A multi-criteria approach for hospital capacity analysis

Hospitals are critical elements of health care systems and analysing their capacity to do work is a very important topic. To perform a system wide analysis of public hospital resources and capacity, a multi-objective optimization (MOO) approach has been proposed. This approach identifies the theoretical capacity of the entire hospital and facilitates a sensitivity analysis, for example of the patient case mix. It is necessary because the competition for hospital resources, for example between different entities, is highly influential on what work can be done. The MOO approach has been extensively tested on a real life case study and significant worth is shown. In this MOO approach, the epsilon constraint method has been utilized. However, for solving real life applications, with a large number of competing objectives, it was necessary to devise new and improved algorithms. In addition, to identify the best solution, a separable programming approach was developed. Multiple optimal solutions are also obtained via the iterative refinement and re-solution of the model.

MULTI-OBJECTIVE GENETIC ALGORITHM FOR EFFICIENT POINT MATCHING IN MULTI-SENSOR SATELLITE IMAGE

This paper investigates a new approach for point matching in multi-sensor satellite images. The feature points are matched using multi-objective optimization (angle criterion and distance condition) based on Genetic Algorithm (GA). This optimization process is more efficient as it considers both the angle criterion and distance condition to incorporate multi-objective switching in the fitness function. This optimization process helps in matching three corresponding corner points detected in the reference and sensed image and thereby using the affine transformation, the sensed image is aligned with the reference image. From the results obtained, the performance of the image registration is evaluated and it is concluded that the proposed approach is efficient.

Accurate point matching based on multi-objective Genetic Algorithm for multi-sensor satellite imagery

This paper investigates a novel approach for point matching of multi-sensor satellite imagery. The feature (corner) points extracted using an improved version of the Harris Corner Detector (HCD) is matched using multi-objective optimization based on a Genetic Algorithm (GA). An objective switching approach to optimization that incorporates an angle criterion, distance condition and point matching condition in the multi-objective fitness function is applied to match corresponding corner-points between the reference image and the sensed image. The matched points obtained in this way are used to align the sensed image with a reference image by applying an affine transformation. From the results obtained, the performance of the image registration is evaluated and compared with existing methods, namely Nearest Neighbor-Random SAmple Consensus (NN-Ran-SAC) and multi-objective Discrete Particle Swarm Optimization (DPSO). From the performed experiments it can be concluded that the proposed approach is an accurate method for registration of multi-sensor satellite imagery. (C) 2014 Elsevier Inc. All rights reserved.

Multiobjective Optimization of Piezoelectric Bimorph Actuator with Rigid Extension

Production of high tip deflection in a piezoelectric bimorph laminar actuator by applying high voltage is limited by many physical constraints. Therefore, piezoelectric bimorph actuator with a rigid extension of non-piezoelectric material at its tip is used to increase the tip deflection of such an actuator. Research on this type of piezoelectric bending actuator is either limited to first order constitutive relations, which do not include non-linear behavior of piezoelectric element at high electric field, or limited to curve fitting techniques. Therefore, this paper considers high electric field, and analytically models tapered piezoelectric bimorph actuator with a rigid extension of non-piezoelectric material at its tip. The stiffness, capacitance, effective tip deflection, block force, output strain energy, output energy density, input electrical energy and energy efficiency of the actuator are calculated analytically. The paper also discusses the multi-objective optimization of this type of actuator subjected to the mechanical and electrical constraints.

Sensitivity analysis and optimization of the nuclear fuel cycle

A sensitivity study has been conducted to assess the robustness of the conclusions presented in the MIT Fuel Cycle Study. The Once Through Cycle (OTC) is considered as the base-line case, while advanced technologies with fuel recycling characterize the alternative fuel cycles. The options include limited recycling in LWRs and full recycling in fast reactors and in high conversion LWRs. Fast reactor technologies studied include both oxide and metal fueled reactors. The analysis allowed optimization of the fast reactor conversion ratio with respect to desired fuel cycle performance characteristics. The following parameters were found to significantly affect the performance of recycling technologies and their penetration over time: Capacity Factors of the fuel cycle facilities, Spent Fuel Cooling Time, Thermal Reprocessing Introduction Date, and incore and Out-of-core TRU Inventory Requirements for recycling technology. An optimization scheme of the nuclear fuel cycle is proposed. Optimization criteria and metrics of interest for different stakeholders in the fuel cycle (economics, waste management, environmental impact, etc.) are utilized for two different optimization techniques (linear and stochastic). Preliminary results covering single and multi-variable and single and multi-objective optimization demonstrate the viability of the optimization scheme.

