Optimisation

In the construction project, time and cost are the most important factors to be considered in the planning of every project. The aim is to finish the projects on time, within budget and to achieve other project objectives. Time loss is costly and it creates a problem which is related to costs. It increases cost with increasing in a time of particular activity. Due to this, the total budget of the project gets collapsed.so Time-cost optimization is essential for construction projects. The objective of time-cost optimization is to determine the optimum project duration corresponding to the minimum total cost.

In modern systems, electrical power distribution and actuation replace mechanical, hydraulic or pneumatic systems. An estimated 30% of world’s electrical energy was processed by power electronic systems in 2005, with a predicted increase to 80% by 2030. An understanding of future capabilities of power electronics & bottlenecks limiting increases in performance of such systems is required. Designing a filter/converter is a complex engineering task that balances many interacting and often conflicting pressures. A computational tool that accounts for them while seeking optimal (power-dense) designs is of high value for designers.

The paper presents a modeling approach related to High Velocity Oxygen-Fuel (HVOF), and similar spraying processes based on combustion. The model and related software allow analysis of temperature and velocity distributions of combustion products and spraying particles inside an HVOF torch as well as downstream of a barrel exit. Interaction of the two-phase spray jet and a substrate is a part of the model too. The main input parameters are the following: type of a fuel, fuel and oxygen flow rates, the torch thermal efficiency, throat diameter, barrel length and diameter, geometry of the expansion, position and diameter of powder injectors, composition and size of the spraying particles, and spraying distance.. A capability of the software is illustrated by the results of calculations regarding the influence of some of the main input parameters on particle temperature and velocity. The results of the modeling are in a good agreement with the published experimental data. The modeling has been successfully used for a modification of a HVOF torch and an improvement of its performance.

This research examined the full range of tasks and activities that design engineers perform, how their working time is distributed among these, and how these issues influence their satisfaction with their work. Seventy-eight design engineers each carried a personal digital assistant (PDA) for twenty working days. Once every hour, they entered data into their PDAs concerning their current work tasks and satisfaction levels, using a work sampling approach. Work tasks were explored from multiple perspectives, yielding highly extensive and detailed results. A key finding was that design engineers' work involves considerable technical engineering work (62.92% of time) and socially collaborative work (40.37% of time). The results were discussed in terms of their implications for academic theory and organizational practice.

This paper discusses the methodology of developing models for the turning process for machining Inconel 718 alloy with coated carbide tool inserts. Approach through Genetic programming (GP) was aimed at with an overall objective of optimizing the process to yield higher metal removal, better surface quality and lower cutting forces. Taguchi’s approach was adopted for the design of experiments and accordingly experiments were carried out, for collecting experimental data in a controlled manner. Such data was grouped separately at random for training genetic models (the models developed using GP) and further validating them. Models generated establish relationship between the input parameters with the output parameter and also show the order of dominance of the input parameters. Prediction of the output parameters is in fair comparison with the actual measured values. For easy understanding and explicit depiction, plots were made for each output parameter. Plots made show the trend of the prediction capability of the models in both the training set and validation set of data; further the plots exhibit the trend of variables’ interaction in the process. This work resulted in developing models for the turning process for Inconel 718 Alloy in a scientific manner. It also enables identifying the optimised set of turning parameters for Inconel 718 material using coated carbide tools, to achieve better surface roughness and higher material removal. This work gains significance in the sense with reasonably minimum number of experiments, reliable model has been generated, validated and further, the process has been optimised with two objectives (viz. improved quality and higher productivity).

In this review, we survey the widespread use of optimisation or mathematical programming approaches in the upstream sector of the petroleum industry, specifically to problems in the area of (1) production systems design and operations, (2) lift gas and production rate allocation and (3) reservoir development, planning, management and optimisation. Early applications have adopted Linear Programming (LP) alongside heuristics-based methods, but the recent ongoing explosion in computing power and advances in optimisation, simulation and computational techniques have enabled the adoption of increasingly complex models. These formulations include non-linear programming and Mixed-Integer Linear (MILP) and Non-Linear (MINLP) programming models. Within these representations, various algorithms and approaches have been employed, for example, metaheuristics such as genetic algorithms to address non-smooth objective functions; techniques for simultaneous decision making in design, planning and scheduling and stochastic programming to handle uncertainty in reservoir information, with the ultimate aim of improving solution quality while reducing computational intensity.

