129 research outputs found
An Analytical Solution for Probabilistic Guarantees of Reservation Based Soft Real-Time Systems
We show a methodology for the computation of the probability of deadline miss
for a periodic real-time task scheduled by a resource reservation algorithm. We
propose a modelling technique for the system that reduces the computation of
such a probability to that of the steady state probability of an infinite state
Discrete Time Markov Chain with a periodic structure. This structure is
exploited to develop an efficient numeric solution where different
accuracy/computation time trade-offs can be obtained by operating on the
granularity of the model. More importantly we offer a closed form conservative
bound for the probability of a deadline miss. Our experiments reveal that the
bound remains reasonably close to the experimental probability in one real-time
application of practical interest. When this bound is used for the optimisation
of the overall Quality of Service for a set of tasks sharing the CPU, it
produces a good sub-optimal solution in a small amount of time.Comment: IEEE Transactions on Parallel and Distributed Systems, Volume:27,
Issue: 3, March 201
Diseño de un aplicativo para la programaciĂłn de salas de cirugĂa teniendo en cuenta el impacto de la reprogramaciĂłn
Este proyecto propone una metodologĂa de soluciĂłn para el problema de ProgramaciĂłn de Salas de CirugĂa (ORS por sus siglas en inglĂ©s Operating Room Scheduling Problem) basada en un caso de estudio dentro de una clĂnica de cuarto nivel ubicada en BogotĂĄ, Colombia. Actualmente, las decisiones crĂticas dentro de este problema son: la estimaciĂłn de los tiempos quirĂșrgicos estĂĄndar y la asignaciĂłn de las cirugĂas para cada perĂodo, sala y dĂa. Estas decisiones son tomadas a partir de la experiencia de los funcionarios de programaciĂłn de la clĂnica, asĂ como de la experiencia de los cirujanos, por lo que esto puede generar reprogramaciones y un bajo porcentaje de utilizaciĂłn de las salas.
El objetivo de este trabajo es diseñar una aplicaciĂłn que determine la programaciĂłn de salas de cirugĂa que permita maximizar su utilizaciĂłn, teniendo en cuenta el impacto de las cirugĂas reprogramadas.This project proposes a resolution strategy for the Operating Room Scheduling Problem (ORS) based on a case study of a fourth level clinic in BogotĂĄ, Colombia. Currently, the critical operational decisions within the ORS are: the estimation of surgical times and the assignment of surgeries by periods, rooms and days. These decisions are made according to the clinical programmersâ knowledge and surgeonsâ experience, which can lead to overtime and a low proportion of OR utilization.
The aim of this process is to design an application that determines the operational programming of the surgery rooms of the clinic, seeking to maximize its use, considering the impact of rescheduled surgeries.Ingeniero (a) IndustrialPregrad
D-SPACE4Cloud: Towards Quality-Aware Data Intensive Applications in the Cloud
The last years witnessed a steep rise in data generation worldwide and, consequently, the widespread adoption of software solutions claiming to support data intensive applications. Competitiveness and innovation have strongly benefited from these new platforms and methodologies, and there is a great deal of interest around the new possibilities that Big Data analytics promise to make reality. Many companies currently en- gage in data intensive processes as part of their core businesses; however, fully embracing the data-driven paradigm is still cumbersome, and es- tablishing a production-ready, fine-tuned deployment is time-consuming, expensive, and resource-intensive. This situation calls for novel models and techniques to streamline the process of deployment configuration for Big Data applications. In particular, the focus in this paper is on the rightsizing of Cloud deployed clusters, which represent a cost-effective alternative to installation on premises. We propose a novel tool, inte- grated in a wider DevOps-inspired approach, implementing a parallel and distributed simulation-optimization technique that efficiently and effec- tively explores the space of alternative resource configurations, seeking the minimum cost deployment that satisfies predefined quality of service constraints. The validity and relevance of the proposed solution has been thoroughly validated in a vast experimental campaign including different applications and Big Data platforms
Energy Management
