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.

L’adaptation de l’agriculture au changement et à la variabilité climatiques au Québec : un processus de diffusion des innovations

Au-delà des variables climatiques, d’autres facteurs non climatiques sont à considérer dans l’analyse de la vulnérabilité et de l’adaptation au changement et variabilité climatiques. Cette mutation de paradigme place l’agent humain au centre du processus d’adaptation au changement climatique, notamment en ce qui concerne le rôle des réseaux sociaux dans la transmission des nouvelles idées. Dans le domaine de l’agriculture, le recours aux innovations est prôné comme stratégie d’adaptation. L’élaboration et l’appropriation de ces stratégies d’adaptation peuvent être considérées comme des processus d’innovation qui dépendent autant du contexte social et culturel d’un territoire, de sa dynamique, ainsi que de la stratégie elle-même. Aussi, l’appropriation et la diffusion d’une innovation s’opèrent à partir d’un processus décisionnel à l’échelle de l’exploitation agricole, qui à son tour, demande une compréhension des multiples forces et facteurs externes et internes à l’exploitation et les multiples objectifs de l’exploitant. Ainsi, la compréhension de l’environnement décisionnel de l’exploitant agricole à l’échelle de la ferme est vitale, car elle est un préalable incontournable au succès et à la durabilité de toute politique d’adaptation de l’agriculture. Or, dans un secteur comme l’agriculture, il est reconnu que les réseaux sociaux par exemple, jouent un rôle crucial dans l’adaptation notamment, par le truchement de la diffusion des innovations. Aussi, l’objectif de cette recherche est d’analyser comment les exploitants agricoles s’approprient et conçoivent les stratégies d’adaptation au changement et à la variabilité climatiques dans une perspective de diffusion des innovations. Cette étude a été menée en Montérégie-Ouest, région du sud-ouest du Québec, connue pour être l’une des plus importantes régions agricoles du Québec, en raison des facteurs climatiques et édaphiques favorables. Cinquante-deux entrevues ont été conduites auprès de différents intervenants à l’agriculture aux niveaux local et régional. L’approche grounded theory est utilisée pour analyser, et explorer les contours de l’environnement décisionnel des exploitants agricoles relativement à l’utilisation des innovations comme stratégie d’adaptation. Les résultats montrent que les innovations ne sont pas implicitement conçues pour faire face aux changements et à la variabilité climatiques même si l’évolution du climat influence leur émergence, la décision d’innover étant largement déterminée par des considérations économiques. D’autre part, l‘étude montre aussi une faiblesse du capital sociale au sein des exploitants agricoles liée à l’influence prépondérante exercée par le secteur privé, principal fournisseur de matériels et intrants agricoles. L’influence du secteur privé se traduit par la domination des considérations économiques sur les préoccupations écologiques et la tentation du profit à court terme de la part des exploitants agricoles, ce qui pose la problématique de la soutenabilité des interventions en matière d’adaptation de l’agriculture québécoise. L’étude fait ressortir aussi la complémentarité entre les réseaux sociaux informels et les structures formelles de soutien à l’adaptation, de même que la nécessité d’établir des partenariats. De plus, l’étude place l’adaptation de l’agriculture québécoise dans une perspective d’adaptation privée dont la réussite repose sur une « socialisation » des innovations, laquelle devrait conduire à l’émergence de processus institutionnels formels et informels. La mise en place de ce type de partenariat peut grandement contribuer à améliorer le processus d’adaptation à l’échelle locale.

Multi-objective assembly job shop scheduling using genetic algorithm and tabu search

Assembly job shop scheduling problem (AJSP) is one of the most complicated combinatorial optimization problem that involves simultaneously scheduling the processing and assembly operations of complex structured products. The problem becomes even more complicated if a combination of two or more optimization criteria is considered. This thesis addresses an assembly job shop scheduling problem with multiple objectives. The objectives considered are to simultaneously minimizing makespan and total tardiness. In this thesis, two approaches viz., weighted approach and Pareto approach are used for solving the problem. However, it is quite difficult to achieve an optimal solution to this problem with traditional optimization approaches owing to the high computational complexity. Two metaheuristic techniques namely, genetic algorithm and tabu search are investigated in this thesis for solving the multiobjective assembly job shop scheduling problems. Three algorithms based on the two metaheuristic techniques for weighted approach and Pareto approach are proposed for the multi-objective assembly job shop scheduling problem (MOAJSP). A new pairing mechanism is developed for crossover operation in genetic algorithm which leads to improved solutions and faster convergence. The performances of the proposed algorithms are evaluated through a set of test problems and the results are reported. The results reveal that the proposed algorithms based on weighted approach are feasible and effective for solving MOAJSP instances according to the weight assigned to each objective criterion and the proposed algorithms based on Pareto approach are capable of producing a number of good Pareto optimal scheduling plans for MOAJSP instances.