Parallel coordinates in computational engineering design

Modern Engineering Design involves the deployment of many computational tools. Re- search on challenging real-world design problems is focused on developing improvements for the engineering design process through the integration and application of advanced com- putational search/optimization and analysis tools. Successful application of these methods generates vast quantities of data on potential optimum designs. To gain maximum value from the optimization process, designers need to visualise and interpret this information leading to better understanding of the complex and multimodal relations between param- eters, objectives and decision-making of multiple and strongly conflicting criteria. Initial work by the authors has identified that the Parallel Coordinates interactive visualisation method has considerable potential in this regard. This methodology involves significant levels of user-interaction, making the engineering designer central to the process, rather than the passive recipient of a deluge of pre-formatted information. In the present work we have applied and demonstrated this methodology in two differ- ent aerodynamic turbomachinery design cases; a detailed 3D shape design for compressor blades, and a preliminary mean-line design for the whole compressor core. The first case comprises 26 design parameters for the parameterisation of the blade geometry, and we analysed the data produced from a three-objective optimization study, thus describing a design space with 29 dimensions. The latter case comprises 45 design parameters and two objective functions, hence developing a design space with 47 dimensions. In both cases the dimensionality can be managed quite easily in Parallel Coordinates space, and most importantly, we are able to identify interesting and crucial aspects of the relationships between the design parameters and optimum level of the objective functions under con- sideration. These findings guide the human designer to find answers to questions that could not even be addressed before. In this way, understanding the design leads to more intelligent decision-making and design space exploration. © 2012 AIAA.

Reasoning with imprecise trade-offs in decision making under certainty and uncertainty

In many real world situations, we make decisions in the presence of multiple, often conflicting and non-commensurate objectives. The process of optimizing systematically and simultaneously over a set of objective functions is known as multi-objective optimization. In multi-objective optimization, we have a (possibly exponentially large) set of decisions and each decision has a set of alternatives. Each alternative depends on the state of the world, and is evaluated with respect to a number of criteria. In this thesis, we consider the decision making problems in two scenarios. In the first scenario, the current state of the world, under which the decisions are to be made, is known in advance. In the second scenario, the current state of the world is unknown at the time of making decisions. For decision making under certainty, we consider the framework of multiobjective constraint optimization and focus on extending the algorithms to solve these models to the case where there are additional trade-offs. We focus especially on branch-and-bound algorithms that use a mini-buckets algorithm for generating the upper bound at each node of the search tree (in the context of maximizing values of objectives). Since the size of the guiding upper bound sets can become very large during the search, we introduce efficient methods for reducing these sets, yet still maintaining the upper bound property. We define a formalism for imprecise trade-offs, which allows the decision maker during the elicitation stage, to specify a preference for one multi-objective utility vector over another, and use such preferences to infer other preferences. The induced preference relation then is used to eliminate the dominated utility vectors during the computation. For testing the dominance between multi-objective utility vectors, we present three different approaches. The first is based on a linear programming approach, the second is by use of distance-based algorithm (which uses a measure of the distance between a point and a convex cone); the third approach makes use of a matrix multiplication, which results in much faster dominance checks with respect to the preference relation induced by the trade-offs. Furthermore, we show that our trade-offs approach, which is based on a preference inference technique, can also be given an alternative semantics based on the well known Multi-Attribute Utility Theory. Our comprehensive experimental results on common multi-objective constraint optimization benchmarks demonstrate that the proposed enhancements allow the algorithms to scale up to much larger problems than before. For decision making problems under uncertainty, we describe multi-objective influence diagrams, based on a set of p objectives, where utility values are vectors in Rp, and are typically only partially ordered. These can be solved by a variable elimination algorithm, leading to a set of maximal values of expected utility. If the Pareto ordering is used this set can often be prohibitively large. We consider approximate representations of the Pareto set based on ϵ-coverings, allowing much larger problems to be solved. In addition, we define a method for incorporating user trade-offs, which also greatly improves the efficiency.

Manipulator trajectory planning using a MOEA

Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.