Forecasts point to a huge increase in energy demand over the next 25 years, with a direct and immediate impact on the exhaustion of fossil fuels, the increase in pollution levels and the global warming that will have significant consequences for all sectors of society. Irrespective of the likelihood of these predictions or what researchers in different scientific disciplines may believe or publicly say about how critical the energy situation may be on a world level, it is without doubt one of the great debates that has stirred up public interest in modern times. We should probably already be thinking about the design of a worldwide strategic plan for energy management across the planet. It would include measures to raise awareness, educate the different actors involved, develop policies, provide resources, prioritise actions and establish contingency plans. This process is complex and depends on political, social, economic and technological factors that are hard to take into account simultaneously. Then, before such a plan is formulated, studies such as those described in this book can serve to illustrate what Information and Communication Technologies have to offer in this sphere and, with luck, to create a reference to encourage investigators in the pursuit of new and better solutions
Decomposition-Based Integer Programming, Stochastic Programming, and Robust Optimization Methods for Healthcare Planning, Scheduling, and Routing Problems
RĂSUMĂ : Il existe de nombreuses applications de planification, dâordonnancement et de confection de
tournées dans les systÚmes de santé. La résolution efficace de ces problÚmes peut aider les responsables de la santé à fournir des services de meilleure qualité, en utilisant efficacement les ressources médicales disponibles. En raison de la nature combinatoire de ces problÚmes, dans de nombreux cas, les algorithmes de programmation en nombres entiers standards dans les logiciels commerciaux de programmation mathématique tels que CPLEX et Gurobi ne
peuvent pas rĂ©soudre efficacement les modĂšles correspondants. Dans cette thĂšse, nous Ă©tudions trois problĂšmes de planification, dâordonnancement et de confection de tournĂ©es des soins de santĂ© et proposons des approches Ă base de dĂ©composition utilisant la programmation
en nombres entiers, la programmation stochastique et une mĂ©thode dâoptimisation robuste. Le premier article de cette thĂšse prĂ©sente un problĂšme intĂ©grĂ© de planification et dâordonnancement dans le cadre des salles dâopĂ©ration. Cette situation implique dâoptimiser lâordonnancement et lâaffectation des chirurgies aux diffĂ©rentes salles dâopĂ©ration, sur un horizon de planification Ă court terme. Nous avons pris en compte les heures de travail quotidiennes
maximales des chirurgiens, le temps de nettoyage obligatoire allouĂ© lors du passage de cas infectieux Ă des cas non infectieux et le respect des dates limites des chirurgies. Nous avons aussi empĂȘchĂ© le chevauchement des chirurgies effectuĂ©es par le mĂȘme chirurgien. Nous avons formulĂ© le problĂšme en utilisant un modĂšle de programmation mathĂ©matique et dĂ©veloppĂ© un algorithme «branch-and-price-and-cut» basĂ© sur un modĂšle de programmation par
contraintes pour le sous-problĂšme. Nous avons mis en place des rĂšgles de dominance et un algorithme de dĂ©tection dâinfaillibilitĂ© rapide. Cet algorithme, basĂ© sur le problĂšme du sac Ă dos multidimensionnel, nous permet dâamĂ©liorer lâefficacitĂ© du modĂšle de programmation de
contraintes. Les rĂ©sultats montrent que notre mĂ©thode prĂ©sente un Ă©cart Ă lâoptimum moyen de 2,81%, ce qui surpasse de maniĂšre significative la formulation mathĂ©matique compacte dans la littĂ©rature. Dans la deuxiĂšme partie de cette thĂšse, pour la premiĂšre fois, nous avons Ă©tudiĂ© lâoptimisation des problĂšmes de tournĂ©es de vĂ©hicules avec visites synchronisĂ©es (VRPS) en tenant compte
de stochasticitĂ© des temps de dĂ©placement et de service. En plus dâenvisager un problĂšme dâordonnancement des soins de santĂ© Ă domicile, nous introduisons un problĂšme dâordonnancement des salles dâopĂ©ration avec des durĂ©es stochastiques qui est une nouvelle application de VRPS. Nous avons modĂ©lisĂ© les VRPS qui ont des durĂ©es stochastiques en programmation stochastique Ă deux niveaux avec des variables entiĂšres dans les deux niveaux. Lâavantage du modĂšle proposĂ© est que, contrairement aux modĂšles dĂ©terministes de la littĂ©rature VRPS, il nâa pas de contraintes «big-M». Cet avantage entraine en contrepartie la prĂ©sence dâun grand nombre de variables entiĂšres dans le second niveau. Nous avons prouvĂ© que les contraintes
dâintĂ©gralitĂ© sur les variables du deuxiĂšme niveau sont triviales ce qui nous permet dâappliquer lâalgorithme «L-shaped» et son implĂ©mentation branch-and-and-cut pour rĂ©soudre le problĂšme. Nous avons amĂ©liorĂ© le modĂšle en dĂ©veloppant des inĂ©galitĂ©s valides et une fonction
de bornes infĂ©rieures. Nous avons analysĂ© les sous-problĂšmes de lâalgorithme en L et nous avons proposĂ© une mĂ©thode de rĂ©solution qui est beaucoup plus rapide que les algorithmes de programmation linĂ©aire standards. En outre, nous avons Ă©tendu notre modĂšle pour modĂ©liser les VRPS avec des temps de dĂ©placement et de service dĂ©pendant du temps.
