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Thesis (Ph.D.)--Hong Kong University of Science and Technology, 2008. / Includes bibliographical references (leaves 94-100). Also available in electronic version.

Thesis (M. Eng.)--Carleton University, 1992. / Also available in electronic format on the Internet.

Thesis (Ph. D.)--City University of Hong Kong, 2005. / "Submitted to Department of Manufacturing Engineering and Engineering Management in partial fulfillment of the requirements for the degree of Doctor of Philosophy." Includes bibliographical references (leaves 164-175).

The main objective of the study was to ascertain the presence of the identified critical success factors for lean sustainability in a chemical manufacturing company in Port Elizabeth. The critical success factors that formed part of the research included leadership involvement and commitment, management involvement and commitment, employee engagement and organisational culture. The methodology used was one of a positivistic approach. A questionnaire was utilized and the responses were analyzed using various statistical methods. Based on the results from the analysis, recommendations and conclusions could be drawn. The inferential results of the study indicated that all the critical success factors identified for this study were present within the organisation. However, there were still a large percentage of respondents that remained neutral across all the questions which could indicate reasons why previous attempts in lean implementation failed. The recommendations provided were based on the findings of the study. An implementation strategy was identified and outlined. This strategy and recommendations will assist in providing a sound platform for a sustainable lean initiative within the organisation.

Production planning is a critical component in supply chain management. The goal of production planning is to meet market demand while minimizing operational costs. There is inherent uncertainty in manufacturing systems due to unscheduled shutdowns and variable production rates. Additionally, actual demand levels cannot be predicted accurately. As a result, there is value in creating a production plan that considers these uncertainties. Scheduling is also a critical component in supply chain management, but at a smaller level of time granularity. Industrially sized scheduling problems are often on such a large scale that the problem is computationally difficult to solve. Consequently, there is value in creating a mathematical model and selecting a solution algorithm that minimizes this burden. This work aims to determine the benefit of a stochastic production planning model over its deterministic counterpart. The problem utilizes a multi-period, multi-product aggregated planning model with a finite horizon in a steel manufacturing environment. The production and demand uncertainty is modelled as a two-stage stochastic mixed integer linear program. The problem utilizes a Monte Carlo simulation technique to create the scenarios used in the optimization. The objective of the optimization is to determine the production volume and inventory levels for each discrete time interval while minimizing the weighted cost of production and surplus. The production decisions must be non-anticipative, immediately implementable, and are subjected to production capacity and inventory holding constraints. This work also investigates the advantages a cost-based model has over its goal-programming counterpart. Finally, this thesis develops several mathematical batch scheduling models that use different modelling paradigms in an effort to compare their computational complexity. With the selection of an appropriate model, model extensions are added to replicate an industrially relevant steel mill scheduling problem for a finishing line using data from a facility located in Ontario, Canada. / Thesis / Master of Applied Science (MASc)

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Aggregate production planning models are of the greatest importance to operations management, since these plans enable management to utilize the major resources at its command. In this report the structure of the aggregate planning problem and a number of different approaches are reviewed and presented. Approaches are classified in three categories: a) workforce smoothing models, b) production smoothing models, c) production and workforce models. The models are compared with respect to the cost structure, parameters estimation, forecast requirement, decision variables, computability and optimization techniques.