As empresas lidam com grupos de clientes distintos, com requisitos que os diferem entre si, portanto é importante aperfeiçoar o atendimento destes clientes por meio de estratégias da cadeia de suprimentos que sejam diferenciadas para cada grupo. A escola enxuta-ágil, apesar de sugerir formas práticas de avaliação da cadeia de suprimento para a tomada de decisão, não oferece muitas opções para sua segmentação. Por outro lado, a proposta de segmentação da escola do alinhamento dinâmico é mais robusta, entretanto esta escola sofre com excessiva normatização, além da imprecisão inerente a seu processo de avaliação primordialmente qualitativo e de difícil aplicação. Uma alternativa para lidar com a imprecisão relativa ao processo de segmentação é a aplicação da teoria dos conjuntos fuzzy. Nesse contexto, este trabalho tem por objetivo desenvolver um modelo quantitativo que utilize a teoria dos conjuntos fuzzy e, com base em dados de vendas, avalie a(s) cadeia(s) de suprimentos da empresa facilitando esta alcançar o alinhamento dinâmico. Os procedimentos de pesquisa utilizados no trabalho podem ser agrupados em três partes: pesquisa bibliográfica, desenvolvimento do modelo quantitativo axiomático descritivo e ilustração por meio de aplicação prática. O modelo computacional desenvolvido colaborou com a busca do alinhamento dinâmico. Obteve-se a identificação das cadeias de suprimentos que atendem aos grupos de clientes avaliados, fornecendo respostas de forma muito mais rápida que a análise proposta pelos modelos encontrados na literatura. A aplicação em caso real validou o modelo, uma vez que os resultados obtidos mostraram-se coerentes com a realidade apontada pelos especialistas da empresa estudada, indicando possíveis ações para o realinhamento da cadeia de suprimentos. / Companies deal with different customer groups, with requirements that differ between them, so it is important to improve customer service through different supply chain strategies for each group. The Leagile School, while suggesting practical ways of assessing the supply chain for decision-making, does not offer many options for its segmentation. The segmentation proposal of Dynamic Alignment School is more robust, however, this school is excessively normative, besides the vagueness inherent in its evaluation process that is primarily qualitative and difficult to apply. An alternative to deal with imprecision related to the segmentation process is the application of fuzzy set theory. In this context, the objective of this work is to develop a quantitative model that uses the fuzzy set theory and, based on sales data, assess the company\'s supply chain(s), facilitating the achievement of the dynamic alignment. The research procedures applied in the work can be grouped into three parts: bibliographic research, development of the descriptive axiomatic quantitative model, and illustration through practical application. The computational model developed collaborated with the search for dynamic alignment. It was possible to identify the supply chains that serve the client groups evaluated, providing answers faster than the analysis proposed by the models found in the literature. The application in real situation validated the model, since the results obtained were consistent with the reality pointed out by the experts of the company studied, indicating possible actions for the realignment of the supply chain.

Artificial intelligence techniques have been widely used for prediction in various areas of sciences and engineering. In the thesis, applications of AI techniques are studied to predict the dilution of industrial outfall discharges. The discharge of industrial effluents from the outfall systems is broadly divided into two categories on the basis of density. The effluent with density higher than the water receiving will sink and called as negatively buoyant jet. The effluent with density lower than the receiving water will rise and called as positively buoyant jet. The effluent discharge in the water body creates major environmental threats. In this work, negatively buoyant jet is considered. For the study, ANFIS model is taken into consideration and incorporated with algorithms such as GA, PSO and FFA to determine the suitable model for the discharge prediction. The training and test dataset for the ANFIS-type models are obtained by simulating the jet using the realizable k-ε turbulence model over a wide range of Froude numbers i.e. from 5 to 60 and discharge angles from 20 to 72.5 degrees employing OpenFOAM platform. Froude number and angles are taken as input parameters for the ANFIS-type models. The output parameters were peak salinity (Sm), return salinity (Sr), return point in x direction (xr) and peak salinity coordinates in x and y directions (xm and ym). Multivariate regression analysis has also been done to verify the linearity of the data using the same input and output parameters. To evaluate the performance of ANFIS, ANFIS-GA, ANFIS-PSO, ANFIS-FFA and multivariate regression model, some statistical parameters such as coefficient of determination (R2), root mean squared error (RMSE), mean absolute error (MAE) and average absolute deviation in percentage are determined. It has been observed that ANFIS-PSO is better in predicting the discharge characteristics.

