A combined dispatching criteria approach to scheduling dual flow-shops

Abstract

This study investigates a dual flow-shops scheduling problem. In the scheduling context, there are two flow shops and each shop involves three processing stages. The two shops are functionally identical but their stage processing times for a job are different. While sequentially going through the three processing stages, each job is allowed to travel between the two shops. That is, for a job, each of its three stages could be processed in any of the two shops. Such a context is called dual flow-shops in the sense that the two flow shops' capacities are completely shared. The scheduling problem involves two decisions: (1) route assignment (i.e. assigning the processing stages of a job to a shop), and (2) job sequencing (i.e. sequencing the jobs waiting before each stage). The scheduling objective is to minimise the coefficient of variation of slack time (CV S ), in which the slack time (also called lateness) denotes the difference between the due date and total completion time of a job. We propose five genetic-algorithm-based (GA-based) solution methods to solve the scheduling problem, which are called GA-EDD, GA-FIFO, GA-SPT, GA-LFO, and GA-COMBO respectively. Numerical experiments indicate that GA-COMBO outperforms the other four methods.

A hybrid particle swarm optimisation for scheduling just-in-time single machine with preemption, machine idle time and unequal release times

“…Mejia et al (2012) proposed the genetic algorithms and Petrinets approach, which combined the modelling power of Petrinets with the optimisation capability of GAs for manufacturing system scheduling. Chiou, Chen, and Wu (2013) investigated a dual flow shop-scheduling problem. The scheduling objective was to minimise the coefficient of variation of slack time (CV S ), in which the slack time (also called lateness) denoted the difference between the due date and total completion time of a job.…”