Pricing decision is the minority of all important decisions which can apparently influence a firm's profit-making within extremely short time. In an era of meagre profit, firms cannot stand any more injury caused of mistake at pricing. However, lots of managers still make pricing decision according to their experience or the action of other competitors without any mechanism of price-determining based on their firms' resource condition. The subject of this research is to probe the abiding price-reducing strategy for fashion appearing firms. Fashion apparel is a kind of commodities with seasonality and popularity, and is an example of all perishable goods. For all sorts of characteristic such as the need for long lead time before production, short time span for sale , and the low salvage value after season...etc., it makes firms reduce price to close out inventories by the end of seasons to evade value loss. When it comes to price-reducing, the fashion apparel is quite different from other commodities. It is a kind of commodity which has speciality of phased and monotonicity on price reduction. Therefore, it lacks two kinds of elasticity which are price-adjusting at any time and adjusting the price range at will. For the characteristic of close interdependence between product and time, and the normal demand on price-reducing, fashion apparel firms need some decision tools which are more fast, correct, and practical than any other ones. With two main parameters which are 'the levels of unsold inventory' and ' the length of season remaining ' along with two parameters which are 'discount factor' and ' the salvage value after season ', this research constructs out an stochastic dynamic programming model to maximize the expect profit and offer an program for calculating the optimal price-reduced range and time. After the analysis of generality and sensitivity with this model, it is found that the characteristics of this model are in conformity with theoretical research and real phenomenon of market. Besides, it is suitable for various kinds of price elastic demand. Hence, this model can be proved to be able to extend to other similar industries with the same nature.

The typical theoretical work on dividend policy suggests five possible imperfections that management should consider. They are taxes, asymmetric information, an incomplete contract, institutional constraints and transaction costs. Different from the typical framework, this dissertation is to study the optimal dividend poly with heterogeneous beliefs among investors. The first model in this study has analyzed investment/dividend policy with heterogeneous beliefs-the full information model in a frictionless economy with divergent types of shareholders. A high dividend policy is optimal with limited endowment for the optimistic investors as the stocks are sold not only to type-o investors, but also to at least one type-p investor holding some shares. A low dividend policy is appropriate with cash dividend D= X0-ao+1 is optimal as the shares are sold only to the type-o investors. Heterogeneous beliefs of investors change dividend policy given the same information even under full information. Following the Miller and Rock (1985) theory, the second model in this dissertation has analyzed heterogeneous beliefs among investors-the two period model in leading to changing a firm¡¦s optimal dividend policy. A firm¡¦s optimal dividend policy is changed not only by the ratio of the pessimistic to optimistic investors, but also heterogeneous beliefs. An increase in the ratio of pessimistic to optimistic investors will result in a higher dividend. On the other hand, as the beliefs of both optimistic and pessimistic investors increase, i.e. a new biotechnology is innovated, a relative low dividend policy is appropriate. Based on the previous analysis, the results show that optimal dividend policy with heterogeneous beliefs among investors in a firm¡¦s earnings exists under heterogeneous beliefs framework. A firm¡¦s optimal dividend policy is different from that of the MM dividend invariance theorem. It is not because of taxes, asymmetric information, incomplete contracts, institutional constraints and traction costs, but heterogeneous beliefs of investors.

An Integrated Chaos Search Genetic Algorithm (CGA) /Fuzzy System (FS), Tabu Search (TS) and Neural Fuzzy Network (NFN) method for load forecasting is presented in this paper. A Fuzzy Hyper-Rectangular Composite Neural Networks (FHRCNNs) was used for the initial load forecasting. Then we used CGAFS and TS to find the optimal solution of the parameters of the FHRCNNs, instead of Back-Propagation (BP). First the CGAFS generates a set of feasible solution parameters and then puts the solution into the TS. The CGAFS has good global optimal search capabilities, but poor local optimal search capabilities. The TS method on the other hand has good local optimal search capabilities. We combined both methods to try and obtain both advantages, and in doing so eliminate the drawback of the traditional ANN training by BP. This thesis presents a hybrid Chaos Search Immune Algorithm (IA)/Genetic Algorithm (GA) and Fuzzy System (FS) method (CIGAFS) for solving short-term thermal generating unit commitment problems (UC). The UC problem involves determining the start-up and shutdown schedules for generating units to meet the forecasted demand at the minimum cost. The commitment schedule must satisfy other constraints such as the generating limits per unit, reserve and individual units. We combined IA and GA, then added chaos search and fuzzy system approach in it. Then we used the hybrid system to solve UC. Numerical simulations were carried out using four cases; ten, twenty and thirty thermal units power systems over a 24-hour period.

