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Exposure to severe levels of hand-arm vibration can lead to hand-arm vibration syndrome. Towards curbing the development of hand-arm vibration syndrome, studies have shown that anti-vibration gloves effectively reduce the transmission of unwanted vibration from vibrating equipment to the human hand. However, most of these studies have focused on the study of anti-vibration gloves for power tools such as chipping hammers, and not much work has been done to design anti-vibration gloves for steering wheels. Also, as most of these studies are based on experimental or modeling techniques, the level of effectiveness and optimum glove properties for better performance remains unclear. To fill this gap, the dynamics of the hand-arm system, with and without gloves, coupled to a steering wheel is studied analytically in this work. A lumped parameter model of the hand-arm system with hand-tool interaction is modeled as a linear spring-damper system. The model is validated by comparing transmissibility obtained numerically to transmissibility obtained from experiments. The resulting governing equations of motion are solved analytically using the method of undetermined coefficients. Parametric analysis is performed on the biomechanical model of the hand-arm system with and without a glove to identify key design parameters. It is observed that the effect of glove parameters on its performance varies based on the frequency range. This observation further motivates us to optimize the glove parameters, using multi-objective optimization, to minimize the overall transmissibility in different frequency ranges. / Master of Science / When the human hand is exposed for a long time to vibrations generated from hand-held tools, such as Jack-hammers, rock breakers and chipping hammers, humans are in danger of developing hand-arm vibration syndrome. Hand-arm vibration syndrome is dangerous as severe episodes of this syndrome could lead to gangrene and eventually amputation of the fingers. To prevent the occurrence of hand-arm vibration syndrome, some researchers have explored the effectiveness of anti-vibration gloves through experiments. However, no work has been performed to identify the optimal glove design that best optimizes an anti-vibration glove for steering wheel applications. To address this issue, this thesis studied a mathematical model of the human-hand, wearing an anti-vibration glove attached to a steering wheel system. To ensure this model could successfully replicate real life applications, measurements computed with the model were compared with measurements on the human-hand obtained from experiments. After successfully ensuring that the model closely replicated real-life measurements, the model was used to design an Anti-vibration glove with optimal values to protect the hand from hand-arm vibration syndrome.

In a world where calls for climate action grow louder by the day, the role of renewable energy and energy efficient generation sources has become extremely important. One such energy efficient resource that can increase the penetration of renewable energy into the grid is the Combined Heat and Power technology. Combined Heat and Power (CHP) plants produce useful thermal and electrical power output from a single input fuel source and are widely used in the industrial and commercial sectors for reliable on-site power production. However, several unfavorable policies combined with inconsistent regulations have discouraged investments in this technology and reduced participation of such facilities in grid operations. The potential benefits that could be offered by this technology are numerous - improving grid resiliency during emergencies, deferring transmission system updates and reducing toxic emissions, to name a few. With increased share of renewable energy sources in the generation mix, there is a pressing need for reliable base generation that can meet the grid requirements without contributing negatively to the environment. Since CHP units are good candidates to help achieve this two-fold requirement, it is important to understand the present barriers to their deployment and grid involvement. In this thesis work, we explore some of these challenges and propose suitable grid integration technology as well as market participation approaches for better involvement of distributed CHP units in the industrial and commercial sectors. / Master of Science / Combined Heat and Power is a generation technology which uses a single fuel source to produce two useful outputs - electric power and thermal energy - by capturing and reusing the exhaust steam by-product. These generating units have much higher efficiencies than conventional power plants, lower fuel emissions and have been a popular choice among several industries and commercial buildings with a need for uninterrupted heat and power. With increasing calls for climate action and large scale deployment of renewable based energy generation sources, there is a higher need for reliable base-line generation which can handle the fluctuations and uncertainty of such renewables. This need can be met by CHP units owing to their geographic distribution and their high operating duration. CHPs also provide a myriad of other benefits for the grid operators and environmental benefits, compared to the conventional generators. However, unfavorable and inconsistent regulatory procedures have discouraged these facility owners from actively engaging in providing grid services. Therefore, it is imperative to look into some of the existing policies and understand where the changes and incentives need to be made. In this work, we look into methods that can ease CHP integration from a technological and an economic point of view, with the aim of encouraging grid operators and CHP owners to be more active participants.

