MULTI-CRITERIA OPTIMIZATION OF N-STEP HYBRID FLOW-SHOP SCHEDULING

- MAKE-TO-ORDER CASES WITH BATCH PROCESS -

Abstract

This paper introduced n-step hybrid flow-shop scheduling (nHFS) of product-mix Make-to-Order production with individual job's loading condition and delivery dates. The ability sometimes depends on the loading rate of a batch process and usable resource constraints due to a job specification in an actual manufacturing process. Based on the nHFS solution proposed by J.N.D Gupta, et.al. (2002) (hereby, n-Gupta), three extensions are presented. First is the optimization of weighting parameters of multi-objective functions without domain knowledge implementation. Particularly, higher performance ensemble methods of Bayesian optimization based on Gaussian Process regression are applied. Second is the appropriate product-mix batch loading considering restricted usable resources. The third is the extension of the multi-objective function to consider the flow balance and capacity distribution between the flows. Numerical evaluation results in a better performance than job-shop scheduling and actual companies' plan, in almost all of the delivery satisfactions, makespan, batch loading rate, and so on.