Optimization of a mixed-model assembly sequencing

In manufacturing assembly facilities, the lack of efficient and effective shop floor communication creates an environment where logistics and quality problems occur daily and take longer than necessary to resolve. Although Enterprise Resource Planning (ERP) systems have been developed and implemented to prevent these types of problems, they are not able to react quickly to unforeseen events such as part shortages. This work focuses on creating a mixed-model assembly sequencing optimization formulation that can be solved quickly to minimize the total earliness and lateness when delivering a finished product to downstream operations or customers. Previous research on mixed-model assembly sequencing has not addressed unknown part availability. Research methods include formulating a mixed-integer program and implementing it in MATLAB, constructing test cases that are consistent with data collected from industry, and assessing the quality of the sequencing decisions. If the execution of the model is successful, the results will help production schedulers immediately react to part quality shortages and other issues that arise on the manufacturing floor. This would grant them the ability to resolve these issues without stopping the assembly lines. Future research aims to incorporate this model into online dashboards to grant easy accessibility and efficient use