Due to the complexity of multi-objective combinatorial optimization problems (MOCO), metaheuristics like multi-objective evolutionary algorithms (MOEA) are gaining importance to obtain a well-converged and well-dispersed Pareto-optimal frontier approximation. In this study, of the well-known MOCO problems, single-dimensional multi-objective knapsack problem and multi-objective assignment problem are taken into consideration. We develop a steady-state and elitist MOEA in order to approximate the Pareto-optimal frontiers. We utilize a territory concept in order to provide diversity over the Pareto-optimal frontiers of various problem instances. The motivation behind the territory definition is to attach the algorithm the advantage of fast execution by eliminating the need for an explicit diversity preserving operator. We also develop an interactive preference incorporation mechanism to converge to the regions that are of special interest for the decision maker by interacting with him/her during the optimization process.

The growth in the use of machine learning in different areas has revealed challenging classification problems that require robust systems. Multiple Classier Systems (MCSs) have attracted interest from researchers as a method that could address such problems. Optimizing the fitness of an MCS improves its, robustness. The lack of an analysis for MCSs from a fitness perspective is identified. To fill this gap, an analytic model from this perspective is derived mathematically by extending the error analysis introduced by Brown and Kuncheva in 2010. The model relates the fitness of an MCS to the average accuracy, positive-diversity, and negative-diversity of the classifiers that constitute the MCS. The model is verified using a statistical analysis of a Monte-Carlo based simulation. This shows the significance of the indicated relationships by the model. This model provides guidelines for developing robust MCSs. It enables the selection of classifiers which compose an MCS with an improved fitness while improving computational cost by avoiding local calculations. The usefulness of the model for designing classification systems is investigated. A new measure consisting of the accuracy and positive-diversity is developed. This measure evaluates fitness while avoiding many calculations compared to the regular measures. A new system (Gadapt) is developed. Gadapt combines machine learning and genetic algorithms to define subsets of the feature space that closely match true class regions. It uses the new measure as a multi-objective criterion for a multi-objective genetic algorithm to identify the MCSs those create the subsets. The design of Gadapt is validated experimentally. The usefulness of the measure and the method of determining the subsets for the performance of Gadapt are examined based on five generated data sets that represent a wide range of problems. The robustness of Gadapt to small amounts of training data is evaluated in comparison with five existing systems on four benchmark data sets. The performance of Gadapt is evaluated in comparison with eleven existing systems on nine benchmark data sets. The analysis of the experiment results supports the validity of the Gadapt design and the outperforming of Gadapt on the existing systems in terms of robustness and performance.

The Levenberg-Marquardt algorithm is a classical method for solving nonlinear systems of equations that can come from various applications in engineering and economics. Recently, Levenberg-Marquardt methods turned out to be a valuable principle for obtaining fast convergence to a solution of the nonlinear system if the classical nonsingularity assumption is replaced by a weaker error bound condition. In this way also problems with nonisolated solutions can be treated successfully. Such problems increasingly arise in engineering applications and in mathematical programming. In this thesis we use Levenberg-Marquardt algorithms to deal with nonlinear equations, multi-objective optimization and complementarity problems. We develop new algorithms for solving these problems and investigate their convergence properties. For sufficiently smooth nonlinear equations we provide convergence results for inexact Levenberg-Marquardt type algorithms. In particular, a sharp bound on the maximal level of inexactness that is sufficient for a quadratic (or a superlinear) rate of convergence is derived. Moreover, the theory developed is used to show quadratic convergence of a robust projected Levenberg-Marquardt algorithm. The use of Levenberg-Marquardt type algorithms for unconstrained multi-objective optimization problems is investigated in detail. In particular, two globally and locally quadratically convergent algorithms for these problems are developed. Moreover, assumptions under which the error bound condition for a Pareto-critical system is fulfilled are derived. We also treat nonsmooth equations arising from reformulating complementarity problems by means of NCP functions. For these reformulations, we show that existing smoothness conditions are not satisfied at degenerate solutions. Moreover, we derive new results for positively homogeneous functions. The latter results are used to show that appropriate weaker smoothness conditions (enabling a local Q-quadratic rate of convergence) hold for certain reformulations. / Der Levenberg-Marquardt-Algorithmus ist ein klassisches Verfahren zur Lösung von nichtlinearen Gleichungssystemen, welches in verschiedenen Anwendungen der Ingenieur-und Wirtschaftswissenschaften vorkommen kann. Kürzlich, erwies sich das Verfahren als ein wertvolles Instrument für die