Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.

This thesis proposes an algorithm to enhance the convergence speed of genetic algorithm by modifying the function flow of a simple GA. Additional operators, such as asexual reproduction, competition, and livability, are added before the survival operation. After adding these three operators to the genetic algorithm, the convergence speed can be increased. Experiments indicate that simulations with the proposed algorithm have a 47% improvement in convergence speed on the traveling salesman problem. As for the graph coloring problem, the proposed algorithm also has a 10% improvement. Also, since these operators are additional parts to the original GA, the algorithm can be further improved by enhancing the operators, such as selection, crossover, and mutation.

This research proposes a Genetic Algorithm based decision support model that provides decision makers with a quantitative basis for multi-criteria decision making related to construction scheduling. In an attempt to overcome the drawbacks of similar efforts, the proposed multi-objective optimization model provides insight into construction scheduling problems. In order to generate optimal solutions in terms of the three important criteria which are project duration, cost, and variation in resource use, a new data structure is proposed to define a solution to the problem and a general Niched Pareto Genetic Algorithm (NPGA) is modified to facilitate optimization procedure. The main features of the proposed Multi-Objective Genetic Algorithm (MOGA) are: A fitness sharing technique that maintains diversity of solutions. A non-dominated sorting method that assigns ranks to each individual solution in the population is beneficial to the tournament selection process. An external archive to prevent loss of optimal or near optimal solutions due to the random effect of genetic operators. A space normalization method to avoid scaling deficiencies. The developed optimization model was applied to two case studies. The results indicate that a wider range of solutions can be obtained by employing the new approach when compared to previous models. Greater area in the decision space is considered and tradeoffs between all the objectives are found. In addition, various resource use options are found and visualized. Most importantly, the creation of a simultaneous optimization model provides better insight into what is obtainable by each option. A limitation of this research is that schedules are created under the assumption of unlimited resource availability. Schedules created with this assumption in real world situations are often infeasible given that resources are commonly constrained and not readily available. As such, a discussion is provided regarding future research as to what data structure has to be developed in order to perform such scheduling under resource constraints.

The all-terrain vehicle (ATV) has been popular due to its simplicity in controlling, high recreational value, and sports utility, Furthermore, seating comfort and control aptness are affected mainly by the suspension and steering mechanism. Therefore, this research is directed towards the ATV¡¦s front suspension and steering mechanism, bearing in mind a set of systematized design procedures as the design and development basis of the front suspension and steering mechanism. First, we will investigate the papers on the front suspension and steering mechanism in order to make an induction on its characteristics and demand. Then, the theories of kinematic analysis of the front suspension and steering mechanism are established. The aided analyzing computer program we edit using the theories, moreover could be the design basis. And, to carry out the creative design of the front suspension and steering mechanism using the systematized procedure of creative mechanism design. In this investigation, we have successfully constructed atlas of the creative mechanism, and the result supplies the following design to select; Finally, we complete the kinematic design of the front suspension and steering mechanism based on the Genetic algorithm (GA), and we can obtain the better mechanism dimensions than Yamaha 660 ATV.

In recent years, microstrip antennas are suitable for applications in wireless communication systems because they have the characteristics of compact size, light weight, low cost and easy to manufacture. So, they play an important role in the navigation equipment of the rocket, space shutter, personal communication, etc. However, in the design and synthesis of antennas, there are a large number of design variables that affect the antenna performance. In early stages, some researchers did not use any optimization tool in parameter tuning of antennas design. The one utilized most is the ¡§trial and error¡¨ method, which is very time-consuming in order to find a suitable solution to verify the possibilities of the antenna structure. Genetic algorithms have been shown to be effective in the design of broadband microstrip antenna. However, their effectiveness with various degrees depends on the skills of the different genetic algorithms. In this dissertation, we propose a Genetic Algorithm (GA)-based refined method to enhance the effectiveness and to solve the gap-coupled microstrip antenna design problem (largest impedance bandwidth). The refined method with optimization process improves the computing performance comparing with the conventional genetic algorithm. By the refined GA method, bandwidth can be widened up to 3.84 times that of a single excited patch. Furthermore, we present a new design for Ultra Wideband (UWB) antenna. In the new research topic, it is expected that the genetic algorithm can find out a range of feasible (range-based) solutions instead of a few of solutions. As a result, the manufacturing process will have more convenience and practicability. Finally, we propose a new method to overcome the problem of signal interference with the UWB system operations. A band notched characteristic is achieved for the antenna to restrain the interference bandwidth. The disclosed antenna and the circuitry for the antenna system are easily integrated. With the simple structure, the fabrication cost for the antenna is also reduced.

