Author(s):
Alves, Maria João 
; Clímaco, João
Date: 2000
Persistent ID: http://hdl.handle.net/10316/5505
Origin: Estudo Geral - Universidade de Coimbra
Document details
An interactive reference point approach for multiobjective mixed-integer progra...
Description
We propose an interactive reference point approach for multiple objective (mixed) integer linear programming problems that exploits the use of branch-and-bound techniques for solving the scalarizing programs. At each dialogue phase, the decision maker must specify a criterion reference point or just choose an objective function he/she wants to improve in respect to the previous efficient (nondominated) solution. In the latter case, a directional search is performed adjusting automatically the reference point used at each stage. Tchebycheff mixed-integer scalarizing programs are successively solved by branch-and-bound. Postoptimality techniques have been developed enabling the algorithm to profit from previous computations to solve the next scalarizing programs. The previous branch-and-bound tree is used as a starting point and operations of simplification and branching are then performed to obtain a new efficient solution. Computational results have shown that this approach is effective for carrying out directional or local searches for efficient solutions. http://www.sciencedirect.com/science/article/B6VCT-4096330-4/1/98c2af744fdd26ba932a7ca1ced5c947
We propose an interactive reference point approach for multiple objective (mixed) integer linear programming problems that exploits the use of branch-and-bound techniques for solving the scalarizing programs. At each dialogue phase, the decision maker must specify a criterion reference point or just choose an objective function he/she wants to improve in respect to the previous efficient (nondominated) solution. In the latter case, a directional search is performed adjusting automatically the reference point used at each stage. Tchebycheff mixed-integer scalarizing programs are successively solved by branch-and-bound. Postoptimality techniques have been developed enabling the algorithm to profit from previous computations to solve the next scalarizing programs. The previous branch-and-bound tree is used as a starting point and operations of simplification and branching are then performed to obtain a new efficient solution. Computational results have shown that this approach is effective for carrying out directional or local searches for efficient solutions. http://www.sciencedirect.com/science/article/B6VCT-4096330-4/1/98c2af744fdd26ba932a7ca1ced5c947
Document Type
Article
Language English
Language English
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