Abstract
This paper studies a multi-stage and parallel-machines scheduling problem which is similar to the traditional hybrid flow shop scheduling (HFS) in the solar cell industry. The multi-stage and parallel-machines scheduling problem in the solar cell industry simultaneously determines the optimal production sequence, multiprocessor task scheduling and machine configurations through dynamically allocating all jobs to multiple machines. We formulate this problem as a mixed integer linear programming model considering the practical characteristics and constraints. A hybrid-coded genetic algorithm is developed to find a near-optimal solution. Preliminary computational study indicates that the developed algorithm not only provides good quality solutions. © 2013 Springer-Verlag Berlin Heidelberg.