Precision agriculture requires detailed and timely information about field condition. In less than the short flight time a UAV (Unmanned Aerial Vehicle) can provide, an entire field can be scanned at the highest allowed altitude. The resulting NDVI (Normalized Difference Vegetation Index) imagery can then be used to classify each point in the field using a FIS (Fuzzy Inference System). This identifies areas that are expected to be similar, but only closer inspection can quantify and diagnose crop properties. In the remaining flight time, the goal is to scout a set of representative points maximizing the quality of actionable information about the field condition. This quality is defined by two new metrics: the average sampling probability (ASP) and the total scouting luminance (TSL). In simulations, the scouting flight plan created using a GA (Genetic Algorithm) significantly outperformed plans created by grid sampling or human experts, obtaining over 99% ASP while improving TSL by an average of 285%.

Battery energy management plays a crucial role in fuel economy improvement of charge-sustaining parallel hybrid electric vehicles. Currently available control strategies consider battery state of charge (SOC) and driver’s request through the pedal input in decision-making. This method does not achieve an optimal performance for saving fuel or maintaining appropriate SOC level, especially during the operation in extreme driving conditions or hilly terrain. The objective of this thesis is to develop a control algorithm using forthcoming traffic condition and road elevation, which could be fed from navigation systems. This would enable the controller to predict potential of regenerative charging to capture cost-free energy and intentionally depleting battery energy to assist an engine at high power demand. The starting point for this research is the modelling of a small sport-utility vehicle by the analysis of the vehicles currently available in the market. The result of the analysis is used in order to establish a generic mild hybrid powertrain model, which is subsequently examined to compare the performance of controllers. A baseline is established with a conventional powertrain equipped with a spark ignition direct injection engine and a continuously variable transmission. Hybridisation of this vehicle with an integrated starter alternator and a traditional rule-based control strategy is presented. Parameter optimisation in four standard driving cycles is explained, followed by a detailed energy flow analysis. An additional potential improvement is presented by dynamic programming (DP), which shows a benefit of a predictive control. Based on these results, a predictive control algorithm using fuzzy logic is introduced. The main tools of the controller design are the DP, adaptive-network-based fuzzy inference system with subtractive clustering and design of experiment. Using a quasi-static backward simulation model, the performance of the controller is compared with the result from the instantaneous control and the DP. The focus is fuel saving and SOC control at the end of journeys, especially in aggressive driving conditions and a hilly road. The controller shows a good potential to improve fuel economy and tight SOC control in long journey and hilly terrain. Fuel economy improvement and SOC correction are close to the optimal solution by the DP, especially in long trips on steep road where there is a large gap between the baseline controller and the DP. However, there is little benefit in short trips and flat road. It is caused by the low improvement margin of the mild hybrid powertrain and the limited future journey information. To provide a further step to implementation, a software-in-the-loop simulation model is developed. A fully dynamic model of the powertrain and the control algorithm are implemented in AMESim-Simulink co-simulation environment. This shows small deterioration of the control performance by driver’s pedal action, powertrain dynamics and limited computational precision on the controller performance.