This research uses the optimal control theory to construct two kinds of dynamic economic systems, which are an economic system without externalities and with externalities. Within each economic system both the centralized economy model and the decentralized economy model are included. The centralized economy (a social planner) model representes a kind of ideal economy, and the goal what the social planner pursues is that the resource allocation of the society satisfies the Pareto Efficiency criteria. On the other hand, the decentralized economy model (consists of a representative producer and a representative consumer) demonstrates the real economy, where economic agents persue their own best interests. While constructing the models, goods market equilibrium, labors market equilibrium, the dynamic accumulation process of capital, and the dynamic accumulation course of pollution are under consideration. Then, I apply optimal control method to get the first order conditions, and compare these f.o.c¡¦.s to verify whether they are unanimous. This paper proves that when externalities of pollution does not exist in the dynamic economic system, the decentralized economy model can achieve the Pareto Efficiency. On the contrary, when externalities of pollution emerges in the dynamic economic system, the decentralized economy model cannot reach Pareto Efficiency. If the externalities of pollution is internalized by the dynamic decentralized economic system economy, it will coincide with Pareto Efficiency. Besides, Pigouvian tax is still an effective policy instrument. Finally, I discuss all dynamic models in this paper to find out whether there exists a long-term and stable steady state. I find stable steady state, saddle-point equilibria, do exist under certain restrictions.

Wireless sensor networks provide an alternative way of improving our environments, such as environment surveillance, hazard monitoring, and other customized environment application, especially in military applications. Furthermore, the coverage issue in wireless sensor networks also plays an important role. Good coverage of a sensor network is an essential issue to ensure the quality of service. This paper studies the barrier coverage problems of a sensor networks, and will find the optimized straight-line path for both best-case and worst-case coverage problems. The optimal algorithm we proposed has a quadratic time complexity and is based on computational geometry. We proposed the distance function theory and applied it in our problems and we used the sweep and divide concept to solve the problems. Furthermore, the correctness of the proposed method is validated and simulated by experiments.

This paper uses the optimal control theory to construct dynamic economic model. The primary purpose of this paper is to discuss how different policies alter the choice problem of the firm and influence the allocation of funds to existing and new research and development activities. I analyze how the fixed-cost regulatory standard and the marginal-cost standard let firm consider externality, in order to protect the consumer of asymmetric information and avoid the problem of adverse selection. The firm maximizes expected profit. At the same time I want to know how the consumer acceptance, mark and audit affect the farmer to purchase the quantity of seed and the allocation of funds . We want to discuss how different price influence the option input path, the option quantity path and the option path . I discuss the different between ultimatum and static model. Finally, I discuss dynamic models in this paper to find out whether there exists a long-term and stable steady state. Saddle-point stability exists under certain restrictions.

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Consider the multiresponse polynomial regression model with one control variable and arbitrary covariance matrix among responses. The present results complement solutions by Krafft and Schaefer (1992) and Imhof (2000), who obtained the n-point D-optimal designs for the multiresponse regression model with one linear and one quadratic. We will show that the D-optimal design is invariant under linear transformation of the control variable. Moreover, the most cases of the exact D-optimal designs on [-1,1] for responses consisting of linear and quadratic polynomials only are derived. The efficiency of the exact D-optimal designs for the univariate quadratic model to that for the above model are also discussed. Some conjectures based on intensively numerical results are also included.

The D-optimal design problems in polynomial regression models with a one-dimensional control variable and k-dimensional response variable Y=(Y_1,...,Y_k) where there are some common unknown parameters are discussed. The approximate D-optimal designs are shown to be independent of the covariance structure between the k responses when the degrees of the k responses are of the same order. Then, the exact n-point D-optimal designs are also discussed. Krafft and Schaefer (1992) and Imhof (2000) are useful in obtaining our results. We extend the proof of symmetric cases for k>= 2.

With the rapid growth of load demand, the distribution system is becoming very complicated such that the operation efficiency and service quality are deteriorated during recent years. Engineers have to solve the problems by applying new technologies to enhance the efficiency of distribution system. In this thesis, an immune algorithm(IA) based on weighting selection as a decision maker is proposed to reach the desired switching operations such that transformer and feeder loading balance can be achieved. The IA antigen and antibody are equivalent to the objective and the feasible solution for a conventional optimization method. The concept of the information entropy is also introduced as a measure of diversity for the population to avoid falling into a local optimal solution. This algorithm prevents the possibility of stagnation in the iteration process and achieves the fast convergence for the global optimization. With the object-orient programming(OOP), this research project is to create the relationship of distribution element objects and encapsulation of data with all 22KV underground systems in Taichung district. The OOP does provide an effective tool for the management of distribution system database and the fault detection, isolation, and service restoration(FDIR) function of feeders and main transformers. According to the attributes of line switches, we can create the 22KV distribution system configuration with the topology processor. In order to calculate the current flows of line switches, this project will also execute the three phase load flow program with the customer information system(CIS), load survey, outage management information system(OMIS), and the data of all feeders and main transformers. In this thesis, the IA is used to solve the optimal switching problem by considering the customer load characteristics for the normal operation and the overload contingency of the distribution system. The efficiency of immune algorithm to solve the problem is verified by comparing to the computing time of the conventional binary integer programming for decision making of switching operation. A Taichung district distribution system is selected for computer simulation to demonstrate the effectiveness of the proposed methodology for solving the optimal switching operation of distribution system. The result of this thesis will be an important reference for distribution automation in Taiwan.