In previous work, a method based in non-linear programming for finding the minimum possible lap time for a given "virtual" car on a given "virtual" circuit has been described. Results have demonstrated the repeatability and accuracy of the process. The modelling, simulation and optimisation scheme has been applied to finding the fastest lap times for the Formula One circuits in Barcelona and Suzuka for a representative car with wide variations in the longitudinal positioning of the mass centre. Results show the increasing difficulty of solving the simulation/optimisation problem as the rearward weight bias increases, changes in the optimal control strategies as the car front to rear balance changes, tyre shear force utilisation factors in the different cases and the potential advantage of cars with rearward mass centre locations. Comparative results are given and the optimised behaviour fully discussed.

The rising demand for an energy efficient and eco-friendly mode of underwater propulsion has led to the development of bio-mimetic robots that adopt the swimming modes of real fishes. This paper deals with the parameter optimization of the bio-mimetic fin of a knife fish robot. The optimization is done to enhance the capability of the robot to generate wide range of undulations. A knife fish robot was fabricated and tested in a pool for different types of waves. The response of each wave on the swimming performance of the robot was studied to obtain the maximum thrust generating wave.

This paper studies the Surpac and Whittle software and their application in designing an optimised pit. Exploration data from Mpeasem Gold Mining Project (MGMP) is used as the primary input for the work. The work entails: block modelling of MGMP deposit; pit optimisation; analysis of the Pit NPV’s sensitivity to changes in gold price and mining cost; and detailed pit design. The deposit has undergone intense weathering, forming an oxidised gold deposit up to about 50 m in depth. Sections through the deposit were used to create a solid model of the orebody, which was divided into blocks to form a block model comprising unit blocks measuring 20 m x 20 m x 10 m. The block model grade was estimated using the Inverse Distance Weighting (IDW) method, giving an average grade of 1.533 g/t with 22.79 Mt of ore. During the optimisation, a total of 82 optimal pit outlines were generated using the 3D Lerchs-Grossmann algorithm. Pit 36 was chosen having the highest the Net Present Value (NPV) @ 10% of $338.60 million. The optimal pit had 21.19 Mt of ore at an average grade of 1.557 g/t. The NPV was very sensitive to gold price changes but marginally sensitive to mining cost changes. From the optimal pit, a detailed pit designed with circular ramp was selected over one with all-cut ramp since it had a higher expected revenue and lower stripping ratio. It is concluded that Surpac and Whittle software combine as a powerful tool for designing an optimal pit.

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization. Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.

The manufacturing cost of WT blade is about 15-20% of wind turbine production cost. The expenses of innovations in design of blades represent the small amount of overall cost of wind turbine production. Profits coming from better structural model, use of suitable composite materials and better techniques of manufacturing, both the blades and composite materials, causes necessity of application of numerical modeling and optimization techniques.

De leeftijdsorganisatie van leerlingen in scholen heeft onder meer tot gevolg dat de minst èn meest ontwikkelde of gevorderde leerlingen in elke groep of klas mèèr schoolproblemen ervaren dan gemiddelde leerlingen. Via ‘Optimaliserend Onderwijs’ kan onderwijskwaliteit voor elke leerling worden gerealiseerd. Dit vraagt samenhangende onderwijsveranderingen en passende schoolontwikkeling. In deze handleiding wordt verhelderd welke veranderingen nodig zijn en hòe deze optimaliserend kunnen worden ontwikkeld in scholen voor basis- en voortgezet onderwijs. Een en ander wordt ook verduidelijkt met praktijkvoorbeelden.

