhalten sich kommerzielle Basis-Softwareprodukte meist als Black-Box und stellen keine Informationen über das interne Verhalten bereit. Außerdem erfordern derartige Softwareprodukte meist spezielle Architekturen und missachten Ressourcenbeschränkungen. Die vorliegende Studie diskutiert die ressourcenbeschränkte Projektplanung mit alternativen Modi und schlägt ein simulationsbasiertes Rahmenwerk vor, mit dem ein heuristisches Multi-Pass-Verfahren zur Verfügung gestellt wird. Das erweiterte Multi-Modus-Problem ist in der Lage, den Produktionsbereich in einer besseren Art und Weise nachzubilden, bei dem eine Aktivität von mehreren Ressourcen unterschiedlicher Qualifikation ausgeführt werden kann. Der vorgeschlagene Rahmen diskutiert die Leistung von Algorithmen und verwendet hierfür Benchmark-Instanzen. Das Verhalten verschiedener Projektnetze und deren Eigenschaften werden auch innerhalb des vorgeschlagenen Rahmenwerks bewertet. Darüber hinaus hilft das offene Rahmenwerk, besondere Eigenschaften von Aktivitäten zu analysieren, um deren Verhalten im Fall von Störungen zu prognostizieren. Die traditionellen Methoden der Risikoanalyse schlagen Slack-basierte Maßzahlen vor, um die Effizienz von Basisplänen zu bestimmen. Das Rahmenwerk wird weiter entwickelt, um mit diesem einen Prüfstand zu gestalten, mit dem nicht-reguläre Maßzahlen bestimmt werden können. Diese Maßnahmen werden als Robustheitsindikatoren bezeichnet und korrelieren mit der Verzögerung derartiger Multi-Modus-Probleme. Solche Leistungsmaße können genutzt werden, um die Wirksamkeit von Basisplänen zu bewerten und ihr Verhalten unter Unsicherheiten zu prognostizieren. Die Ergebnisse dieser Tests werden als modifizierte Zielfunktion verwendet, in der ein bi-objektives Leistungsmaß aus Durchlaufzeit und Robustheit eingesetzt wird, um die Effizienz der vorgeschlagenen Heuristiken zu testen. Da diese Leistungsmaße das Verhalten von Aktivitäten unter Störungen zeigen, werden diese auch genutzt, um die Formfaktoren und Puffergrößen für die Entwicklung eines stochastischen Modells zu bestimmen. Die Analyse der Projektergebnisse, durchgeführt mittels Monte-Carlo-Simulationen, unterstützt das Argument von Teilpuffern für die Modellierung von Aktivitätsdauern anstatt Ansätze mit Extrempuffern und PERT-beta-Schätzungen. / Over the past decades, researches in the field of operations management have focused on optimization strategies based on meta-heuristics for the complex-combinatorial problem of resource constrained scheduling. In simple terms, the solution for this particular problem categorized as NP-hard problem, exhibits a large search space, is computationally intractable, and requires techniques other than random search. Meta-heuristic algorithms start with a single or multiple solutions to explore and optimize using deterministic data and retrieve a valid optimum only under specified input conditions. These input conditions define a solution search space for a theoretical world undergoing no disturbance. But change is inherent to the real world; one is faced with risks and uncertainties in everyday life. The present study explores solution methodologies in the face of uncertainties. The contributions of this thesis are two-fold. As mentioned earlier, existing optimization strategies have been vigorously investigated in the past decade with respect to exploring large solution search space. Although, it is an established fact that the improvement and performance of optimization strategies is highly correlated with the initial solutions, existing literature regarding this area is not exhaustive and mostly focuses on the development of meta-heuristic algorithms such as genetic algorithms and particle swarm optimization. The initial solutions are developed through simulation based strategies mainly based on greedy rules and event based simulation. However, the available commercial softwares are primarily modeled as a black box and provide little information as to internal processing. Additionally, such planners require special architecture and disregard resource constraints. The present study discusses the multi-mode resource constrained scheduling problem and proposes a simulation-based framework to provide a multi-pass heuristic method. The extended version of multi-mode problem is able to imitate production floor in an improved manner where a task can be performed with multiple resources with certain qualifications. The performance of the proposed framework was analyzed using benchmark instances. The behavior of different project networks and their characteristics is also evaluated within the proposed framework. In addition, the open framework aids in determining the particular characteristic of tasks in order to analyze and forecast their behavior in case of disruptions. The traditional risk analysis techniques suggest slack-based measures in order to determine the efficiency of baseline schedules. The framework is further developed to design a test bench in order to determine non-regular performance measures named as robustness indicators which correlate with the delay of such cases as multi-mode problem. Such performance measures can be used to indicate the effectiveness of baseline schedules and forecast their behavior. The outputs of these tests are used to modify the objective function which uses makespan and robustness indicators as a bi-objective performance measure in order to test the efficiency of proposed heuristics. Furthermore, since these measures indicate the behavior of tasks under disruptions, they are utilized in order to determine the shape factors and buffers for the development of a stochastic model. The analysis of project outcomes performed through Monte-Carlo simulations supports the argument of partial buffer sizing for modeling activity duration estimates rather than extreme buffer approaches proposed via PERT-beta estimates.

The thesis focuses on rationality in decisions by managers. The terms rationality, irrationality and bounded rationality are defined in the first part. The current state of knowledge on the concept of bounded rationality in decision making is then followed by a specific consideration of managerial decision making. The chosen bounded rationality effects, including heuristics, are also described. The purpose of the second part of this study is to examine experimentally the differential uses of heuristics among the students of the Faculty of Management relative to completion of the Managerial Decision Making course. The effect of time to use of heuristics is examined as well.

This thesis is continuation of the bachelor thesis "Model of delivery routes and placement logistics centers with opportunities of their optimization". It is about distribution problems and specifically a vehicle routing problem. The aim of this thesis is finding a solution of the vehicle routing problem which will be used repeatedly in the firm. The main task is achieving the lowest costs (total kilometers) with maximum utilization of vehicle capacity; in such conditions that all requirements of logistics centers will be satisfied and maximal capacity of vehicle will be tolerated. For calculation was used a solver Gurobi 6.0.3 in system MPL for Windows 4.2, which won't, however, provide the optimal solution and problem solving takes too long time. Next for calculation was used heuristics insert method and is written by VBA (Visual Basic for Applications) in MS Excel. Finally, there is a comparison of these methods with the original solution of the vehicle routing plan and solution of the bachelor thesis. Then the computational experiment was done, which tested effect to result, if other distribution center (starting point) will be bulit. The computational experiment was consist from heuristic insert method, solver Gurobi and heuristic saving algorithm from bachelor thesis.