Earticle

초록

영어

MES job shop scheduling problem is the core module, and its essence is a kind of resource constraints, time constraints and process constraints such as combinatorial optimization problems, research and application of job shop scheduling problem for China's manufacturing sector improve management, productivity, and the implementation of advanced manufacturing strategies are important, therefore, job shop scheduling research has theoretical and practical significance. In this paper, the slow evolution of GA algorithm will occur, or premature, and the SA algorithm is introduced to the crossover and mutation, genetic simulated annealing algorithm is proposed - GASA algorithm, mathematical model is given algorithm, the algorithm model, the algorithm process. GA parallel sampling of the time optimization algorithm performance can be improved, and the control of SA binding guidelines to control the convergence of the algorithm to avoid prematurity. The classical scheduling problem FT06, GASA correctness of the algorithm is verified and compared with the traditional GA algorithm is verified GASA efficiency of the algorithm.

목차

Abstract
 1. Introduction
 2. The Description of Single and Small Batch Job-Shop Scheduling Problem
  2.1. The Mathematical Model of Single Piece and Small Batch Scheduling Problem
 3. GASA Hybrid Scheduling Algorithm
 4. The Simulation Experiments of GASA Hybrid Algorithm On Job-Shop Scheduling Problem
  4.1 A Typical Job Shop Scheduling Problem
  4.2 FT06 Scheduling Experiments
 5. Conclusions
 References

키워드

저자

• Huangtao [ Harbin University of Science and Technology, Harbin, China ]
• Tan yanna [ China United Network Communications Corp Harbin branch, Harbin, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of u- and e- Service, Science and Technology

• 간기

  격월간

• pISSN

  2005-4246

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605