The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

The few microcontroller based active/semi-active prosthetic knee joints available commercially are extremely expensive and do not consider the uncertainties of inputs sensory information. Progressing in the controller of the current prosthetic devices and creating artificial lower limbs compatible with different users may lead to more effective and low-cost prostheses. This can affect the life style of lots of amputees specially the land-mine victims in developing war-torn countries who are unable to partake in the advancement of the current intelligent prosthetic knees. The purpose of the proposed Active Prosthetic Knee (APK) design is to investigate a new schema that allows the device to provide the full necessary torque at the knee joint based on echoing the state of the intact leg. This study involves the design features of the mechanical aspects, sensing system, communication, and knowledge-based controller to implement a cost-effective APK. The proposed microcontroller based prosthesis utilizes a ball screw system accompanied by a high-speed brushed servomotor to provide one degree of freedom for the fabricated prototype. Moreover, a modular test-bed is manufactured to mimic the lower limb motion which contributes investigating different controllers for the prototype. Thus, the test bed allows assessing the primary performance of the APK before testing on a human subject. Different types of sensing systems (electromyography and lower limb inclination angles) are investigated to extract signals from the user’s healthy leg and send the captured data to the APK controller. The methodology to measure each type of signal is described, and comparison analyses are provided. Wireless communication between the sensory part and actuator is established. A knowledge-based control mechanism is developed that takes advantage of an Adaptive-Network-based Fuzzy Inference System (ANFIS) to determine knee torque as a function of the echoing angular state of the able leg considering the uncertainty of inputs. Therefore, the developed controller can make the APK serviceable for different users. The fuzzy membership function’s parameters and rules define the knowledge-base of the system. This knowledge is based on existing experience and known facts about the walking cycle.

Trip planning is a well cited problem for which various solutions have been reported in the literature. This problem has been typically addressed, to a large extent, as a shortest distance path planning problem. In some scenarios, the concept of shortest path is extended to reflect temporal objectives and/or constraints. This work takes an alternative perspective to the trip planning problem in the sense it being situation aware. Thus, allowing multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the trip and the traveler. The work in this thesis introduces TSADA (Traveler Situation Awareness and Decision Aid) system. TSADA is designed as a modular system that combines linguistic situation assessment with user-centric decision-making. The trip planning problem is modeled as a graph G. The objective is to find a route with the minimum cost. Both hard and soft objective/attributes are incorporated. Soft objective/attributes such as safety, speed and driving comfortability are described using a linguistic framework and processed using hierarchical fuzzy inference engine. A user centric situation assessment is used to compute feasible routes and map them into route recommendation scheme: recommended, marginally recommended, and not recommended. In this work, we introduce traveler's doctrines concept. This concept is proposed to make the process of situation assessment user centric by being driven by the doctrine that synthesizes the user's specific demands. Hard attributes/objectives, such as the time window and trip monitory allowances, are included in the process of determining the final decision about the trip. We present the underline mathematical formulation for this system and explain the working of the proposed system to achieve optimal performance. Results are introduced to show how the system performs under a wide range of scenarios. The thesis is concluded with a discussion on findings and recommendations for future work.

Adverse weather such as hurricanes can significantly affect the reliability of composite power systems. Predicting the impact of hurricanes can help utilities for better preparedness and make appropriate restoration arrangements. In this dissertation, the impact of hurricanes on the reliability of composite power systems is investigated. Firstly, the impact of adverse weather on the long-term reliability of composite power systems is investigated by using Markov cut-set method. The Algorithms for the implementation is developed. Here, two-state weather model is used. An algorithm for sequential simulation is also developed to achieve the same goal. The results obtained by using the two methods are compared. The comparison shows that the analytical method can obtain comparable results and meantime it can be faster than the simulation method. Secondly, the impact of hurricanes on the short-term reliability of composite power systems is investigated. A fuzzy inference system is used to assess the failure rate increment of system components. Here, different methods are used to build two types of fuzzy inference systems. Considering the fact that hurricanes usually last only a few days, short-term minimal cut-set method is proposed to compute the time-specific system and nodal reliability indices of composite power systems. The implementation demonstrates that the proposed methodology is effective and efficient and is flexible in its applications. Thirdly, the impact of hurricanes on the short-term reliability of composite power systems including common-cause failures is investigated. Here, two methods are proposed to archive this goal. One of them uses a Bayesian network to alleviate the dimensionality problem of conditional probability method. Another method extends minimal cut-set method to accommodate common-cause failures. The implementation results obtained by using the two methods are compared and their discrepancy is analyzed. Finally, the proposed methods in this dissertation are also applicable to other applications in power systems.

