The author develops a mathematically rigorous and concise formulation of a general, multistage input-output model for complex production systems, by integrating various useful concepts, developed in different disciplines. This model was designed to serve equally well for production planning, cost measurement and allocation purposes. After the objective, scope and methodology are set forth, the concepts of linking the stages of a production system and reducing the multistage model into different equivalent formats are demonstrated. Then the concepts of jumping inputs, internal flows and feedbacks are introduced into the model, to account for characteristics of more complex production systems. All previously discussed concepts are then integrated into a single, generalized input-output model. It is shown, how the physical model may be used to calculate the total costs of alternative production programs, how standard costs can be derived and how dollar-coefficient matrices may be used. Some of the existing literature is then integrated and evaluated within this new framework. Finally a summary is complemented by a list of areas that seem to deserve further research. / Business, Sauder School of / Graduate

Today's highly competitive marketplaces require production systems that are flexible and responsive to changing demands. To remain competitive, companies need close coordination and exchange of computer interpretable information between product design and the manufacturing system. Computer-Aided Process Planning (CAPP) is an essential key for achieving closer links among design and manufacturing activities. The purpose of process planning is to generate feasible sequences for producing a part in a given production facility. To generate process plans automatically (true generative CAPP), design information along with production facility information needs to be appropriately represented. Most CAPP systems assume feasible designs as input and lack the capability to evaluate designs for manufacturability with respect to the production facility. The objective of this research is to develop a true generative CAPP system that is an integral part of a design for manufacturability (DFM) application for machined components. It involves determining appropriate representation schemes of machined components and production facility resources. The created CAPP Module, developed using C++, consists of five process dependent modules for automatic process plan generation and evaluation: (1) Process selection, (2) Machine/Tool Selection, (3) Setup/Fixture Planning, (4) Operation Sequence Planning, and (5) Process Plan Evaluation. Process plan generation is performed by the first four modules. Evaluation of process plans is performed by the Process Plan Evaluation Module. Criteria such as cost, resource utilization, and production requirement, are used to generate the most appropriate process plan and to select additional process plans as needed. / Master of Science

JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. The heart of the operation is the processing facilities where freshly caught Pacific salmon are prepared for sale to end consumers and institutional buyers. As the operations of JSM evolved, the decision making for allocating a catch of salmon with varying characteristics amongst a set of final products has become too complex and time consuming. The focus of this study is to determine an effective and efficient method for JSM to allocate daily a fresh salmon harvest between the various products they produce on a daily basis. The goal is short-term production planning, to allocate the catch among the products in such a manner that the profit potential of the catch is maximized, i.e. prepare a production schedule that maximizes the total profit over the planning horizon. Additional goals of this project include: automation of the decision making process for the catch allocation, "what if" planning, decreasing expert dependency, reducing decision making time, and building a practical and innovative decision support tool. In order to solve this problem efficiently and effectively, optimization models were developed for allocating the catch to the end products. A corresponding decision support tool was built for the end-users at JSM.

JS McMillan Fisheries Ltd. (JSM) is a Vancouver-based company with operations in nearly all levels of the commercial fishing industry, from supply through distribution. The heart of the operation is the processing facilities where freshly caught Pacific salmon are prepared for sale to end consumers and institutional buyers. As the operations of JSM evolved, the decision making for allocating a catch of salmon with varying characteristics amongst a set of final products has become too complex and time consuming. The focus of this study is to determine an effective and efficient method for JSM to allocate daily a fresh salmon harvest between the various products they produce on a daily basis. The goal is short-term production planning, to allocate the catch among the products in such a manner that the profit potential of the catch is maximized, i.e. prepare a production schedule that maximizes the total profit over the planning horizon. Additional goals of this project include: automation of the decision making process for the catch allocation, "what if" planning, decreasing expert dependency, reducing decision making time, and building a practical and innovative decision support tool. In order to solve this problem efficiently and effectively, optimization models were developed for allocating the catch to the end products. A corresponding decision support tool was built for the end-users at JSM. / Business, Sauder School of / Graduate