Gewährleistung einer schnelleren Konvergenz für eine Lösung des nichtlinearen Systems, wenn die klassische nichtsinguläre Annahme durch eine schwächere Fehlerschranke der eingebundenen Bedingung ersetzt wird. Auf diese Weise, lassen sich ebenfalls Probleme mit nicht isolierten Lösungen erfolgreich behandeln. Solche Probleme ergeben sich zunehmend in den praktischen, ingenieurwissenschaftlichen Anwendungen und in der mathematischen Programmierung. In dieser Arbeit verwenden wir Levenberg-Marquardt- Algorithmus für nichtlinearere Gleichungen, multikriterielle Optimierung - und nichtlineare Komplementaritätsprobleme. Wir entwickeln neue Algorithmen zur Lösung dieser Probleme und untersuchen ihre Konvergenzeigenschaften. Für ausreichend differenzierbare nichtlineare Gleichungen, analysieren und bieten wir Konvergenzergebnisse für ungenaue Levenberg-Marquardt-Algorithmen Typen. Insbesondere, bieten wir eine strenge Schranke für die maximale Höhe der Ungenauigkeit, die ausreichend ist für eine quadratische (oder eine superlineare) Rate der Konvergenz. Darüber hinaus, die entwickelte Theorie wird verwendet, um quadratische Konvergenz eines robusten projizierten Levenberg-Marquardt-Algorithmus zu zeigen. Die Verwendung von Levenberg-Marquardt-Algorithmen Typen für unbeschränkte multikriterielle Optimierungsprobleme im Detail zu untersucht. Insbesondere sind zwei globale und lokale quadratische konvergente Algorithmen für multikriterielle Optimierungsprobleme entwickelt worden. Die Annahmen wurden hergeleitet, unter welche die Fehlerschranke der eingebundenen Bedingung für ein Pareto-kritisches System erfüllt ist. Wir behandeln auch nicht differenzierbare nichtlineare Gleichungen aus Umformulierung der nichtlinearen Komplementaritätsprobleme durch NCP-Funktionen. Wir zeigen für diese Umformulierungen, dass die bestehenden differenzierbaren Bedingungen nicht zufrieden mit degenerierten Lösungen sind. Außerdem, leiten wir neue Ergebnisse für positiv homogene NCP-Funktionen. Letztere Ergebnisse werden verwendet um zu zeigen, dass geeignete schwächeren differenzierbare Bedingungen (so dass eine lokale Q-quadratische Konvergenzgeschwindigkeit ermöglichen) für bestimmte Umformulierungen gelten.

La mise en oeuvre des véhicules électriques dans le secteur du transport de fret présente une solution durable qui répond aux objectifs environnementaux et économiques. Cette thèse s'oriente dans cette direction, elle porte sur l'étude des problèmes de transport électrique selon deux niveaux décisionnels à savoir le niveau stratégique et opérationnel.Au niveau stratégique, nous traitons le problème d'allocation des segments de recharge d'un véhicule électrique par des ondes électromagnétiques. Pour cela, nous proposons une modélisation du problème sous forme de programme mathématique mixte en nombre entier qui tient compte de la particularité du réseau routier et du véhicule. L'objectif est de déterminer; dans un réseau qui se compose de plusieurs chemins; une allocation stratégique qui constitue un compromis entre le coût d'achat du matériel de recharge et le coût de la batterie en satisfaisant un ensemble de contraintes liées au fonctionnement du système lors de l'exploitation et qui garantissent l'arrivée du véhicule à sa destination sans rupture de charge. Ainsi, nous montrons l'utilité de nos travaux dans un contexte industriel à travers le projet 'Green Truck'. Ce projet consiste à remplacer les camions à combustion par les camions électriques; adapté à la technologie d'alimentation par induction; dans la zone industrialo-portuaire du Havre. Dans cette optique et dans un premier temps, nous traitons le problème d'installation des segments de recharge dynamique. Dans un deuxième temps, nous intégrons le mode de rechargement statique dans la stratégie d'allocation. Nous adoptons la version multi-objective de l'algorithme d'optimisation par essaim de particules pour résoudre le problème. En effet, l'algorithme a montré sa robustesse et son efficacité vis-à-vis de problèmes d'optimisation non-linéaires. Après la linéarisation de notre modèle, nous comparons les résultats obtenus avec ceux issus à partir du solveur CPLEX. Nous montrons la validité des résultats obtenus à travers leur analyse et leur discussion.Au niveau opérationnel, nous étudions le problème de tournées de véhicules dans le cas d'une flott( mixte composée de véhicules électriques et à combustion, ce qui est un véritable réseau industrie rencontré dans la pratique. La particularité de notre travail réside dans la considération du cas où le émissions sont limitées par un système de plafonnement d'émissions pour les véhicule conventionnels. Afin de résoudre le modèle mathématique que nous avons élaboré, nous avons indu trois heuristiques dans l'algorithme SPEA-II qui répondent aux contraintes engendrées par la batterie limitée des véhicules électriques. Après l'analyse des performances de l'algorithme résultant, nou, concluons que l'approche de résolution permet d'achever des résultats compétitifs. / The implementation of electric vehicles in the freight transport sector presents a sustainable solution that meets environmental and economic objectives. This thesis is oriented in this direction, it deals with the study of the problems of electric transportation according to two decisional levels namely the strategic and operational levels.At the strategic level, we study the problem of the location of the wireless charging infrastructure in a transport network composed of multiple routes between the origin and the destination. To find a strategic solution to this problem, we first and foremost propose a nonlinear integer programming