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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한산업공학회 Industrial Engineering & Management Systems Industrial Engineering & Management Systems Vol.18 No.1
- 발행연도
- 2019.3
- 수록면
- 132 - 142 (11page)
- DOI
- 10.7232/iems.2019.18.1.132
이용수
초록· 키워드
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This paper addresses the solution of a two-stage stochastic mixed integer programming model for an investment of planning problems applied to the petroleum products supply chain. In this context, we present the development of decomposition techniques for the stochastic Benders decomposition and Lagrangian relaxation methods. The combinational technique of cross decomposition is a suitable one for exact solution of the mixed integer programming problems which uses simultaneously the advantages of Lagrangian relaxation and Benders decomposition methods that each reinforces one another.
The basic idea for this technique is the generation of suitable upper and lower bounds for the optimal value of the original problem at each iteration. In this paper, as far as cross decomposition algorithm is concerned, we present a new hybrid Lagrangian relaxation algorithm for updating the Lagrangian multiplier set, based on the combination of cutting-plane, sub-gradient and trust-region strategies. The convergence of this technique regarding the convergence of Benders decomposition method in finite iteration numbers has been guaranteed.
Results suggest that the proposed approach is able to efficiently solve the problem under consideration, achieving better performance in terms of computational times as compared to other techniques.
The basic idea for this technique is the generation of suitable upper and lower bounds for the optimal value of the original problem at each iteration. In this paper, as far as cross decomposition algorithm is concerned, we present a new hybrid Lagrangian relaxation algorithm for updating the Lagrangian multiplier set, based on the combination of cutting-plane, sub-gradient and trust-region strategies. The convergence of this technique regarding the convergence of Benders decomposition method in finite iteration numbers has been guaranteed.
Results suggest that the proposed approach is able to efficiently solve the problem under consideration, achieving better performance in terms of computational times as compared to other techniques.
목차
ABSTRACT
1. INTRODUCTION
2. BACKGROUND
3. PROBLEM STATEMENT
4. MATHEMATICAL MODEL
5. SOLUTION ALGORITHM
6. ILLUSTRATIVE CASE STUDY
7. CONCLUSION
REFERENCES
참고문헌 (28)
참고문헌 신청
Barahona, F. / 2000 / The volume algorithm : producing primal solutions with a sub-gradient method / Mathematical Programming 87(3):385~399
Benders, J. F. / 1962 / Partitioning procedures for solving mixed-variables programming problems / Numerische Mathematik 4(1):238~252
Carøe, C. C. / 1999 / Dual decomposition in stochastic integer programming / Operations Research Letters 24(1-2):37~45
Cheney, E. W. / 1959 / Newton’s method for convex programming and Tchebycheff approximation / Numerische Mathematik 1(1):253~268
Christopher, M. / 2016 / Logistics & supply chain management / Pearson UK
Benders, J. F. / 1962 / Partitioning procedures for solving mixed-variables programming problems / Numerische Mathematik 4(1):238~252
Carøe, C. C. / 1999 / Dual decomposition in stochastic integer programming / Operations Research Letters 24(1-2):37~45
Cheney, E. W. / 1959 / Newton’s method for convex programming and Tchebycheff approximation / Numerische Mathematik 1(1):253~268
Christopher, M. / 2016 / Logistics & supply chain management / Pearson UK
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