한국경영과학회지 (Journal of the Korean Operations Research and Management Science Society)

  • 제14권2호
  • /
  • Pages.105-114
  • /
  • 1989
  • /
  • 1225-1119(pISSN)
  • /
  • 2733-4759(eISSN)
한국경영과학회 (The Korean Operations Research and Management Science Society)
권호 표지

제약부 구간 선형 회귀모델에 의한 실동시간의 견적

Estimation of the Actual Working Time by Interval Linear Regression Models with Constraint Conditions

  • Hwang, S. G. (Department of Industrial Engineering, University of OsakaPrefecture) ;
  • Seo, Y. J. (Department of Industrial Engineering, University of KyungNam)
  • 발행 : 1989.12.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

The actual working time of jobs, in general, is different to the standard time of jobs. In this paper, in order to analyze the actual working time of each job in production, we use the total production amount and the encessary total working time. The method which analyzes the actual working time is as follows. In this paper, we propose the interval regression analysis for obtaining an interval linear regression model with constraint conditions with respect to interval parameters. The merits of this method are the following.1) it is easy to obtain an interval linear model by solving a LP problem to which the formulation of proposed regression analysis is reduced, 2) it is easy to add constraint conditions about interval parameters, which are a sort of expert knowledge. As an application, within a case which has given certain data, the actual working time of jobs and the number of workers in a future plan are estimated through the real data obtianed from the operation of processing line in a heavy industry company. It results from the proposed method that the actual working time and the number of workers can be estimated as intervals by the interval regression model.

키워드

참고문헌

  1. Handbook of Industrial Engineering Time Standard Niebel,B.W.
  2. Motion and Time Study Design and Measurement of work(7th ed.) Barnes,R.M.
  3. Work Study Senjyu,S.
  4. Fuzzy Sets and Systems v.1 no.1 Fuzzy Sets as a Basis for a Theory of Possibility Zadeh,L.A.
  5. J. of the Operations Research Society of Japan v.25 no.2 A Linear Regression Model with Fuzzy Functions Tanaka,H.;Uejima,S.;Asai,K.
  6. IEEE Trans. v.SMC-12 Linear Regression Analysis with Fuzzy Model Tanaka,H.;Uejima,S.;Asai,K.
  7. IEEE Trans. v.SMC-14 Fuzzy Solution in Fuzzy Linear Programming Problems Tanaka,H.;Asai,K.
  8. System and Control v.28 no.7 Possibilistic Model and its Applications Tanaka,H.
  9. The Japanese Journal of Ergonomics v.22 no.5 Identification of Learning Curve Based on Fuzzy Regression Model Tanaka,H.;Simomura,T.;Watada,J.
  10. Systems and Control v.30 no.9 The Formulation of the Fuzzy GMDH Tanaka,H.;Watada,J.;Hayashi,I.;Asai,K.
  11. Trans. of the Society of Instrument and Control Engineers v.22 no.10 On Three Formulations of Fuzzy Linear Regression Analysis Tanaka,H.;Watada,J.;Hayashi,I.
  12. Fuzzy Sets and Systems v.24 no.3 Fuzzy Data Analysis by Possibilistic Linear Models Tanaka,H.
  13. APORS' 88, Seoul, Korea on August 24-26 Institution of Standard Time by Possibilistic Linear Regression with Restriction Seo,Y.J.;Hwang,S.G.;Hayashi,I.;Tanaka,H.
  14. 3rd Fuzzy system Symposium Interval Regression Analysis Tanaka,H.;Hayashi,I.;Watada,J.
  15. Fuzzy Sets and Systems, Theory and Application Dubois,D.;Prade,H.
  16. IEA 10th International Congress Proceedings, Sydney Australia on August 1-5 Analysis of Human Judgement by Interval Hwang,S.G.;Hayashi,I.;Tanaka,H.
  17. The Journal of Industrial Engineering v.17 no.4 Using Linear Programming to Determine Time Standars Chrisman,J.A.
  18. A mathematical Theory of Evidence Shafer,G.
  19. Introduction to Interval Computations Alefeld,G.;Herzberger,J.;J.Rokne(trans.)
  20. SIAM Methods and Applications of Interval Analysis Moore,R.E.
  21. Measurement of Amount of Office Work and Actual Affairs of the Fixed Personnel Estimation Konou,H.