Conception des réseaux maillés sans fil à multiples-radios multiples-canaux

Généralement, les problèmes de conception de réseaux consistent à sélectionner les arcs et les sommets d’un graphe G de sorte que la fonction coût est optimisée et l’ensemble de contraintes impliquant les liens et les sommets dans G sont respectées. Une modification dans le critère d’optimisation et/ou dans l’ensemble de contraintes mène à une nouvelle représentation d’un problème différent. Dans cette thèse, nous nous intéressons au problème de conception d’infrastructure de réseaux maillés sans fil (WMN- Wireless Mesh Network en Anglais) où nous montrons que la conception de tels réseaux se transforme d’un problème d’optimisation standard (la fonction coût est optimisée) à un problème d’optimisation à plusieurs objectifs, pour tenir en compte de nombreux aspects, souvent contradictoires, mais néanmoins incontournables dans la réalité. Cette thèse, composée de trois volets, propose de nouveaux modèles et algorithmes pour la conception de WMNs où rien n’est connu à l’ avance. Le premiervolet est consacré à l’optimisation simultanée de deux objectifs équitablement importants : le coût et la performance du réseau en termes de débit. Trois modèles bi-objectifs qui se différent principalement par l’approche utilisée pour maximiser la performance du réseau sont proposés, résolus et comparés. Le deuxième volet traite le problème de placement de passerelles vu son impact sur la performance et l’extensibilité du réseau. La notion de contraintes de sauts (hop constraints) est introduite dans la conception du réseau pour limiter le délai de transmission. Un nouvel algorithme basé sur une approche de groupage est proposé afin de trouver les positions stratégiques des passerelles qui favorisent l’extensibilité du réseau et augmentent sa performance sans augmenter considérablement le coût total de son installation. Le dernier volet adresse le problème de fiabilité du réseau dans la présence de pannes simples. Prévoir l’installation des composants redondants lors de la phase de conception peut garantir des communications fiables, mais au détriment du coût et de la performance du réseau. Un nouvel algorithme, basé sur l’approche théorique de décomposition en oreilles afin d’installer le minimum nombre de routeurs additionnels pour tolérer les pannes simples, est développé. Afin de résoudre les modèles proposés pour des réseaux de taille réelle, un algorithme évolutionnaire (méta-heuristique), inspiré de la nature, est développé. Finalement, les méthodes et modèles proposés on été évalués par des simulations empiriques et d’événements discrets.

Gestion adaptative des ressources dans les réseaux maillés sans fil à multiples-radios multiples-canaux

Depuis quelques années, la recherche dans le domaine des réseaux maillés sans fil ("Wireless Mesh Network (WMN)" en anglais) suscite un grand intérêt auprès de la communauté des chercheurs en télécommunications. Ceci est dû aux nombreux avantages que la technologie WMN offre, telles que l'installation facile et peu coûteuse, la connectivité fiable et l'interopérabilité flexible avec d'autres réseaux existants (réseaux Wi-Fi, réseaux WiMax, réseaux cellulaires, réseaux de capteurs, etc.). Cependant, plusieurs problèmes restent encore à résoudre comme le passage à l'échelle, la sécurité, la qualité de service (QdS), la gestion des ressources, etc. Ces problèmes persistent pour les WMNs, d'autant plus que le nombre des utilisateurs va en se multipliant. Il faut donc penser à améliorer les protocoles existants ou à en concevoir de nouveaux. L'objectif de notre recherche est de résoudre certaines des limitations rencontrées à l'heure actuelle dans les WMNs et d'améliorer la QdS des applications multimédia temps-réel (par exemple, la voix). Le travail de recherche de cette thèse sera divisé essentiellement en trois principaux volets: le contrôle d‟admission du trafic, la différentiation du trafic et la réaffectation adaptative des canaux lors de la présence du trafic en relève ("handoff" en anglais). Dans le premier volet, nous proposons un mécanisme distribué de contrôle d'admission se basant sur le concept des cliques (une clique correspond à un sous-ensemble de liens logiques qui interfèrent les uns avec les autres) dans un réseau à multiples-sauts, multiples-radios et multiples-canaux, appelé RCAC. Nous proposons en particulier un modèle analytique qui calcule le ratio approprié d'admission du trafic et qui garantit une probabilité de perte de paquets dans le réseau n'excédant pas un seuil prédéfini. Le mécanisme RCAC permet d‟assurer la QdS requise pour les flux entrants, sans dégrader la QdS des flux existants. Il permet aussi d‟assurer la QdS en termes de longueur du délai de bout en bout pour les divers flux. Le deuxième volet traite de la différentiation de services dans le protocole IEEE 802.11s afin de permettre une meilleure QdS, notamment pour les applications avec des contraintes temporelles (par exemple, voix, visioconférence). À cet égard, nous proposons un mécanisme d'ajustement de tranches de temps ("time-slots"), selon la classe de service, ED-MDA (Enhanced Differentiated-Mesh Deterministic Access), combiné à un algorithme efficace de contrôle d'admission EAC (Efficient Admission Control), afin de permettre une utilisation élevée et efficace des ressources. Le mécanisme EAC prend en compte le trafic en relève et lui attribue une priorité supérieure par rapport au nouveau trafic pour minimiser les interruptions de communications en cours. Dans le troisième volet, nous nous intéressons à minimiser le surcoût et le délai de re-routage des utilisateurs mobiles et/ou des applications multimédia en réaffectant les canaux dans les WMNs à Multiples-Radios (MR-WMNs). En premier lieu, nous proposons un modèle d'optimisation qui maximise le débit, améliore l'équité entre utilisateurs et minimise le surcoût dû à la relève des appels. Ce modèle a été résolu par le logiciel CPLEX pour un nombre limité de noeuds. En second lieu, nous élaborons des heuristiques/méta-heuristiques centralisées pour permettre de résoudre ce modèle pour des réseaux de taille réelle. Finalement, nous proposons un algorithme pour réaffecter en temps-réel et de façon prudente les canaux aux interfaces. Cet algorithme a pour objectif de minimiser le surcoût et le délai du re-routage spécialement du trafic dynamique généré par les appels en relève. Ensuite, ce mécanisme est amélioré en prenant en compte l‟équilibrage de la charge entre cliques.

