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Thesis. 1979. Ph.D.--Massachusetts Institute of Technology. Alfred P. Sloan School of Management. / MICROFICHE COPY AVAILABLE IN ARCHIVES AND DEWEY. / Bibliography: leaves 152-154. / Ph.D.

Timely provision of quality products at the lowest prices possible has become the utmost competitive edge being pursued by virtually all manufacturing firms. They endeavour to speed up their production and deliveries of goods to end customers in order to make more money and even survive in the fierce competition arena. Although much progress has been made in operations management and a series of production planning approaches have been proposed to achieve various manufacturing operations goals, optimisation results are often rendered unrealistic and even misleading, for few studies have considered the overall corporate goal of shareholder wealth maximisation and the specific economic environments where manufacturing firms operate. Some critical factors closely related to interests of corporate owners, such as working capital management and capital structure, are rarely involved in an overwhelming majority of production planning problems. Moreover, the overlook of the effects of production planning results on the environment makes them more impractical and even unavailable in real-world manufacturing environments. To this end, the dissertation proposes a stochastic production planning model for the uncertain make-to-order production environment, with the focus mainly on the lot sizing decision-making policy. The primary goal of the optimization problem is to maximise the sustainable full interests of corporate owners, namely, the shareholder wealth, rather than to optimise some traditional local or short-term objective functions, such as work flow times, accounting costs, accounting profits and the like. To improve the generality and exactness of the proposed model, all involved uncertain random events are characterized by their own inherent statistical merits without any impractical assumptions on their distributions. The improvement of production planning is not the only one single source of the wealth-based business performance. There are also some other critical factors which can impose direct influences on shareholder wealth. Among these potential shareholder wealth drivers, we choose to examine the effective management of working capital and capital structure, for they are closely pertinent to a firm’s financial position and its cash flow status. In addition, environmental protection has in recent decades aroused extensively global attention because of its far-reaching impingements on the social and economic developments of the world. The carbon emission in production, especially its main component—carbon dioxide, is generally recognized as the most important emission source. To mitigate their diverse interference with the climate and the environment, a wide range of emission reduction measures, laws, and legislations has been enacted and implemented, making production planning optimisations more complicated. To better reflect the emerging production planning environment facing manufacturing firms, the emission trading system for carbon management, which has thus far become the most popular market-based carbon reduction mechanism, is incorporated into the proposed production planning model. To theoretically and analytically validate the proposed approach, the probability and convex theories are adopted to prove the convexity or concavity of the optimisation objectives and the relevant global optimal characteristics. Numerical experiments are further conducted to demonstrate the important implications of the proposed optimisation model to production planning in industrial practices. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Doctoral / Doctor of Philosophy

The Production Switching Heuristic (PSH) developed by Mellichamp and Love (1978) has been suggested as a more realistic, practical and intuitively appealing approach to aggregate production planning (APP). In this research, PSH has been modified to present a more sophisticated open grid search procedure for solving the APP problem. The effectiveness of this approach has been demonstrated by determining a better near-optimal solution to the classic paint factory problem using a personal computer based application written in THINK PASCAL. The performance of the modified production switching heuristic is then compared in the context of the paint factory problem with results obtained by other prominent APP models including LDR, PPP, and PSH to conclude that the modified PSH offers a better minimum cost solution than the original PSH model. / Graduation date: 1992

In recent years, lots of research effort was spent on the integration of process planning and job-shop scheduling. Various integrated process planning and scheduling (IPPS) models and solution approaches have been proposed. The previous and existing research approaches are able to demonstrate the feasibility of implementing IPPS. However, most of them assumed that setup time is negligible or only part of the processing time. For machined parts, the setup for each operation includes workpiece loading and unloading, tool change, etc. For setup that depends only on the operation to be processed (sequence-independent), it is applicable to adopt the assumption of not considering setup in IPPS. For setup that depends on both the operation to be processed and the immediately preceding operation (sequence-dependent), it is an oversimplification to adopt such assumption. In such cases, the setup time varies with the sequence of the operations. The process plans and schedules constructed under such assumption are not realistic or not even feasible. In actual practice, therefore, the setup time should be separated from the process time in performing the IPPS functions. In this thesis, a new approach is proposed for IPPS problems with setup time consideration for machined parts. Inseparable and sequence-dependent setup requirements are added into the IPPS problems. The setup times are separated from the process times and they vary with the sequence of the operations. IPPS is regarded as NP-hard problem. With the separated consideration of setup times, it becomes even more complicated. An Ant Colony Optimization (ACO) approach is proposed to handle this complicated problem. The system is constructed under a multi-agent system (MAS). AND/OR graph is used to record the set of feasible production procedures and sequences. The ACO algorithm computes results by an autocatalytic process with the objective to minimize the makespan. Software agents called “artificial ants” traverse through the feasible routes in the graph and finally construct a schedule. A setup time parameter is added into the algorithm to influence the ants to select the process with less setup time. The approach is able to construct a feasible solution with less setup time. Experimental studies have been performed to evaluate the performance of MAS-ACO approach in solving IPPS problems with separated consideration of setup times. The experimental results show that the MAS-ACO approach can effectively handle the problem. / published_or_final_version / Industrial and Manufacturing Systems Engineering / Master / Master of Philosophy

published_or_final_version / Mathematics / Master / Master of Philosophy

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Firms in several discrete parts manufacturing industries, e.g., electronics equipment, computers, telecommunications equipment etc. operate in a multi-plant environment where products are processed successively at several plants. Prior studies have ignored the interaction between different plants in a multi-plant scenario. The objective of this dissertation is to study the impact of coordination on the cost performance of a two-plant firm. / We propose a model that jointly determines production and inventory decisions so that the total cost of holding inventory and overtime, at the two plants is minimized. Our model captures the interaction between the two plants and is preferable to the uncoordinated or the sequential approach which ignores this interaction. We consider the case with limited capacity and explicitly model setup times. Strategies based on Lagrangian relaxation and Lagrangian decomposition methodologies are proposed to solve the model. / Two main findings emerge from this research. First, our results indicate that coordination could lead to improved cost performance and enhanced profits for firms. Two parameters, the setup time to processing time ratio and the capacity utilization at the two plants played a significant role in determining the cost improvements. Managerial implications relating to implementation of the coordinated model are discussed. The second important finding of this research is that Lagrangian decomposition consistently outperforms Lagrangian relaxation in terms of achieving better deviation from the optimal solution, for this problem. A Linear Programming based technique for further enhancing the convergence between the upper and lower bounds is presented. / In the quest for improved performance, multi-plant coordination represents an important strategy for firms. The contribution of the current research is in modelling some of the salient issues of this problem and exploring promising methodological directions.

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