Abstract:
With the industry 4.0 idea that emerged recently, manufacturing systems have been enhanced by advanced manufacturing technologies. These smart manufacturing systems can respond faster to the changes in the production plans which are mostly due to the updates in customer order quantities, order due dates, unexpected machine breakdowns, material supply problems, etc. This study aims to support the dynamic structure of smart factories by providing an efficient, effective and flexible platform for production planning and scheduling. In most of the studies in the literature, production control and scheduling plans have been handled separately and iteratively due their computational complexities. As an improvement, in this thesis a mixed integer quadratic programming (MIQP) model is developed to optimize the integrated production plans and daily schedules with minimum total cost. Then, this optimization model is linearized and a mixed integer linear programming (MILP) model is generated to improve its computational performance. Finally, the optimization model is embedded into a web based decision support system (DSS) together with a database and user interface to provide an efficient, effective and flexible decision making environment in a smarter cyber-physical system. The DSS is verified with different test scenarios and its computational performance is measured.