An Electronic Market-Maker

This paper presents an adaptive learning model for market-making under the reinforcement learning framework. Reinforcement learning is a learning technique in which agents aim to maximize the long-term accumulated rewards. No knowledge of the market environment, such as the order arrival or price process, is assumed. Instead, the agent learns from real-time market experience and develops explicit market-making strategies, achieving multiple objectives including the maximizing of profits and minimization of the bid-ask spread. The simulation results show initial success in bringing learning techniques to building market-making algorithms.

Evolución del sistema de información empresarial SAP en la Universidad Del Rosario

Esta propuesta documenta cómo fue el proceso de implementación de SAP en la Universidad del Rosario (UR). Además permite determinar los factores que la definen cómo una implementación exitosa. En esta investigación no tiene en cuenta la implementación de los sistemas de gestión académica, de recursos humanos o e-learning ya que el estudio solo está centrado en el área administrativa. También se estudia el grado de madurez de los sistemas de información de la Universidad, usando el modelo de Luftam validado en el contexto de las tecnologías. La gran pregunta de investigación es: ¿Cómo fue la implementación de SAP en la UR y cómo esto fue una implementación exitosa?

Multi-criteria analysis and the resolution of sustainable development dilemmas: a stakeholder management approach

We demonstrate that stakeholder-oriented multi-criteria analysis (MCA) can adequately address a variety of sustainable development dilemmas in decision-making, especially when applied to complex project evaluations involving multiple objectives and multiple stakeholder groups. Such evaluations are typically geared towards satisfying simultaneously private economic goals, broader social objectives and environmental targets. We show that, under specific conditions, a variety of stakeholder-oriented MCA approaches may be able to contribute substantively to the resolution or improved governance of societal conflicts and the pursuit of the public good in the form of sustainable development. We contrast the potential usefulness of these stakeholder-oriented approaches – in terms of their ability to contribute to sustainable development – with more conventional MCA approaches and social cost–benefit analysis.

Research methodologies for planning ecotourism and nature conservation

Ecotourism is an important niche market in the world tourism industry. It is becoming increasingly popular as an alternative to mass tourism. The emergence of ecotourism was facilitated by the environmental damage associated with mass tourism. Ecotourism is defined in many ways and there is no consensus as to its exact meaning. However, a number of salient elements, such as environmental conservation, maintenance of biodiversity, a satisfying experience for the visitors, study and appreciation of nature and sustainable community development, are included in many definitions. Tourism creates negative environmental externalities in the form of environmental damage. Such adverse effects can have serious implications for the tourism industry because they damage the very natural resource that forms the raw material for ecotourism. Ecotourism ventures should thus be properly planned and implemented and carefully monitored. Proper planning of ecotourism is hampered by the paucity of relevant qualitative and quantitative information. The use of analytical tools such as the Contingent Valuation Method, carrying capacity, decision analysis techniques with which multiobjective and uncertain consequences can be analysed, and other management strategies, such as the Safe Minimum Standard, can be useful in enabling better planning of ecotourism. Ecotourism can thus enhance the opportunities for better management of natural resources while providing a satisfying experience for the visitor.

Evolutionary multiobjective optimization in engineering management: an empirical study on bridge deck rehabilitation

There exist multiple objectives in engineering management such as minimum cost and maximum service capacity. Although solution methods of multiobjective optimization problems have undergone continual development over the past several decades, the methods available to date are not particularly robust, and none of them performs well on the broad classes. Because genetic algorithms work with a population of points, they can capture a number of solutions simultaneously, and easily incorporate the concept of Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with the rehabilitation planning of bridge decks at a network level by minimizing the rehabilitation cost and deterioration degree simultaneously.

Evolutionary multiobjective optimization in engineering management: an empirical application in infrastructure systems

Generally multiple objectives exist in transportation infrastructure management, such as minimum cost and maximum service capacity. Although solution methoak of multiobjective optimization problems have undergone continual development over the part several decades, the methods available to date are not particularly robust, and none of them perform well on the broad classes. Because genetic algorithms work with apopulation ofpoints, they can capture a number of solutions simultaneously, and easily incorporate the concept of a Pareto optimal set in their optimization process. In this paper, a genetic algorithm is modified to deal with an empirical application for the rehabilitation planning of bridge decks, at a network level, by minimizing the rehabilitation cost and deterioration degree simultaneously.

Incorporating stakeholder values into regional forest planning : a value function approach

Forest management involves multiple objectives, multiple stakeholders, complex socio-ecological and political interactions. Public involvement in forest decision making is a challenging task that involves controversies. Various participatory tools such as public consultation forums, public comment processes, opinion polls are used to consult and to obtain inputs from communities. All these methods can provide useful information but they fail to quantify the trade-offs systematically and offer little help in minimizing conflicts. The Australian Regional Forest Agreement (RFA) program was implemented in response to the decades of conflicts and debate between various stakeholder groups and government over the use and management of forest resources. So far, it has not been able to minimize conflicts in the forestry sector, partly due to its poor incorporation and integration of stakeholder values. This paper uses the value functions approach in modelling stakeholder values in regional forest planning. The results of the study indicate that this method can help to incorporate value preferences effectively into the decision making process. It can also increase the transparency and credibility of the forest planning exercises such as RFA process.

