Title from PDF of title page. / Document formatted into pages; contains 80 pages. / Thesis (M.S.I.E.)--University of South Florida, 2003. / Includes bibliographical references. / Text (Electronic thesis) in PDF format. / ABSTRACT: This study focuses on the study of a unique but commonly occurring manufacturing problem of scheduling of customized jobs consisting of two operations on a single multi-purpose machine with the performance objective of minimizing the number of tardy jobs (jobs that are not completed by their due dates). Each customized job to be complete needs one unique operation and one common operation performed on it. We considered a static case in this work. The objective of minimizing the number of tardy jobs is considered where all jobs have equal weights and the maximum tardiness has no effect on the performance. This problem is proved in literature as NP-hard and hence practically very difficult to obtain optimal solution within reasonable computational time. Till date only a pseudo-polynomial algorithm is given to solve this problem with no concrete computational experiments designed to prove the efficiency and working of the algorithm for different problem instances. / ABSTRACT: We propose a heuristic algorithm based on the Moore-Hodgson's algorithm combining with other procedures and optimal schedule properties from the literature to solve this problem. In literature, Moore-Hodgson's algorithm is an efficient heuristic algorithm that minimizes the number of tardy jobs for the classical single machine one-operation problems. The performance of the heuristic is evaluated through extensive computational experiments for large real size data. The obtained results are compared to the solutions obtained by implementing the optimal pseudo-polynomial algorithm and the performance of the heuristic is tested on large data sets. The test data for the computational experiments are generated randomly using MATLAB 6.1. Future directions of research and development on the problem to improve the obtained solution by the heuristic algorithm are given. / System requirements: World Wide Web browser and PDF reader. / Mode of access: World Wide Web.

This study focuses on the study of a unique but commonly occurring manufacturing problem of scheduling of customized jobs consisting of two operations on a single multi-purpose machine with the performance objective of minimizing the number of tardy jobs (jobs that are not completed by their due dates). Each customized job to be complete needs one unique operation and one common operation performed on it. We considered a static case in this work. The objective of minimizing the number of tardy jobs is considered where all jobs have equal weights and the maximum tardiness has no effect on the performance. This problem is proved in literature as NP-hard and hence practically very difficult to obtain optimal solution within reasonable computational time. Till date only a pseudo-polynomial algorithm is given to solve this problem with no concrete computational experiments designed to prove the efficiency and working of the algorithm for different problem instances. We propose a heuristic algorithm based on the Moore-Hodgson's algorithm combining with other procedures and optimal schedule properties from the literature to solve this problem. In literature, Moore-Hodgson's algorithm is an efficient heuristic algorithm that minimizes the number of tardy jobs for the classical single machine one-operation problems. The performance of the heuristic is evaluated through extensive computational experiments for large real size data. The obtained results are compared to the solutions obtained by implementing the optimal pseudo-polynomial algorithm and the performance of the heuristic is tested on large data sets. The test data for the computational experiments are generated randomly using MATLAB 6.1. Future directions of research and development on the problem to improve the obtained solution by the heuristic algorithm are given.

This thesis discusses the operational coordination of electrical appliances and devices in a smart home. At present, the diminishing volume of fossil fuels and the increasing pressure to use renewable sources of energy necessitate the integration of such volatile sources into electrical grids. This process, however, results in higher energy costs, and the consumers are thus more willing to change their behaviour to either reduce the expenses or maintain them at a reasonable level. One of the relatively few customer-oriented options to optimise energy costs consists in the demand – response principle, which utilises external information to minimise energy consumption during high price periods. Assuming the constantly changing conditions in electrical grids, and thus also the varying demands, it is vital to provide for automatic optimisation excluding the need of user intervention. The thesis presents a method which, after being implemented into the control member, will facilitate the optimal use of appliances and devices within a smart home. As the behaviour considered optimal from the perspective of demand - response is often inconsistent with the consumer‘s requirements for comfortable use of the appliances, the proposed technique offers a compromise through enabling the consumer to select the appropriate strategy. Five universal optimisation models are designed within the thesis; these models facilitate description of common home appliances and local electricity sources. The core of the method lies in formulating and optimising a mixed integer quadratic problem (MIQP). The optimisation task yields an operational schedule for the individual appliances, and this scheme considers the energy costs, the working cycle of the appliance, the user’s demands, the system restrictions and/or other input data. Furthermore, the author extends the above-discussed general technique, enabling it to adopt robust behaviour. The method then secures the preset strategy even during a marked change of the input conditions, and its robustness is a viable precondition for the overall applicability of the technique in the real control member.

This thesis introduces a new model formulation to solve routing and scheduling problems, with the main applications in answering routing and scheduling problems faced by a sea-cargo shipping company and a railroad company. For the work in sea-cargo routing and scheduling, we focus on the tramp shipping operation. Tramp shipping is a demand-driven type of shipping operation which does not have fixed schedules. The schedules are based on the pickup and download locations of profitable service requests. Given set of products distributed among a set of ports, with each product having pickup and download time windows and a destination port, the problem is to find the schedule for a fleet of ships that maximizes profit over a specified time horizon. The problem is modeled as a Mixed Integer Non-Linear Program and reformulated as an equivalent Mixed Integer Linear Program. Three heuristic methods, along with computational results, are presented. We also exploit the special structure enjoyed by our model and introduce an upper-bounding problem to the model. With a little modification, the model is readily extendable to reflect soft time windows and inter-ship cargo-transfers. The other part of our work deals with train routing and scheduling. A typical train shipment consists of a set of cars having a common origin and destination. To reduce the handling of individual shipments as they travel, shipments are grouped into blocks. The problem is that given sets of blocks to be carried from origins to destinations, construct the most cost effective train routes and schedules and determine block-to-train assignments, such that the number of block transfers (block swaps) between trains, the number of trains used, and some other cost measures are minimized. Incorporating additional precedence requirements, the modeling techniques from the shipping research are employed to formulate a mixed integer nonlinear program for this train routing and scheduling problem. Computational results are presented.