Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2004. / Includes bibliographical references (leaves 173-179). Also available in electronic version. Access restricted to campus users.

Thesis (M.S.)--Brigham Young University. Dept. of Geography, 2007. / Includes bibliographical references (p. 79-87).

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2009. / Includes bibliographical references.

Thesis (doctoral)--Technische Universität Müunchen, 2004. / Includes bibliographical references (p. 135-155).

Thesis (M.S.)--State University of New York at Binghamton, Thomas J. Watson School of Engineering and Applied Science, Dept. of Systems Science and Industrial Engineering, 2005. / Includes bibliographical references.

Thesis (Ph.D.)--McMaster University, 2004. / Advisor: George Steiner. Includes bibliographical references (leaves 87-91). Also available via World Wide Web.

by Gabriel R. Bitran, David Marieni, Jim Noonan. / "October 1983." / Bibliography: leaf 27.

Charles H. Fine, Arnoldo C. Hax. / "September 1984." / Bibliography: p. 34-35.

Thesis (MScEng (Industrial Engineering))--University of Stellenbosch, 2009. / Scheduling is a core activity in the manufacturing business. It assists with efficient and effective utilization of capital-intensive resources and increased throughput, thus increasing profitability. The focus in this thesis is on scheduling of manufacturing orders in a make-to-order job-shop enterprise. It is widely accepted that manufacturing of large volumes and production with as few as possible product variants is the most cost-effective business approach, but the need for low volume, once-off engineering parts will always exist. Many approaches to scheduling exist, including translation of a scheduling problem to a Travelling Salesman analogue, while Discrete-event computer simulation is well established as a means to assist with scheduling. Simulation is appealing in the manufacturing environment, as it can realistically imitate dynamic, stochastic processes while being descriptive in forecasting the future. In this thesis, the development and testing of a simulation-based scheduler is described. The scheduler was developed for, and in collaboration with a South African make-to-order job-shop enterprise. A supporting information system was also developed and it is required that the enterprise changes some of its business processes if this scheduler is implemented. The scheduler considers the status of the enterprise each time a new order is received, and the current schedule is reviewed and may be revised at such a point in time, making it a real-time scheduler. Several classic scheduling dispatching rules and –measures were incorporated in the scheduler. These include First-in First-out, Earliest Due Date, Longest Processing Time, Shortest Processing Time, Smallest Slack and Critical Ratio (dispatching rules), while the performance measures are Makespan, Earliness, Lateness, Average Flow Time and Machine Usage. The proposed scheduler has been verified and validated using test data and designed confidence building tests, and its performance was also compared to an actual, historical schedule. The functioning of the scheduler is finally demonstrated using a stochastic test environment. The scheduler has generally performed satisfactorily and should be implemented as the final phase of this project.

The thesis is presented in three papers format. Paper 1 presents the first bibliometric literature survey of its kind on aggregate production planning (APP) in presence of uncertainty. It surveys a wide range of the literatures which employ operations research/management science methodologies to deal with APP in presence of uncertainty by classifying them into six main categories such as stochastic mathematical programming, fuzzy mathematical programming and simulation. After a preliminary literature analysis, e.g. with regard to number of publications by journal and publication frequency by country, the literature about each of these categories is shortly reviewed. Then, a more detailed statistical analysis of the surveyed research, with respect to the source of uncertainty, number of publications trend over time, adopted APP strategies, applied management science methodologies and their sub-categories, and so on, is presented. Finally, possible future research paths are discussed on the basis of identified research trends and research gaps. The second paper proposes a novel decision model to APP decision making problem based on mixed chase and level strategy under uncertainty where the market demand acts as the main source of uncertainty. By taking into account the novel features, the constructed model turns out to be stochastic, nonlinear, multi-stage and multi-objective. APP in practice entails multiple-objectivity. Therefore, the model involves multiple objectives such as total revenue, total production costs, total labour productivity costs, optimum utilisation of production resources and capacity and customer satisfaction, and is validated on the basis of real world data from beverage manufacturing industry. Applying the recourse approach in stochastic programming leads to empty feasible space, and therefore the wait and see approach is used instead. After solving the model using the real-world industrial data, sensitivity analysis and several forms of trade-off analysis are conducted by changing different parameters/coefficients of the constructed model, and by analysing the compromise between objectives respectively. Finally, possible future research directions, with regard to the limitations of present study, are discussed. The third paper is to appraise the performance of different APP strategies in presence of uncertainty. The relevant models for various APP strategies including the pure chase, the pure level, the modified chase and the modified level strategies are derived from the fundamental model developed for the mixed chase and level strategy in paper 2. The same procedure, which is used in paper 2, follows to solve the models constructed for these strategies with respect to the aforementioned objectives/criteria in order to provide business and managerial insights to operations managers about the effectiveness and practicality of these APP policies under uncertainty. Multiple criteria decision making (MCDM) methods such as additive value function (AVF), the technique for order of preference by similarity to ideal solution (TOPSIS) and VIKOR are also used besides multi-objective optimisation to assess the overall performance of each APP strategy.