Although the use of computer-software for design analyses of fluid-thermal systems has become common place in the industry, most dedicated industrial applications are not suitable for educational purposes because they do not allow the students to develop their own analytical models from the basic principles apart from being expensive to acquire and require proper training to use. In this respect, general-purpose applications such as Microsoft Excel can be more useful than the dedicated industrial applications. This book illustrates the use of an Excel-based modelling platform for optimisation analyses of fluid-thermal systems. While Excel with its user-interface, built-in functions, and graphical tools is the backbone of the modelling platform, what makes the platform adequate for optimisation analyses of fluid-thermal systems are (i) the Solver add-in that comes with Excel, (ii) an add-in called Thermax developed by the author for determining fluid properties, and (iii) Visual Basic for Applications (VBA) programming language that also comes with Excel. While the functions provided by Thermax are adequate for all the analyses presented in this book, VBA is useful for the development of additional custom functions if required. The developers of the MIDACO solver have made available a free-to-download version for Excel users that is used for the multi-objective optimisation analyses presented in the last three chapters of the book.

Light is used to illuminate objects in the built environment. Humans can only observe light reflected from an object. Light absorbed by an object turns into heat and does not contribute to visibility. Since the spectral output of the new lighting technologies can be tuned, it is possible to imagine a lighting system that detects the colours of objects and emits customised light to minimise the absorbed energy. Previous optimisation studies investigated the use of narrowband LEDs to maximise the efficiency and colour quality of a light source. While these studies aimed to tune a white light source for general use, the lighting system proposed here minimises the energy consumed by lighting by detecting colours of objects and emitting customised light onto each coloured part of the object. This thesis investigates the feasibility of absorption-minimising light source spectra and their impact on the colour appearance of objects and energy consumption. Two computational studies were undertaken to form the theoretical basis of the absorption-minimising light source spectra. Computational simulations show that the theoretical single-peak spectra can lower the energy consumption up to around 38 % to 62 %, and double-peak test spectra can result in energy savings up to 71 %, without causing colour shifts. In these studies, standard reference illuminants, theoretical test spectra and coloured test samples were used. These studies are followed by the empirical evidence collected from two psychophysical experiments. Data from the experiments show that observers find the colour appearance of objects equally natural and attractive under spectrally optimised spectra and reference white light sources. An increased colour difference, to a certain extent, is found acceptable, which allows even higher energy savings. However, the translucent nature of some objects may negatively affect the results.

The aim of this paper is to determine the possibility of the stand-alone hybrid renewable energy system which able to satisfy the electric load demand by using HOMER Software. Besides, it is always desirable to have an environmental friendly energy system in order to maintain its natural beauty and prevent the effect of global warming. As the project economics is concerned, it is necessary to indentify the system life cycle cost or Total Net Present Cost, TNPC, of the optimal hybrid energy system, where this also can be analysing by optimization in HOMER. For observing the realistic effect has on power system designed and it economics, it is also important to assess the effect of uncertainty or a change in sensitivity variable such wind speed and fuel price which this can be done by performing sensitivity analysis. Finally, this paper is an output of a final year project to support the involving of using HOMER package in our undergraduate education at Brunel University.

ISO 26262, titled Road Vehicles–Functional Safety, is the new automotive functional safety standard for passenger vehicle industry. In order to accomplish the goal of designing and developing dependable automotive systems , ISO 26262 uses the concept of Automotive Safety Integrity Levels (ASILs), the adaptation of Safety Integrity Levels. ASILs are allocated to the components and subsystems that can cause system failure and malfunctions that lead to hazards. ASILs allocation is a hard problem consists of finding the optimal allocation of safety levels to the system architecture which must guarantee that the highest safety requirements are met while development cost of the automotive system is kept minimum. There were many successful attempts to solve this problem using different techniques. However, it is worth pointing out that there is an absence of a review that provides an in-depth study of all the existing methods and highlights their merits and demerits. This paper presents an overview of different approaches that were used to solve ASILs allocation problem. The review provides an overview of safety requirements including the related standards followed by a study of the resolution methods of the existing approaches. The study of each approach provides a detailed explanation of the used methodology and a discussion of its strength and weaknesses including the main open challenges.

Optimising stope design is an intricate element of underground mine planning where optimal designs are expected to integrate multiple technical aspects. Orebody uncertainty is a critical aspect affecting the forecasted performance of designs and is linked to the failing of meeting production targets and project financial expectations. Following recent conceptual developments in open pit mining, the present paper develops and explores a new probabilistic mixed integer programming model developed to optimise stope designs, including size, location and number of stopes under consideration of grade uncertainty and predefined levels of acceptable risk. An application of the method at Kidd Creek Mine, Ont., Canada, demonstrates its practical aspects including risk quantification for contained ore tonnes, grade and economic potential.

