Optimal Fair-Workload Scheduling: A Case Study at Glorytek - Tzu-Chin Lin; Bertrand M. T. Lin (Mathematics, 2023)
Taichung is the center of the Taiwanese precision optical industry. Optics companies are modernized and automated, with most running 24 h production lines. With machines running around the clock, production lines must be assigned engineers to handle unexpected situations. The optical lens industry depends on precision technology. For fully automated production lines, each production process requires an engineer to be on call to troubleshoot production problems in real-time. However, shifts are currently scheduled manually, and the staff of each unit are responsible for scheduling the various production processes for each month. Administrative staff for each engineering department must take half a day to one day to complete the shift for a month, with results that usually do not ensure the best average workload, often leading engineers to question its fairness. Considering the manpower requirements for the actual production line shift and the fairness of balancing shifts, the scope of this study is the shift scheduling of engineering staff in the assembly line to perform different duties during a fixed cycle. The research aims to provide a solution for Glorytek to increase the efficiency of engineering shift scheduling and optimize the allocation of engineering staff. We will compare the duty allocation and efficiency of the current manual shift scheduling system with a new automated one. The results show that the efficiency of shift scheduling arrangements increased by more than 96%, and the maximum number of days of staff attendance (5 days) is less than that for manual assignment (6 days) while still satisfying the shift limits stipulated by the company. Two factors remain when implementing the proposed system. First, due to technical concerns, the internal process of the scheduling arrangement would be shifted from administrative staff to the IT department. Another concern is the inevitable investment in off-the-shelf optimization software.