An application of genetic algorithms to lot-streaming flow shop scheduling

Abstract

A Hybrid Genetic Algorithm (HGA) approach is proposed for a lot-streaming flow shop scheduling problem, in which a job (lot) is split into a number of smaller sublots so that successive operations can be overlapped. The objective is the minimization of the mean weighted absolute deviation of job completion times from due dates. This performance criterion has been shown to be non-regular and requires a search among schedules with intermittent idle times to find an optimal solution. For a given job sequence, a Linear Programming (LP) formulation is presented to obtain optimal sublot completion times. Objective function values of LP solutions are used to guide the HGA's search toward the best sequence. The performance of the HGA approach is compared with that of a pairwise interchange method.