A strategy for adaptive control and energetic optimization of aerobic fermentors was implemented, with both air flow and agitation speed as manipulated variables. This strategy is separable in its components: control, optimization, estimation. We optimized parameter’s estimation (from the usual KLa correlation) using sinusoidal excitation of air flow and agitation speed. We have implemented parameter’s estimation trough recursive least squares algorithm with forgetting factor. We carried separate essays on control, optimization and estimation algorithms. We carried our essays using an original computational simulation environment, with noise and delay generating facilities for data sampling and filtering.

Our results show the convergence and robustness of the estimation algorithm used, improved with use of both forgetting factor and KLa dead-band facilities. Control algorithm used in our work compares favorably with PID using the integrated area criteria for deviation between oxygen molarity and critical molarity (set point). Optimization algorithm clearly reduces energetic consumption, respecting critical molarity. Integration of control, optimization and adaptive algorithms was implemented, but future work is needed for stability. Methods were defined and implemented for stability improvement. We have implemented data acquisition and computer manipulation of air flow and agitation speed for actual fermentors.

Operations Research and Applications: An International Journal (ORAJ) is a quarterly open access peer-reviewed journal that publishes articles which contribute new results in all areas of the Operations Research and innovative Applications. The journal focuses on all technical and practical aspects of Operations Research, applications and Implementation techniques. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on understanding advances in Operations Research and establishing new collaborations in these areas. Authors are solicited to contribute to the journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Operations Research and Applications but are not limited to, the following

This paper describes how Okumura Hata's model is chosen and optimized for urban outdoor coverage in the Code Division Multiple Access (CDMA) system operating in 800MHz UHF frequency band, South-south Nigeria. This optimized path loss model is based on the empirical measurements collected in the CDMA network focusing on the city centre of Benin, Edo state. It is developed by comparing the calculated path loss from collected measurements with the well-known path loss models within applicable frequency range of CDMA system, such as Hata, SUI, Lee, and Egli's Model. The Hata model was chosen as a reference for this optimized path loss model development based on the closest path loss exponent and smallest mean error as compared to the measured path loss. This optimized Hata model is implemented in the path loss calculation during the validation process. Thus, this optimized model is successfully improved and would be more reliable to be applied in the Nigeria CDMA system for urban path loss calculation in the 800MHz frequency band.

In recent days, the silicon carbide particulate reinforced aluminium metal matrix composites are most promising material in various engineering applications due to their strength to weight ratio, wear resistance and thermal resistance over the non-reinforced alloys. However, these materials are very difficult to cut by conventional machining methods due to the presence of silicon carbide particles. To overcome this limitation, the wire electrical discharge machining (WEDM) is employed to machine these composites. The aim of this study is to optimize the process parameters in wire electrical discharge machining (WEDM) of Al6061/SiCp composite using AHP-TOPSIS method. Al 6061/2% SiCp/3 µm particulate metal matrix composite is fabricated by using stir casting method and the uniformity of particle distribution was analyzed by SEM. Taguchi L 18 orthogonal array is designed by considering various process parameters viz. Wire Type (WT), Pulse ON Time (T ON), Pulse OFF Time (T OFF), Wire Feed rate (WF) and Sensitivity (S) for conducting WEDM experiments. The obtained experimental results were analyzed and the results revealed that Sensitivity (S) is the prevailing factor on the response characteristics of WEDM followed by pulse ON time (T ON), wire feed rate (WF), Wire Type (WT) and pulse OFF time (T OFF).

Manufacturing processes have never been perfect. It is often inefficient requiring a lot of input. Lean manufacturing is a technique to reduce wastes in manufacturing. These wastes generally include various non-value added activities like over processing, over production, waiting, unnecessary motion, nonconformance, unwanted transportation, excessive inventory. The refinement intended to reduce the costs of manufacturing and time. The time and resources for manufacturing is too precious today, wasting them is unthinkable. Optimum use of these time and resources increases the productivity and profitability of the company also increases the company's competitiveness. A lean manufacturing tool known as value stream mapping is used to identify these non-value added activities and to eliminate them. Also design of experiments is employed to find out the factors to turn the manufacturing process outputs optimum. A tire manufacturing company is studied and optimised in this thesis using ARENA simulation, Value stream mapping and Taguchi method. The value stream map is created using Edraw Max and design of experiments is performed MINITAB.

