Division of Information Technology, Engineering and the Environment
School of Advanced Manufacturing and Mechanical Engineering
School of Advanced Manufacturing and Mechanical Engineering
Thesis Abstract
Tough competition in the current global economy has been forcing vendors to collaborate with their adjacent parties in managing their material flows to achieve a higher level of customer satisfaction. An integrated production and inventory policy between vendors and buyers would provide significant operational cost saving compared if they work as independent parties. Despite this potential saving, however, previous research in that area is limited to two-echelon and single-product problems. Therefore, this research aims to extend those earlier works by dealing with a multi-stage and multi-product system, comprising of multiple buyers, a single manufacturer and multiple suppliers. One major reason for this extension is that the considered structure occurs more in real industrial practice. This issue is addressed in a joint perspective with minimum costs as the performance indicator. A mathematical model representing the average total cost of the entire system has been successfully formulated and verified. The decision variables of the model include the number of deliveries for each buyer and supplier, a common production cycle and the manufacturer’s production sequence. The next steps of this research will deal with developing an efficient solution search using the Genetic Algorithm, measuring the performance of the model by evaluating the potential saving and lastly, assessing the performance of the developed algorithm. These processes will be performed through extensive numerical experimentations.
Keywords: inventory model, lot sizing, JELS, Genetic Algorithm, integrated vendor-buyer