solution to reach a compromise between the cost of the battery, which is related to its capacity, and the cost of installing the power transmitters, while maintaining the quality of the vehicle's routing. Thus, we show the utility of our work in an industrial context through the 'Green Truck' project. This project consists of replacing diesel trucks by inductive trucks in the industrial-port area of Le Havre. Initially, we are dealing with the problem of allocation of dynamic charging segments. In a second step, we integrate the static reload mode in the allocation strategy. We adapt the multi-objective particle swarm optimization (MPSO) approach to our problem, as the particles were robust in solving nonlinear optimization problems. Since we have a multi-objective problem with two binary variables, we combine the binary and discrete versions of the particle swarm optimization approach with the multi-objective one. To assess the quality of solutions generated by the PSO algorithm, the problem is transformed into an equivalent linear programming problem and solved with CPLEX optimizer. The results are analyzed and discussed in order to point out the efficiency of our resolution method.At the operational level, we study a new version of the vehicle routing problem with a mix fleet of electric and combustion vehicles, which is a real industrial network encountered in practice. The particularity of our work lies in the consideration of the case where emissions are limited by an emission cap system for conventional vehicles. In order to solve the mathematical model that we have developed, we have included three heuristics in the SPEA-II algorithm that respond to the constraints generated by the limited battery of electric vehicles. After analyzing the performance of the resulting algorithm, we conclude that the resolution approach achieves competitive results.

Dealing with optimization problems with more than one objective has been an important research area in evolutionary computation. The class of multi-objective problems (MOPs) is an important one because multi-objectivity exists in almost all aspects of human life; whereby there usually exist several compromises in each problem. Multi-objective evolutionary algorithms (MOEAs) have been applied widely in many real-world problems. This is because (1) they work with a population during the course of action, which hence offer more flexible control to find a set of efficient solutions, and (2) real-world problems are usually black-box where an explicit mathematical representation is unknown. However, MOEAs usually require a large amount of computational effort. This is a sub- stantial challenge in bringing MOEAs to practice. This thesis primarily aims to address this challenge through an investigation into issues of scalability and the balance between exploration and exploitation. These have been outstanding research challenges, not only for MOEAs, but also for evolutionary algorithms in general. A distributed framework of local models using explicit niching is introduced as an overarching umbrella to solve multi-objective optimization problems. This framework is used to address the two-part question about first, the role of communication messages and second, the role of explicit niching in distributed evolutionary multi-objective optimization. The concept behind the framework of local models is for the search to be conducted locally in different areas of the decision search space, which allows the local models to be distributed on different processing nodes. During the optimization process, local models interact (exchange messages) with each other using rules inspired from Particle Swarm Optimization (PSO). Hence, the hypothesis of this work is that running simultaneously several search engines in different local areas is better for exploiting local information, while exchanging messages among those diverse engines can provide a better exploration strategy. For this framework, as the models work locally, they gain access to some global knowledge of each other. In order to validate the proposed framework, a series of experiments on a wide range of test problems was conducted. These experiments were motivated by the following studies which in their totality contribute to the verification of our hypothesis: (1) studying the performance of the framework under different aspects such as initialization, convergence, diversity, scalability, and sensitivity to the framework's parameters, (2) investigating interleaving guidance in both the decision and objective spaces, (3) applying local models using estimation of distributions, (4) evaluating local models in noisy environments and (5) the role of communication messages and explicit niching in distributed computing. The experimental results showed that: (1) the use of local models increases the chance of MOEAs to improve their performance in finding the Pareto optimal front, (2) interaction strategies using PSO rules are suitable for controlling local models, and that they also can be coupled with specialization in order to refine the obtained non-dominated set, (3) estimation of distribution improves when coupled with local models, (4) local models work well in noisy environments, and (5) the communication cost in distributed systems with local models can be reduced significantly by using summary information (such as the direction information naturally determined by local models) as the communication messages, in comparison with conventional approaches using descriptive information of individuals. In summary, the proposed framework is a successful step towards efficient distributed MOEAs.