In this thesis we have considered a cognitive radio network (CRN) with a pair of primary user (PU) and secondary user (SU) in spectrum sharing networks in path-loss and without path-loss propagation environments under identically distributed m-Nakagami fading channel. The thesis consists of three parts. In the first part we propose an optimized Takagi-Sugeno Fuzzy Inference System (FIS) based power control strategy in cognitive radio networks (CRN) in spectrum sharing network in without path-loss propagation environment. The second part proposes an optimized Takagi-Sugeno FIS based power control strategy in cognitive radio networks in spectrum sharing network in path-loss propagation environment. For without path-loss propagation environment the proposed FIS takes the interference channel gain ratio between SU transmitter (CUtx) and PU receiver (PUrx) and Signal to Noise Ratio (SNR) towards PU transmitter (PUtx) as antecedents and outputs the power scaling factor for SU. For path-loss propagation environment the proposed FIS takes the relative distance ratio between CUtx and PUrx and SNR towards PUtx as antecedents and outputs the power scaling factor for SU. The output power scaling factor is used to vary the transmit power of SU such that it does not degrade the quality of service (QoS) of PU link. The third part presents an implementation of orthogonal frequency division multiplexing (OFDM) transmission technique in CRN. The OFDM technique has intellectual attractive features like coping with the inter symbol interference (ISI), while providing increasing spectral efficiency and improved performance. This can be used in emergency conditions where transmission requires reliability and high data rate. The OFDM transmission technique is applied towards SU transmitter in CRN, which enables SU to utilize the spectrum efficiently under various fading environments. Spectrum sharing networks in with and without path-loss propagation environments and OFDM transmission were tested for bit error rate (BER) performance after fading effects from m-Nakagami fading channel. We conclude that by applying Takagi-Sugeno Fuzzy Inference System (FIS) based power control strategy we can improve the BER performance of PU when compared with no power control strategy and with other fuzzy based power control technique. OFDM transmission technique gives us better data rate and slightly improved BER in CRN hence making it suitable for use in emergency conditions. / mobile: 0735032048 (Muneeb Goraya)

Trip planning is a well cited problem for which various solutions have been reported in the literature. This problem has been typically addressed, to a large extent, as a shortest distance path planning problem. In some scenarios, the concept of shortest path is extended to reflect temporal objectives and/or constraints. This work takes an alternative perspective to the trip planning problem in the sense it being situation aware. Thus, allowing multitudes of traveler centric objectives and constraints, as well as aspects of the environment as they pertain to the trip and the traveler. The work in this thesis introduces TSADA (Traveler Situation Awareness and Decision Aid) system. TSADA is designed as a modular system that combines linguistic situation assessment with user-centric decision-making. The trip planning problem is modeled as a graph G. The objective is to find a route with the minimum cost. Both hard and soft objective/attributes are incorporated. Soft objective/attributes such as safety, speed and driving comfortability are described using a linguistic framework and processed using hierarchical fuzzy inference engine. A user centric situation assessment is used to compute feasible routes and map them into route recommendation scheme: recommended, marginally recommended, and not recommended. In this work, we introduce traveler's doctrines concept. This concept is proposed to make the process of situation assessment user centric by being driven by the doctrine that synthesizes the user's specific demands. Hard attributes/objectives, such as the time window and trip monitory allowances, are included in the process of determining the final decision about the trip. We present the underline mathematical formulation for this system and explain the working of the proposed system to achieve optimal performance. Results are introduced to show how the system performs under a wide range of scenarios. The thesis is concluded with a discussion on findings and recommendations for future work.