Fossil fuel depletion, global warming with rapid changes in climate, and increases in oil prices have motivated scientists to search for alternative fuel. Biodiesel can be an effective solution despite some limitations, such as poor fuel properties and engine performance. From this perspective, experiments were carried out to improve fuel properties and engine performance by using a binary blend of palm and coconut biodiesel at an optimized ratio. MATLAB optimization tool was used to determine this blend ratio. A new biodiesel was developed and represented by PC (optimum blend of palm and coconut biodiesel). Engine performance and emission were tested under a full load at variable speed condition by using a 20% blend of each biodiesel with petroleum diesel, and the results were compared with petroleum diesel under both turbocharged and non-turbocharged conditions. PC20 (blend of 20% PC biodiesel and 80% petroleum diesel) showed the highest engine power with lower brake-specific fuel consumption than the other tested fuels in the presence of a turbocharger. The emissions of PC20 were lower than those of all other tested fuels. The experimental analysis reveals that PC showed superior performance and emission over palm biodiesel blend.

Bat Algorithm (BA) is a simple and effective global optimization algorithm which has been applied to a wide range of real-world optimisation problems. Various extensions to Bat algorithm have been proposed in the past; prominent amongst them being ShBAT. ShBAT is a hybrid between BA and Shuffled Frog Leaping Algorithm-SFLA; a memetic algorithm based on food search behavior of frogs. ShBAT integrates the shuffling and reorganization technique of SFLA to enhance the exploitation capabilities of BAT. This paper proposes Enhanced Shuffled Bat algorithm (EShBAT) an extension to ShBAT. In ShBAT, different memeplexes evolve independently, with different cultures. EShBAT improves the exploitation capabilities of ShBAT by grouping together the best of each memeplex to form a super-memeplex. This super-memeplex evolves independently to further exploit the best solutions. The performance of EShBAT is verified over 30 well-known benchmark functions. Experimental results indicate a significant improvement of EShBAT over BA and ShBAT.

This paper addresses a control frequency scheme of the microgrid system using a fractional order PID controller. The proposed Microgrid system is consisted of a Photovoltaic System, Wind Turbine Generator, Diesel Engine Generator, Fuel Cell, and different storage systems like Battery Energy Storage Systems, and Flywheel Energy Storage Systems. The principal objective of the present paper is to limit the frequency and power deviations by the application of the proposed controller which has five parameters to be determined through optimization techniques. Krill Herd algorithm is used for determining the optimum fractional order PID controller parameters using the Integral of Squared Error. A comparison between the Genetic Algorithm and Krill Herd is done, and the obtained simulation results presents that the investigated controller-based Krill Herd outperforms the Genetic Algorithm in terms of fewer fluctuations in power and frequency deviation. 1. INTRODUCTION Today the system control and stability are leading greater interest in the use of microgrid. Renewable Energy Sources are the energy produced by natural sources such as wind, sunlight, rain, lids and geothermal heat which are mostly used in power generation [1]. Renewable energy generation like wind and photovoltaic are the most important sources because of the depletion of fossil sources in the future and the greenhouse caused by gas emissions which have a dangerous effect on the environment situation. Renewable energy resources (RES) lead to reduce the focus on the use of fossil fuel sources [2-3]. Recently the frequency regulation is considered a big challenge related to using the microgrid system, due to the microgrid complexity, structure, and emergence of stochastic renewable energy sources. Frequency and power system stability is noted as an important problem in an electrical microgrid. The Microgrid system comprises two renewable sources like a wind turbine generator (WTG), solar generation, fuel cell (FCS), diesel engine generator (DEG), Battery Energy Storage Systems (BESS) and Flywheel Energy Storage Systems (FESS). The FESS is being selected for its efficiency and the short-time response [4-5]. The microgrid can either operated in two modes interconnected or isolated from the utility grid. The advantage of the microgrid system is its suitability for providing the energy demand in the rural and isolated areas and helping the main grid in the connected model [6]. Unbalance between generation and load provokes the fluctuation of the system frequency and power which results in some control difficulties.

