Job shop scheduling problem with alternative machines using genetic algorithms

Abstract

The classical job shop scheduling problem (JSP) is the most popular machine scheduling model in practice and is known as NP-hard. The formulation of the JSP is based on the assumption that for each part type or job there is only one process plan that prescribes the sequence of operations and the machine on which each operation has to be performed. However, JSP with alternative machines for various operations is an extension of the classical JSP, which allows an operation to be processed by any machine from a given set of machines. Since this problem requires an additional decision of machine allocation during scheduling, it is much more complex than JSP. We present a domain independent genetic algorithm (GA) approach for the job shop scheduling problem with alternative machines. The GA is implemented in a spreadsheet environment. The performance of the proposed GA is analyzed by comparing with various problem instances taken from the literatures. The result shows that the proposed GA is competitive with the existing approaches. A simplified approach that would be beneficial to both practitioners and researchers is presented for solving scheduling problems with alternative machines.