The complex co-evolutionary systems approach -CCeSA - provides a well-suited framework for analysing agricultural systems, serving as a bridge between physical and socioeconomic sciences, alowing for the explaination of phenomena, and for the use of metaphors for thinking and action. By studying agricultural systems as self-generated, hierarchical, complex co-evolutionary farming systems - CCeFSs -, one can investigate the interconnections between the elements that constitute CCeFSs, along with the relationships between CCeFSs and other sytems, as a fundamental step to understanding sustainability as an emergent property of the system. CCeFSs are defined as human activity systems emerging from the purposes, gestalt, mental models, history and weltanschauung of the farm manager, and from his dynamic co-evolution with the environment while managing the resources at his hand to achieve his own multiple, conflicting, dynamic, semi-structured, and often incommensurable and conflicting purposes while performing above thresholds for failure, and enough flexibility to dynamically co-evolve with its changing biophysical and socioeconomic environment for a given future period. Fitness and flexibility are essential features of sustainable CCeFSs because they describe the systems' dynamic capacity to explore and exploit their dynamic phase space while co-evolving with it. This implies that a sustainable CCeFS is conceived as a set of dynamic, co-evolutionary processes, contrasting with the standard view of sustainability as an equilibrium or steady-state. Achieving sustainable CCeFSs is a semi-structured, constrained, multi-objective and dynamic optimisation management problem, with an intractable search space, that can be solved within CCeSA with the help of a multi-objective co-evolutionary optimisation tool. Carnico-ICSPEA2, a co-evolutionary navigator - CoEvoNav -used as a CCeSA's tool for harnessing the complexity of the CCeFS of interest and its environment towards sustainability, is introduced. The software was designed by its end-user - the farm manager and author of this thesis - as an aid for the analysis and optimisation of the San Francisco ranch, a beef cattle enterprise running on temperate pastures and fodder crops in the Central Plateau of Mexico. By combining a non-linear simulator and a multi-objective evolutionary algorithm with a deterministic and stochastic framework, the CoEvoNav imitates the co-evolutionary pattern of the CCeFS of interest. As such, the software was used by the farm manager to navigate through his CCeFS's co-evolutionary phase space towards achieving sustainability at farm level. The ultimate goal was to enhance the farm manager's decision-making process and co-evolutionary skills, through an increased understanding of his system, the co-evolutionary process between his mental models, the CCeFS, and the CoEvoNav, and the continuous discovery of new, improved sets of heuristics. An overview of the methodological, theoretical and philosophical framework of the thesis is introduced. Also, a survey of the Mexican economy, its agricultural sector, and a statistical review of the Mexican beef industry is presented. Concepts such as modern agriculture, the reductionist approach to agricultural research, models, the system's environment, sustainability, conventional and sustainable agriculture, complexity, evolution, simulators, and multi-objective optimisation tools are extensively reviewed. Issues concerning the impossibility of predicting the long-term future behaviour of CCeFSs, along with the use of simulators as decision support tools in the quest for sustainable CCeFSs are discussed. The rationale behind the simulator used for this study, along with that of the multi-objective evolutionary tools used as a makeup of Carnico-ICSPEA2 are explained. A description of the San Francisco ranch, its key on-farm sustainability indicators in the form of objective functions, constraints, and decision variables, and the semi-structured, multi-objective, dynamic, constrained management problem posed by the farm manager's planned introduction of a herd of bulls for fattening as a way to increase the fitness of his CCeFS via a better management of the system's feed surpluses and the acquisition of a new pick-up truck are described as a case study. The tested scenario and the experimental design for the simulations are presented as well. Results from using the CoEvoNav as the farm manager's extended phenotype to solve his multi-objective optimisation problem are described, along with the implications for the management and sustainability of the CCeFS. Finally, the approach and tools developed are evaluated, and the progress made in relation to methodological, theoretical, philosophical and conceptual notions is reviewed along with some future topics for research.

