The planning and control of manufacturing systems is a complex activity involving a myriad of decisions and optimization algorithms. ln a Computer Integrated Manufacturing System (CIMS) these decisions and algorithms span several functions, require information from several sources, and have consequences in several sectors of the manufacturing system. This dissertation is concerned with the development of a structured methodology for executing this activity. Therefore, the primary activities are first, to develop the framework for a Computer Integrated Production Planning and Control (ClPP&C) system, and second, to formulate and solve specific mathematical models which are nested in the developed framework. The framework functions as a "city plan" for the production control activity in a CIMS environment, while the mathematical methodologies are pieces of the decision architecture.

Achieving a CIMS implies achieving an integrated manufacturing system. Implying the production control system needs to be designed with specific consideration of the concepts, issues, and principles of integrated manufacturing. As such, these concepts, issues and principles are identified and developed in this research. A model of CIMS is developed and the role of ClPP&C is analyzed. A framework for integration in manufacturing is developed and used to guide the modeling efforts. . The ClPP&C "city plan" is developed using an adaptation of the IDEF methodology. The objective of the plan is to define the separate problems which are to be solved in production control, the interrelationships between these problems, and the synergy which causes them to behave as a single system.

This research specifically addresses the master aggregate scheduling (MA-Schedule) and the coordinating production scheduling (CP-Schedule) problems within the CIPP&C plan. The MA-Scheduling problem prescribes how much of a family is to be produced in a time period, and is formulated in detail as a non-linear 0-1 mixed integer program. The formulation aggregates capacity, time, and products; models routing and capacity flexibility; and considers the availability of material transporters. The solution procedure incorporates linearization methods, preprocessing algorithms, and large-scale MIP solvers. The CP-Schedule is formulated as two separate problems. The first disaggregates time and product and is to be solved as a MIP. The second problem determines the start time of each product batch at a cell. lt is equivalent to the minimum makespan problem and solution approaches are discussed.

A network of programs was designed to execute the scheduling methodology. Experimental results with the methodology are reported. These results provide insights into system performance in various conditions. Specifically, the impact of flexibility, loading, transporter availability, and cost dimensions are analyzed.