Operations research and enterprise systems: Third International Conference, ICORES 2014, Angers, France, March 6-8, 2014, Revised Selected Papers

Depto. de Estadística e Investigación OperativaFac. de Ciencias MatemáticasTRUEpu

Multi-attribute Performance Models for Small Manufacturing Enterprises

Nowadays, there are huge environmental changes in the business world. These changes have resulted in tremendous growth and opportunities for new markets but also in challenges that threaten the operations and survival of firms. These competitive pressures are driving firms to re-evaluate their competitive strategies, supply chains, and manufacturing technologies in order to improve performance and survive long term. Small and medium-sized enterprises also face these challenges, which influence their operations and existence. They are significantly constrained by remarkable limitations in terms of financial resources as well as non-financial factors, such as informal strategic decisions and actions. Reports have revealed that small enterprises are vulnerable to failure. Only around 50% of them in Canada and the United States survive for more than five years. Focusing on financial measures alone is not a good strategy for guaranteeing the long term success of a business. The absence of objective and formal strategic decisions and performance measurement systems in small enterprises increase their chances of failure. Therefore, models have been developed that assess and translate informal and qualitative in small enterprises into measurable, quantitative data. This allows for the evaluation and measurement of decisions and actions, which increases the chances of success for a small enterprise. Using the multi-criteria decision methodology (MCDM) allows for the following: integrating and linking various levels of decision-making and processes, converting subjective information into objective decision making, executing individual business preferences, and ranking strategic attributes and business processes. An analytical hierarchy process approach was first used to develop a simple model. Using the case of a small manufacturing enterprise, it was found that the business did not emphasize financial measures alone; they also paid attention to non-financial measures, such as reliability and responsiveness. It was observed that the business was willing to rank strategic attributes and supporting business processes each time there was a change in the external environment. Finally, an analytical network process approach to express the links and effects among the supply chains of a small business were established, and an overall business performance formula was created

Proceedings of the First Karlsruhe Service Summit Workshop - Advances in Service Research, Karlsruhe, Germany, February 2015 (KIT Scientific Reports ; 7692)

Since April 2008 KSRI fosters interdisciplinary research in order to support and advance the progress in the service domain. KSRI brings together academia and industry while serving as a European research hub with respect to service science. For KSS2015 Research Workshop, we invited submissions of theoretical and empirical research dealing with the relevant topics in the context of services including energy, mobility, health care, social collaboration, and web technologies

Robust and stochastic approaches to network capacity design under demand uncertainty

This thesis considers the network capacity design problem with demand uncertainty using the stochastic, robust and distributionally robust stochastic optimization approaches (DRSO). Network modeling in itself has found wide areas of application in most fields of human endeavor. The network would normally consist of source (origin) and sink (destination) nodes connected by arcs that allow for flows of an entity from the origin to the destination nodes. In this thesis, a special type of the minimum cost flow problem is addressed, the multi-commodity network flow problem. Commodities are the flow types that are transported on a shared network. Offered demands are, for the most part, unknown or uncertain, hence a model that immune against this uncertainty becomes the focus as well as the practicability of such models in the industry. This problem falls under the two-stage optimization framework where a decision is delayed in time to adjust for the first decision earlier made. The first stage decision is called the "here and now", while the second stage traffic re-adjustment is the "wait and see" decision. In the literature, the decision-maker is often believed to know the shape of the uncertainty, hence we address this by considering a data-driven uncertainty set. The research also addressed the non-linearity of cost function despite the abundance of literature assuming linearity and models proposed for this. This thesis consist of four main chapters excluding the "Introduction" chapter and the "Approaches to Optimization under Uncertainty" chapter where the methodologies are reviewed. The first of these four, Chapter 3, proposes the two models for the Robust Network Capacity Expansion Problem (RNCEP) with cost non-linearity. These two are the RNCEP with fixed-charge cost and RNCEP with piecewise-linear cost. The next chapter, Chapter 4, compares the RNCEP models under two types of uncertainties in order to address the issue of usefulness in a real world setting. The resulting two robust models are also comapared with the stochastic optimization model with distribution mean. Chapter 5 re-examines the earlier problem using machine learning approaches to generate the two uncertainty sets while the last of these chapters, Chapter 6, investigates DRSO model to network capacity planning and proposes an efficient solution technique

Ordonnancement des trains dans une gare complexe et à forte densité de circulation

This thesis focuses on the trains platforming problem within busy and complex railway stations and aims to develop a computerized dispatching support tool for railway station dispatchers to generate a full-day conflict-free timetable. The management of rail traffic in stations requires careful scheduling to fit to the existing infrastructure, while avoiding conflicts between large numbers of trains and satisfying safety or business policy and objectives. Based on operations research techniques and professional railway expertise, we design a generalized mathematical model to formalize the trains platforming problem including topology of railway station, trains' activities, dispatching constraints and objectives. As a large-scale problem, full-day platforming problem is decomposed into tractable sub-problems in time order by cumulative sliding window algorithm. Each sub-problem is solved by branch-and-bound algorithm implemented in CPLEX. To accelerate calculation process of sub-problems, tri-level optimization model is designed to provide a local optimal solution in a rather short time. This local optimum is provided to branch-and bound algorithm as an initial solution.This system is able to verify the feasibility of tentative timetable given to railway station. Trains with unsolvable conflicts will return to their original activity managers with suggestions for the modification of arrival and departure times. Time deviations of commercial trains' activities are minimized to reduce the delay propagation within the whole railway networks.Cette thèse porte sur l'ordonnancement des trains dans les gares complexes en forte densité de circulation. L'objet se situe à la réalisation d'un outil pour aider les managers de la gare à générer un tableau des horaires sans-conflits dans un journée. Le management des circulations ferroviaires dans la gare demande l'ordonnancement soigneux pour adapter les ressources limités, en évitant les conflits entre les trains et satisfaisant l'objectif et les politiques économiques et de la sécurité en même temps. D'après les méthodes appliquées en recherche opérationnelle et les expériences professionnelles, une modèle mathématique applicable aux gares différentes est construit pour formaliser le problème de l'ordonnancement des trains contenant la topologie de la gare, activités des trains, contraintes de planification et objectives. Comme un problème à grande échelle, l'ordonnancement des trains dans un journée est décomposé en sous-problèmes traitables dans l'ordre du temps par sliding window algorithme accumulé. Chaque sous-problème est résolu par branch-and-bound de CPLEX. Afin d'accélérer le calcul des sous-problèmes, tri-level optimisation méthode est construit pour offrir une solution optimale locale dans un temps de calcul assez court. Cette solution est donnée à branch-and-bound comme une solution initiale.Ce système consiste à vérifier la faisabilité des horaires donnés à la gare. Les trains avec les conflits insolvables sont retournés à l'origine de ces trains avec les modifications des heures proposées. Déviations des trains commerciaux sont minimisées pour diminuer la propagation du délai dans le réseau ferroviaire

Safety and Reliability - Safe Societies in a Changing World

The contributions cover a wide range of methodologies and application areas for safety and reliability that contribute to safe societies in a changing world. These methodologies and applications include: - foundations of risk and reliability assessment and management - mathematical methods in reliability and safety - risk assessment - risk management - system reliability - uncertainty analysis - digitalization and big data - prognostics and system health management - occupational safety - accident and incident modeling - maintenance modeling and applications - simulation for safety and reliability analysis - dynamic risk and barrier management - organizational factors and safety culture - human factors and human reliability - resilience engineering - structural reliability - natural hazards - security - economic analysis in risk managemen