Les rĂ©sultats de lâoptimisation montrent que, pour le problĂšme stochastique de soins Ă domicile, lâalgorithme «branch-and-cut» rĂ©sout Ă lâoptimalitĂ© les exemplaires avec 15 patients et 10% Ă 30% de visites synchronisĂ©es. Il trouve Ă©galement des solutions avec un Ă©cart Ă lâoptimum moyen de de 3,57% pour les cas avec 20 patients. De plus lâalgorithme «branch-and-cut» rĂ©sout Ă lâoptimalitĂ© les problĂšmes dâordonnancement stochastique des salles dâopĂ©ration avec 20 chirurgies. Ceci est une amĂ©lioration considĂ©rable par rapport Ă la littĂ©rature qui fait Ă©tat de cas avec 11 chirurgies. En outre, la modĂ©lisation proposĂ©e pour le problĂšme dĂ©pendant du temps trouve des solutions optimales pour dâune grande portion des exemplaires dâordonnancement de soins de santĂ© Ă domicile avec 30 Ă 60 patients et diffĂ©rents taux de visites
synchronisĂ©es. Dans la derniĂšre partie de cette thĂšse, nous avons Ă©tudiĂ© une catĂ©gorie de modĂšles dâoptimisation
robuste en deux Ă©tapes avec des variables entiĂšres du problĂšme adversaire. Nous avons analysĂ© lâimportance de cette classe de problĂšmes lors de la modĂ©lisation Ă deux niveaux de problĂšmes de planification de ressources robuste en deux Ă©tapes oĂč certaines tĂąches ont des temps dâarrivĂ©e et des durĂ©es incertains. Nous considĂ©rons un problĂšme de rĂ©partition et dâaffectation dâinfirmiĂšres comme une application de cette classe de modĂšles robustes. Nous avons appliquĂ© la dĂ©composition de Dantzig-Wolfe pour exploiter la structure de ces modĂšles, ce qui nous a permis de montrer que le problĂšme initial se rĂ©duit Ă un problĂšme
robuste Ă une seule Ă©tape. Nous avons proposĂ© un algorithme Benders pour le problĂšme reformulĂ©. Ătant donnĂ© que le problĂšme principal et le sous-problĂšme dans lâalgorithme Benders sont des programmes Ă nombres entiers mixtes, il requiert une quantitĂ© de calcul importante Ă chaque itĂ©ration de lâalgorithme pour les rĂ©soudre de maniĂšre optimale. Par consĂ©quent, nous avons dĂ©veloppĂ© de nouvelles conditions dâarrĂȘt pour ces programmes Ă nombres entiers
mixtes et fourni des preuves de convergence. Nous avons dĂ©veloppĂ© Ă©galement un algorithme heuristique appelĂ© «dual algorithm». Dans cette heuristique, nous dualisons la relaxation linĂ©aire du problĂšme adversaire dans le problĂšme reformulĂ© et gĂ©nĂ©rons des coupes itĂ©rativement pour façonner lâenveloppe convexe de lâensemble dâincertitude. Nous avons combinĂ© cette heuristique avec lâalgorithme Benders pour crĂ©er un algorithme plus efficace appelĂ© algorithme «Benders-dual algorithm». De nombreuses expĂ©riences de calcul sur le problĂšme de rĂ©partition et dâaffectation dâinfirmiĂšres sont effectuĂ©es pour comparer ces algorithmes.----------ABSTRACT : There are many applications of planning, scheduling, and routing problems in healthcare systems. Efficiently solving these problems can help healthcare managers provide higher-quality
services by making efficient use of available medical resources. Because of the combinatorial nature of these problems, in many cases, standard integer programming algorithms in commercial mathematical programming software such as CPLEX and Gurobi cannot solve the corresponding models effectively. In this dissertation, we study three healthcare planning, scheduling, and routing problems and propose decomposition-based integer programming,
stochastic programming, and robust optimization methods for them. In the first essay of this dissertation, we study an integrated operating room planning and scheduling problem that combines the assignment of surgeries to operating rooms and scheduling over a short-term planning horizon. We take into account the maximum daily working hours of surgeons, prevent the overlapping of surgeries performed by the same surgeon, allow time for the obligatory cleaning when switching from infectious to noninfectious cases, and respect the surgery deadlines. We formulate the problem using a mathematical programming model and develop a branch-and-price-and-cut algorithm based on a constraint
programming model for the subproblem. We also develop dominance rules and a fast infeasibility-detection algorithm based on a multidimensional knapsack problem to improve the efficiency of the constraint programming model. The computational results show that our method has an average optimality gap of 2.81% and significantly outperforms a compact mathematical formulation in the literature. As the second essay of this dissertation, for the first time, we study vehicle routing problems
with synchronized visits (VRPS) and stochastic/time-dependent travel and service times. In addition to considering a home-health care scheduling problem, we introduce an operating room scheduling problem with stochastic durations as a novel application of VRPS. We
formulate VRPS with stochastic times as a two-stage stochastic programming model with integer variables in both stages. An advantage of the proposed model is that, in contrast to the deterministic models in the VRPS literature, it does not have any big-M constraints. This
advantage comes at the cost of a large number of second-stage integer variables. We prove that the integrality constraints on second-stage variables are trivial, and therefore we can apply the L-shaped algorithm and its branch-and-cut implementation to solve the problem.
