(145c) Material Handling and Production Line Debottleneck Optimization

Multi-stage material handling (MSMH) processes are broadly used in industries for manufacturing massive amount of products/workpieces (jobs) through multiple processing steps, e.g., an electroplating system.  A hoist is a controlled robot mainly conducting job lifting, releasing, and moving along its production line by following a preset movement schedule based on the job processing recipe.  Nowadays, one of the big challenges for MSMH process is simultaneously manufacturing multiple types of jobs with different processing recipes from a single production line; meanwhile, some processing stages should have the multi-job processing capacities.  These stringent requirements make optimal hoist scheduling become very complicated but also very important.  In this paper, we consider such a situation that an MSMH production line has an optimal hoist schedule but the scheduling performance already meets an inherent bottleneck due to the lack of processing stages/units.  Under this situation, additional parallel processing stages/units have to be added to boost the productivity associated with the development of a new hoist scheduling strategy, i.e., debottleneck the performance of the existing production line. 

Unlike previous hoist scheduling cases, this material handling and production line debottleneck problem requires both decision making of the debottleneck scheme and hoist scheduling identification, which has never been discussed before.  In this paper, an MILP (Mixed Integer Linear Programming) based modeling methodology is developed to optimize the debottleneck problem, including identifying which unit/units should be increased by their processing capacity and what is the optimal  hoist schedule under the newly designed MSMH process, so that an investment will receive the best economic return.  The efficacy of the proposed methodology is demonstrated by successfully tackling a cyclic hoist scheduling problem, where 8 units with various capacities are employed in the production line to continuously produce three different types of jobs.  The scheduling result can be guaranteed as the global optimal solution as well.