Hopfield신경망에 의한 주문생산 일정계획

• 영문명: Job shop scheduling for hopfield neural networks
• 발행기관: 건국대학교 경제경영연구소
• 저자명: 표준승(Soon Seung Pyo)
• 간행물 정보: 『상경연구』제24권 제1호, 65~83쪽, 전체 19쪽
• 주제분류: 경제경영 > 경제학
• 파일형태: PDF
• 발행일자: 1999.06.30

영문 초록

Job shop scheduling belongs to a called Constraint optimization problems. Scheduling involves looking for a combination that minimizes a cost function subject to the given constraints of the problem. For the constraing optimization problems dealing with discrete of continuous parameters, it is desirable to find the optimal value such that energy function, Lyapunov function can be minimized. In job shop scheduling, these are n jobs with m machines, it is desirable to find the best job sequence in scheduling the n jobs on the m machines so that the earliest completion time can be achieved. Since job shop scheduling have been shown to be NP-complete problems, this means that the computation requirement does increase in a polynomial fashion. For NP-complete problems, the optimal shlution in not always obvious and cannot be guaranteed unless the entire solution base is known. In this paper, the simulated annealing approach is applied for the minimization of energy function. Experiment results, it can be speeded up the convergence rate of the energy function.

목차

Ⅰ. 서론

Ⅱ. Hopfield신경망의 수학적 특성

Ⅲ. 주문생산일정계획에의 적용

Ⅳ. 수치예

Ⅴ. 결론

참고문헌

Abstract