We enhance the model by developing valid inequalities and a lower bounding functional. We analyze the subproblems of the L-shaped algorithm and devise a solution method for them that is much faster than standard linear programming algorithms. Moreover, we extend our model to formulate VRPS with time-dependent travel and service times. Computational results show that, in the stochastic home-health care scheduling problem, the branch-and-cut algorithm optimally solves instances with 15 patients and 10% to 30% of synchronized visits. It also finds solutions with an average optimality gap of 3.57% for instances with 20 patients. Furthermore, the branch-and-cut algorithm ptimally solves stochastic operating room scheduling problems with 20 surgeries, a considerable improvement over the literature that reports on instances with 11 surgeries. In addition, the proposed formulation for the time-dependent problem solves a large portion of home-health care scheduling instances with 30 to 60 patients and different rates of synchronized visits to optimality.
For the last essay of this dissertation, we also study a class of two-stage robust optimization models with integer adversarial variables. We discuss the importance of this class of problems in modeling two-stage robust resource planning problems where some tasks have uncertain arrival times and duration periods. We consider a two-stage nurse planning problem as an application of this class of robust models. We apply Dantzig-Wolfe decomposition to exploit the structure of these models and show that the original problem reduces to a singlestage robust problem. We propose a Benders algorithm for the reformulated single-stage
problem. Since the master problem and subproblem in the Benders algorithm are mixed integer programs, it is computationally demanding to solve them optimally at each iteration of the algorithm. Therefore, we develop novel stopping conditions for these mixed integer programs and provide the relevant convergence proofs. We also develop a heuristic algorithm called dual algorithm. In this heuristic, we dualize the linear programming relaxation of the adversarial problem in the reformulated problem and iteratively generate cuts to shape the convex hull of the uncertainty set. We combine this heuristic with the Benders algorithm to create a more effective algorithm called Benders-dual algorithm. Extensive computational
experiments on the nurse planning problem are performed to compare these algorithms
Design of Heuristic Algorithms for Hard Optimization
This open access book demonstrates all the steps required to design heuristic algorithms for difficult optimization. The classic problem of the travelling salesman is used as a common thread to illustrate all the techniques discussed. This problem is ideal for introducing readers to the subject because it is very intuitive and its solutions can be graphically represented. The book features a wealth of illustrations that allow the concepts to be understood at a glance. The book approaches the main metaheuristics from a new angle, deconstructing them into a few key concepts presented in separate chapters: construction, improvement, decomposition, randomization and learning methods. Each metaheuristic can then be presented in simplified form as a combination of these concepts. This approach avoids giving the impression that metaheuristics is a non-formal discipline, a kind of cloud sculpture. Moreover, it provides concrete applications of the travelling salesman problem, which illustrate in just a few lines of code how to design a new heuristic and remove all ambiguities left by a general framework. Two chapters reviewing the basics of combinatorial optimization and complexity theory make the book self-contained. As such, even readers with a very limited background in the field will be able to follow all the content
CITIES: Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship
This book collects important contributions on smart cities. This book was created in collaboration with the ICSC-CITIES2020, held in San José (Costa Rica) in 2020. This book collects articles on: energetic efficiency and sustainability; infrastructures, energy and the environment; mobility and IoT; governance and citizenship
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