Genetic Algorithm for Hybrid Flow-Shop Scheduling with Parrel Batch Processors

Author

Feng, Haodi ; Lu, Shenpeng ; Li, Xiuqian

Author_Institution

Sch. of Comput. Sci. & Technol., Shandong Univ., Jinan, China

Volume

2

fYear

2009

fDate

10-11 July 2009

Firstpage

9

Lastpage

13

Abstract

In classical flow-shop scheduling problem, each processor can process one job at a time. However, in practice, there may be many processors that can process jobs batch by batch. We call these processors batch processors. If the processing time of a batch is equal to the largest processing time among its members, we call such a batch processor parallel batch processor. In this paper, we study the hybrid flow-shop problem in which the processors are parrel batch processors. This problem is obviously NP-hard. Therefore, we propose a genetic algorithm in this work.

Keywords

batch processing (industrial); flow shop scheduling; genetic algorithms; parallel algorithms; NP-hard problem; genetic algorithm; hybrid flow-shop scheduling; parallel batch processor; parrel batch processor; Approximation algorithms; Computer science; Decoding; Encoding; Genetic algorithms; Genetic engineering; Genetic mutations; Heuristic algorithms; Processor scheduling; Real time systems; flow-shop scheduling; genetic algorithm; parallel batch processor;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Engineering, 2009. ICIE '09. WASE International Conference on

Conference_Location

Taiyuan, Chanxi

Print_ISBN

978-0-7695-3679-8

Type

conf

DOI

10.1109/ICIE.2009.87

Filename

5211484