A Heuristic for Inserting Randomly Arriving Jobs Into an Existing Hoist Schedule

Abstract

Hoist scheduling in automated electroplating lines has been extensively studied in a static environment. However, practical electroplating lines are subject to diversified unforeseen disruptions that require frequent rescheduling to maintain or optimize system performance. This paper addresses a hoist scheduling problem, where randomly arriving jobs need to be inserted into an existing schedule without changing the sequence of hoist moves already scheduled. The objective is to minimize the total completion time of all the jobs in the existing schedule and a newly inserted job. We develop a polynomial-time heuristic that adjusts the starting times of the existing hoist moves to a limited extent but does not bring about a severe disturbance of the existing hoist moves. We compare our algorithm with two existing approaches with different rescheduling policies (i.e., partial and zero adjustment of the existing schedule). We empirically analyze the productivity and the stability of the schedules generated by the three approaches. Computational results demonstrate that our algorithm can generate more productive and stable schedules than the two existing approaches. Note to Practitioners —Electroplating and chemical surface treatment lines with automated material handling hoists are commonplace in electronics, semiconductor, and many other manufacturing industries. In an uncertain environment, hoist rescheduling plays an important role in improving the productivity and reducing the impact of disruptions. This paper presents a hoist scheduling algorithm to deal with dynamic job arrivals by considering the impact of the disturbance incurred by rescheduling. Our algorithm can generate a better schedule with smaller total completion time and slighter disturbance than the existing algorithms. The proposed algorithm runs in polynomial time and can be used to control hoist operations in practical electroplating lines. A comparative analysis provides useful insights on the implementation of rescheduling approaches and policies to industry practitioners.