The complex co-evolutionary systems approach -CCeSA - provides a well-suited framework for analysing agricultural systems, serving as a bridge between physical and socioeconomic sciences, alowing for the explaination of phenomena, and for the use of metaphors for thinking and action. By studying agricultural systems as self-generated, hierarchical, complex co-evolutionary farming systems - CCeFSs -, one can investigate the interconnections between the elements that constitute CCeFSs, along with the relationships between CCeFSs and other sytems, as a fundamental step to understanding sustainability as an emergent property of the system. CCeFSs are defined as human activity systems emerging from the purposes, gestalt, mental models, history and weltanschauung of the farm manager, and from his dynamic co-evolution with the environment while managing the resources at his hand to achieve his own multiple, conflicting, dynamic, semi-structured, and often incommensurable and conflicting purposes while performing above thresholds for failure, and enough flexibility to dynamically co-evolve with its changing biophysical and socioeconomic environment for a given future period. Fitness and flexibility are essential features of sustainable CCeFSs because they describe the systems' dynamic capacity to explore and exploit their dynamic phase space while co-evolving with it. This implies that a sustainable CCeFS is conceived as a set of dynamic, co-evolutionary processes, contrasting with the standard view of sustainability as an equilibrium or steady-state. Achieving sustainable CCeFSs is a semi-structured, constrained, multi-objective and dynamic optimisation management problem, with an intractable search space, that can be solved within CCeSA with the help of a multi-objective co-evolutionary optimisation tool. Carnico-ICSPEA2, a co-evolutionary navigator - CoEvoNav -used as a CCeSA's tool for harnessing the complexity of the CCeFS of interest and its environment towards sustainability, is introduced. The software was designed by its end-user - the farm manager and author of this thesis - as an aid for the analysis and optimisation of the San Francisco ranch, a beef cattle enterprise running on temperate pastures and fodder crops in the Central Plateau of Mexico. By combining a non-linear simulator and a multi-objective evolutionary algorithm with a deterministic and stochastic framework, the CoEvoNav imitates the co-evolutionary pattern of the CCeFS of interest. As such, the software was used by the farm manager to navigate through his CCeFS's co-evolutionary phase space towards achieving sustainability at farm level. The ultimate goal was to enhance the farm manager's decision-making process and co-evolutionary skills, through an increased understanding of his system, the co-evolutionary process between his mental models, the CCeFS, and the CoEvoNav, and the continuous discovery of new, improved sets of heuristics. An overview of the methodological, theoretical and philosophical framework of the thesis is introduced. Also, a survey of the Mexican economy, its agricultural sector, and a statistical review of the Mexican beef industry is presented. Concepts such as modern agriculture, the reductionist approach to agricultural research, models, the system's environment, sustainability, conventional and sustainable agriculture, complexity, evolution, simulators, and multi-objective optimisation tools are extensively reviewed. Issues concerning the impossibility of predicting the long-term future behaviour of CCeFSs, along with the use of simulators as decision support tools in the quest for sustainable CCeFSs are discussed. The rationale behind the simulator used for this study, along with that of the multi-objective evolutionary tools used as a makeup of Carnico-ICSPEA2 are explained. A description of the San Francisco ranch, its key on-farm sustainability indicators in the form of objective functions, constraints, and decision variables, and the semi-structured, multi-objective, dynamic, constrained management problem posed by the farm manager's planned introduction of a herd of bulls for fattening as a way to increase the fitness of his CCeFS via a better management of the system's feed surpluses and the acquisition of a new pick-up truck are described as a case study. The tested scenario and the experimental design for the simulations are presented as well. Results from using the CoEvoNav as the farm manager's extended phenotype to solve his multi-objective optimisation problem are described, along with the implications for the management and sustainability of the CCeFS. Finally, the approach and tools developed are evaluated, and the progress made in relation to methodological, theoretical, philosophical and conceptual notions is reviewed along with some future topics for research.