Where and when to revegetate : a quantitative method for scheduling landscape reconstruction

Restoration of native vegetation is required in many regions of the world, but determining priority locations for revegetation is a complex problem. We consider the problem of determining spatial and temporal priorities for revegetation to maximize habitat for 62 bird species within a heavily cleared agricultural region, 11 000 km2 in area. We show how a reserve-selection framework can be applied to a complex, large-scale restoration-planning problem to account for multi-species objectives and connectivity requirements at a spatial extent and resolution relevant to management. Our approach explicitly accounts for time lags in planting and development of habitat resources, which is intended to avoid future population bottlenecks caused by delayed provision of critical resources, such as tree hollows. We coupled species-specific models of expected habitat quality and fragmentation effects with the dynamics of habitat suitability following replanting to produce species-specific maps for future times. Spatial priorities for restoration were determined by ranking locations (150-m grid cells) by their expected contribution to species habitat through time using the conservation planning tool, ‘‘Zonation.’’ We evaluated solutions by calculating expected trajectories of habitat availability for each species. We produced a spatially explicit revegetation schedule for the region that resulted in a balanced increase in habitat for all species. Priority areas for revegetation generally were clustered around existing vegetation, although not always. Areas on richer soils and with high rainfall were more highly ranked, reflecting their potential to support high-quality habitats that have been disproportionately cleared for agriculture. Accounting for delayed development of habitat resources altered the rank-order of locations in the derived revegetation plan and led to improved expected outcomes for fragmentation-sensitive species. This work demonstrates the potential for systematic restoration planning at large scales that accounts for multiple objectives, which is urgently needed by land and natural resource managers.

ACE-Obesity : priority setting for obesity prevention

The ACE-Obesity study uses an evidence-based approach to evaluate interventions aimed at reducing the prevalence of obesity in Australian youth. It informs decision-makers about the benefits of individual interventions and the packaging of a coherent strategy for obesity prevention and management. To avoid methodological confounding, the approach employs standardised methods including a two stage concept of benefit; a common comparator, setting and decision context; Australian data; and extensive probabilistic uncertainty testing. The technical cost-effectiveness results (cost per DALY) for each of the selected interventions will be reported. Modelling is undertaken to convert changes in behaviour to BMI outcomes and then to DALYs, and issues of the attribution of costs across multiple objectives arise. Due process is achieved by involving stakeholders on a Working Group, and by consideration of second stage filters (such as equity, acceptability and feasibility). The results are brought together in a 'league table' in which all the interventions are ranked in order of economic merit without the usual methodological concerns about results drawn from studies lacking in comparability. In packaging interventions to meet particular budget allocations, the divisibility, mutual exclusivity and returns to scale of individual interventions are considered, as well as issues of program logic, target group coverage and a range of settings.

A particle swarm based hybrid system for imbalances medical data sampling

Background
Medical and biological data are commonly with small sample size, missing values, and most importantly, imbalanced class distribution. In this study we propose a particle swarm based hybrid system for remedying the class imbalance problem in medical and biological data mining. This hybrid system combines the particle swarm optimization (PSO) algorithm with multiple classifiers and evaluation metrics for evaluation fusion. Samples from the majority class are ranked using multiple objectives according to their merit in compensating the class imbalance, and then combined with the minority class to form a balanced dataset.

Results
One important finding of this study is that different classifiers and metrics often provide different evaluation results. Nevertheless, the proposed hybrid system demonstrates consistent improvements over several alternative methods with three different metrics. The sampling results also demonstrate good generalization on different types of classification algorithms, indicating the advantage of information fusion applied in the hybrid system.

Conclusion
The experimental results demonstrate that unlike many currently available methods which often perform unevenly with different datasets the proposed hybrid system has a better generalization property which alleviates the method-data dependency problem. From the biological perspective, the system provides indication for further investigation of the highly ranked samples, which may result in the discovery of new conditions or disease subtypes.

Resolving conflicts in fire management using decision theory: asset-protection versus biodiversity conservation

Agencies charged with nature conservation and protecting built-assets from fire face a policy dilemma because management that protects assets can have adverse impacts on biodiversity. Although conservation is often a policy goal, protecting built-assets usually takes precedence in fire management implementation. To make decisions that can better achieve both objectives, existing trade-offs must first be recognized, and then policies implemented to manage multiple objectives explicitly. We briefly review fire management actions that can conflict with biodiversity conservation. Through this review, we find that common management practices might not appreciably reduce the threat to built-assets but could have a large negative impact on biodiversity. We develop a framework based on decision theory that could be applied to minimize these conflicts. Critical to this approach is (1) the identification of the full range of management options and (2) obtaining data for evaluating the effectiveness of those options for achieving asset protection and conservation goals. This information can be used to compare explicitly the effectiveness of different management choices for conserving species and for protecting assets, given budget constraints. The challenge now is to gather data to quantify these trade-offs so that fire policy and practices can be better aligned with multiple objectives