Title :
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
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;
Conference_Titel :
Information Engineering, 2009. ICIE '09. WASE International Conference on
Conference_Location :
Taiyuan, Chanxi
Print_ISBN :
978-0-7695-3679-8
DOI :
10.1109/ICIE.2009.87