As a result of ¡§the X-ray Phase Problem¡¨, traditional direct methods can¡¦t solve the structures of the large molecules. For exploring alternate methods, Wu-Pei Su applied simulated annealing to solve the structure of the large molecules and obtained success. Adopting his concept, we wrote a program for solving the structure of the molecules by C Program Language. And for decreasing the running time of the program, we introduced the concept of genetic algorithm into simulated annealing method.

Many methods can be used to predict the secondary structure of an RNA sequence. One of the methods is the dynamic programming approach. However, the dynamic programming approach takes too much time. Thus, it is not practical to solve the problem of long sequences with dynamic programming. RAGA (RNA Sequence Alignment by the Genetic Algorithm) is a genetic algorithm to align two similar sequences that the structure of one of them (master sequence) is known and another (slave sequence) is unknown. We can predict an RNA sequence by analyzing several homologous sequence alignment. In this thesis, we add an operator to mutate the residues of the base pairs in the master sequence and realign two sequences again. We compare our operator with other traditional operators, such as crossover and mutation. The experiment results show that our new operator gets a big improvement.

Natural gas plants can have multiple owners for raw natural gas streams and processing facilities as well as for multiple products. Therefore, a proper cost allocation method is necessary for taxation of the profits from natural gas and crude oil as well as for cost sharing among gas producers. However, cost allocation methods most often used in accounting, such as the sales value method and the physical units method, may produce unacceptable or even illogical results when applied to natural gas processes. Wright and Hall (1998) proposed a new approach called the design benefit method (DBM), based upon engineering principles, and Wright et al. (2001) illustrated the potential of the DBM for reliable cost allocation for natural gas processes by applying it to a natural gas process. In the present research, a rigorous modeling technique for the DBM has been developed based upon a Taylor series approximation. Also, we have investigated a cost allocation framework that determines the virtual flows, models the equipment, and evaluates cost allocation for applying the design benefit method to other scenarios, particularly those found in the petroleum and gas industries. By implementing these individual procedures on a computer, the proposed framework easily can be developed as a software package, and its application can be extended to large-scale processes. To implement the proposed cost allocation framework, we have investigated an optimization methodology specifically geared toward economic optimization problems encountered in natural gas plants. Optimization framework can provide co-producers who share raw natural gas streams and processing plants not only with optimal operating conditions but also with valuable information that can help evaluate their contracts. This information can be a reasonable source for deciding new contracts for co-producers. For the optimization framework, we have developed a genetic-quadratic search algorithm (GQSA) consisting of a general genetic algorithm and a quadratic search that is a suitable technique for solving optimization problems including process flowsheet optimization. The GQSA inherits the advantages of both genetic algorithms and quadratic search techniques, and it can find the global optimum with high probability for discontinuous as well as non-convex optimization problems much faster than general genetic algorithms.

In this thesis two case studies are performed about solving two design problems we face during the design phase of new Volvo truck. One is to solve the frame packing problem on CAN bus. The other is to solve the LDC allocation problem. Both solutions are targeted to meet as many end-to-end latency requirements as possible. Now the solution is obtained through manually approach and based on the designer experience. But it is still not satisfactory enough. With the development of artificial intelligence method we propose two methods based on genetic algorithm to solve our design problem we face today. In first case study about frame packing we perform one single genetic algorithm process to find the optimal solution. In second case study about LDC allocation we proposed how to handle two genetic algorithm processes together to reach the optimal solution. In this thesis we show the feasibility of adopting artificial intelligence concept in some activities of the truck design phases like we do in both case studies.

For many years research in artificial intelligence followed a symbolic paradigm which required a level of knowledge described in terms of rules. More recently subsymbolic approaches have been adopted as a suitable means for studying many problems. There are many search mechanisms which can be used to manipulate subsymbolic components, and in recent years general search methods based on models of natural evolution have become increasingly popular. This thesis examines a hybrid symbolic/subsymbolic approach and the application of evolutionary algorithms to a problem from each of the fields of shape representation (finding an iterated function system for an arbitrary shape), natural language dialogue (tuning parameters so that a particular behaviour can be achieved) and speech recognition (selecting the penalties used by a dynamic programming algorithm in creating a word lattice). These problems were selected on the basis that each should have a fundamentally different interactions at the subsymbolic level. Results demonstrate that for the experiments conducted the evolutionary algorithms performed well in most cases. However, the type of subsymbolic interaction that may occur influences the relative performance of evolutionary algorithms which emphasise either top-down (evolutionary programming - EP) or bottom-up (genetic algorithm - GA) means of solution discovery. For the shape representation problem EP is seen to perform significantly better than a GA, and reasons for this disparity are discussed. Furthermore, EP appears to offer a powerful means of finding solutions to this problem, and so the background and details of the problem are discussed at length. Some novel constraints on the problem's search space are also presented which could be used in related work. For the dialogue and speech recognition problems a GA and EP produce good results with EP performing slightly better. Results achieved with EP have been used to improve the performance of a speech recognition system.