Variable Neighborhood Search for Drilling Operation Scheduling in PCB Industries

Abstract

Among all types of production environment, identical parallel machines are frequently used to increase the manufacturing capacity of the drilling operation in Taiwan printed circuit board (PCB) industries. So when a manager plans the production scheduling, multiple but conflicting objectives are often considered. Unlike the single objective problem, the multiple-objective version no longer looks for an individual optimal solution, but a Pareto front consisting of a set of non-dominated solutions. The manager then can select one of the alternatives from the set. For this matter, our research aims at applying a variable neighborhood search (VNS) algorithm in the identical parallel machine scheduling problem (IPMSP) with two conflicting objectives: makespan and total tardiness. In VNS, two neighborhoods are defined – insert a job to a different position or swap two jobs in the sequence. To save the computational expense, one of the neighborhoods is randomly selected for the target solution which is also arbitrarily chosen from the current Pareto front. The proposed VNS algorithm is tested on a set of real data collected from a leading PCB factory in Taiwan and its performance is compared with well-known methods in the literature. The computational results show that VNS outperforms all competing algorithms – SPGA, MOGA, NSGA-II, SPEA-II, and MACO in terms of solution quality and computational time.