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This study is about contract evaluation and selection in an environment where there exist two or more parties, namely a producer and one or more outsourcing companies offering different contract options. The producer faces dynamic deterministic demand, which is known at the beginning of the planning horizon, and has to decide on the quantities of production, outsourcing, inventory carrying, and backorder. Among these decisions, the producer has the opportunity of subcontracting from a set of options offered by the subcontractors with possibly distinct contract terms. The contract options are in terms of length of the contract, fixed and variable costs associated with order placed, delivery lead-time and capacity guaranteed for use. A mathematical model is used for the evaluation of available options and for the selection of the ones in order to minimize cost incurred by the producer. The model provides desired quantities of production, inventory carrying and backorder, and also determines how different contract options will beused through the planning horizon. Extensive experimentation is performed using different factors affecting the optimal solution of the model in specific instances. These results are used in order to come up with a framework where various contracting schemes for subcontracting can be obtained. This framework can assist the producer in the decision of alternative courses of actions to be taken by him as a function of contract terms.

Thesis (Ph. D.)--Oregon State University, 1995. / Typescript (photocopy). Includes bibliographical references (leaves 133-137). Also available on the World Wide Web.

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With the growth of the distributed power generation market and the increasing integration of energy systems, more and more low carbon technologies are being installed at the domestic building level to optimise daily energy cost and reduce carbon emissions. The objective of this thesis is to optimise domestic building daily energy cost, and to identify ways of reducing the installation and maintenance costs of all domestic energy infrastructures. In this thesis, general energy conversion, storage and transmission in domestic buildings are considered. The first key part of this thesis is to size a combined heat and power (CHP) unit based on the Maximum Rectangle (MR) method and use the Genetic Algorithm (GA) method to optimize daily energy cost for a building without an energy storage system. The second key part of the thesis is to size a hybrid energy storage system (HESS) and develop a new rule-based energy control rule to optimise energy cost for a building with an energy storage system. The results show that after sizing the HESS, the daily benefit-cost ratio of the HESS is increased by approximately a factor of two over previous work. Additionally, the proposed rule based energy control model can yield up to 19.8% energy cost saving compared to a system dependent solely on electricity from the grid and using a boiler to generate heat. This ratio is almost equal to the previous work, but the present work increases customers’ comfort level by treating all load as critical. In addition, the optimization approach in this thesis is more real-world feasible, because it is not possible for exact loads to be known in advance. The results also show that daily energy cost saving for a building with HESSs and the appropriate control rule is approximately 47% higher than a building with a well-sized CHP but no HESS; and the capacity of CHP can also be reduced when the HESS is installed. Thus, the installation and maintenance costs of HESSs can be offset by reducing the capacity of CHP to some extent. In addition, the proposed control algorithm and HESSs have outstanding performance in improving the effective CHP output efficiency and average CHP input to output ratio. This proves the combination of HESS and the proposed rule-base control algorithm can reduce carbon emissions and make full use of CHP capacity. Page | iii However at present, the benefit to cost ratios of case studies of such domestic energy systems are always lower than 11% giving a negative return on investment. This figure is mainly limited by the high manufacturing price of HESSs and CHP. In the medium to long term future, the downward trend in battery and CHP manufacturing costs, coupled with changing energy tariffs are likely to lead to overwhelmingly positive cost benefit for this technology.