A multi-objective sensitivity approach to training providers' evaluation and quota allocation planning

The practice of solely relying on the human resources department in the selection process of external training providers has cast doubts and mistrust across other departments as to how trainers are sourced. There are no measurable criteria used by human resource personnel, since most decisions are based on intuitive experience and subjective market knowledge. The present problem focuses on outsourcing of private training programs that are partly government funded, which has been facing accountability challenges. Due to the unavailability of a scientific decision-making approach in this context, a 12-step algorithm is proposed and tested in a Japanese multinational company. The model allows the decision makers to revise their criteria expectations, in turn witnessing the change of the training providers' quota distribution. Finally, this multi-objective sensitivity analysis provides a forward-looking approach to training needs planning and aids decision makers in their sourcing strategy.

Construction of neural network-based prediction intervals using particle swarm optimization

Prediction intervals (PIs) are excellent tools for quantification of uncertainties associated with point forecasts and predictions. This paper adopts and develops the lower upper bound estimation (LUBE) method for construction of PIs using neural network (NN) models. This method is fast and simple and does not require calculation of heavy matrices, as required by traditional methods. Besides, it makes no assumption about the data distribution. A new width-based index is proposed to quantitatively check how much PIs are informative. Using this measure and the coverage probability of PIs, a multi-objective optimization problem is formulated to train NN models in the LUBE method. The optimization problem is then transformed into a training problem through definition of a PI-based cost function. Particle swarm optimization (PSO) with the mutation operator is used to minimize the cost function. Experiments with synthetic and real-world case studies indicate that the proposed PSO-based LUBE method can construct higher quality PIs in a simpler and faster manner.

An effective heuristic for stockyard planning and machinery scheduling at a coal handling facility

Coal handling is a complex process involving different correlated and highly dependent operations such as selecting appropriate product types, planning stockpiles, scheduling stacking and reclaiming activities and managing train loads. Planning these operations manually is time consuming and can result in non-optimized schedules as future impact of decisions may not be appropriately considered. This paper addresses the operational scheduling of the continuous coal handling problem with multiple conflicting objectives. As the problem is NP-hard in nature, an effective heuristic is presented for planning stockpiles and scheduling resources to minimize delays in production and the coal age in the stockyard. A model of stockyard operations within a coal mine is described and the problem is formulated as a Bi- Objective Optimization Problem (BOOP). The algorithm efficacy is demonstrated on different real-life data scenarios. Computational results show that the solution algorithm is effective and the coal throughput is substantially impacted by the conflicting objectives. Together, the model and the proposed heuristic, can act as a decision support system for the stockyard planner to explore the effects of alternative decisions, such as balancing age and volume of stockpiles, and minimizing conflicts due to stacker and reclaimer movements.

Multi-objective image segmentation with an interactive evolutionary computation approach

In this paper, a multi-objective image segmentation approach with an Interactive Evolutionary Computation (IEC)-based framework is presented. Two objectives, i.e., the overall deviation and the connectivity measure, are optimized simultaneously using a mu