Offshore wind farm managers and schedulers need to manage large numbers of wind turbine visits every day, in order to: repair minor faults; conduct inspections; and perform scheduled service operations. Daily schedules form a choice of which maintenance activities to conduct, taking account of: constraints on weather conditions, shifts, vessel and technician capabilities and availability; and the impact of activities on wind farm profitability. This forms a formidable optimisation challenge that today is solved " by hand " by a scheduler. The work presented here contains three aspects of importance. First, a powerful and flexible meta-heuristic optimisation model is developed to solve this problem, where the simulation algorithms and objective can be altered without any change to the optimiser. Second, a practical valuation methodology is developed, where historic wind farm data can be used to identify strengths and weaknesses in any maintenance planning method and estimate financial return on investment from implementation. Finally, the methodology described is implemented and tested, by applying the valuation methodology to data from the Princess Amalia Wind Park in The Netherlands. Even given the limited scope of this case study, automating daily maintenance planning can bring significant financial benefits: 302 kV over 5 months.

Laser micro-drilling is potent enough to replace the mechanical removal methods in many industrial applications especially for machining of difficult-to-cut materials like ceramics and composites. In the present study, laser micro-drilling of alumina has been investigated experimentally. Nd:YAG laser of wavelength of 1.06 μm is used to investigate process parameters such as lamp current, pulse frequency, air pressure and pulse width. Response surface methodology is adapted to perform experimental design and also to develop the mathematical relationships relating input and output process parameters. Since the responses are conflicting in nature, it is difficult to obtain a single combination of cutting parameters satisfying both the objectives in any one solution. Therefore, it is essential to explore the optimisation landscape to generate the set of dominant solutions. Further, this study adapted non-dominated sorting genetic algorithm II (NSGA II) to optimise the responses such that a set of mutually dominant solutions are found.

This paper presents a new single-parameter local search heuristic named Step Counting Hill Climbing algorithm (SCHC). It is a very simple technique, where the current cost serves as an acceptance bound for a number of consequent steps. This number is a sole algorithmic parameter, which should be set up by the user. Furthermore, the counting of steps can be organized in different ways, so the proposed method supposes a high number of variants and modifications. In this paper, we investigate three basic versions of SCHC with the university exam timetabling problem. Our experiments demonstrate that the proposed technique has the properties quite similar to the Late Acceptance Hill Climbing method, i.e. the convergence time is proportional to the value of its parameter and a non-linear rescaling of a problem does not affect the search performance. However, our new method has two additional advantages: a more flexible acceptance condition and better (on average) overall performance. In this study we present the comparison of these two search techniques, and compare both of them with Simulated Annealing. The Step Counting Hill Climbing has shown the strongest performance over three competitor algorithms on the most of our benchmark problems.

Superellipses can be used to represent in a compact form a large variety of shapes, and are useful for modelling in the fields of computer graphics and computer vision. However, fitting them to data is difficult and computationally expensive. Moreover, when only partial data is available the parameter estimates become unreliable. This paper attempts to improve the process of fitting to partial data by combining gradient and curvature information with the standard algebraic distance. Tests show that the addition of gradient information seems to enhance the robustness of fit and decrease the number of iterations needed. Curvature information appears to have only marginal effects.

• Whole-system optimisation model for wind-electricity-hydrogen networks • Determines the number, size and location of conversion, storage and transport technologies • Electricity versus hydrogen transmission • Determines hourly operation of the whole network over an entire year • Role and value of existing wind turbines, underground storage and hydrogen pipelines Abstract This paper presents the optimal design and operation of integrated wind-hydrogen-electricity networks using the general mixed integer linear programming energy network model, STeMES [1]. The network comprises: 10 wind turbines; electrolysers, fuel cells, compressors and expanders; pressurised vessels and underground storage for hydrogen storage; hydrogen pipelines and electricity overhead/underground transmission lines;