Earticle

초록

영어

Heuristic evolutionary optimization algorithms are the solutions to many engineering optimization problems. Differential evolution (DE) is a real stochastic evolutionary parameter optimization in current use.DE does not require more control parameters compared to other evolutionary algorithms. Master Production Scheduling (MPS) is posed as one of multi objective parameter optimization problems and often require an optimal solution for the success of a business organization by balancing demand and supply. This work reviews some of the fundamental theory of differential evolution, the methodology for master production scheduling calculation and most important results. The results available for the existing algorithms are compared with results obtained by the proposed evolutionary algorithm. The analysis reveals that the DE algorithm provides a better solution with reasonable computational time.

목차

Abstract
 1. Introduction
 2. Multi-objective optimization for MPS using differential evolution (MOMDE)
  2.1. Chromosome representation
  2.2. Basics of differential evolution
  2.3. Initial population criteria
  2.4. The stopping criteria
 3. The Fitness Function
 4. MPS problem considered
 5. Results and discussion
 6. Conclusions and Future Scope
 References

키워드

저자

• S. Radhika [ Dept. of ME, R.V.R & J.C College of Engineering (Autonomous), Guntur ] Corresponding Author
• Ch. Srinivasa Rao [ Dept. of ME, Andhra University College of Engineering (Autonomous), Visakhapatnam ]
• K. Karteeka Pavan [ Dept. of IT, R.V.R. & J.C College of Engineering (Autonomous), Guntur ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605