On the computation of all supported efficient solutions in multi-objective integer network flow problems

This paper presents a new algorithm for identifying all supported non-dominated vectors (or outcomes) in the objective space, as well as the corresponding efficient solutions in the decision space, for multi-objective integer network flow problems. Identifying the set of supported non-dominated vectors is of the utmost importance for obtaining a first approximation of the whole set of non-dominated vectors. This approximation is crucial, for example, in two-phase methods that first compute the supported non-dominated vectors and then the unsupported non-dominated ones. Our approach is based on a negative-cycle algorithm used in single objective minimum cost flow problems, applied to a sequence of parametric problems. The proposed approach uses the connectedness property of the set of supported non-dominated vectors/efficient solutions to find all integer solutions in maximal non-dominated/efficient facets.

[1]  Antonio Sedeño-Noda,et al.  An algorithm for the biobjective integer minimum cost flow problem , 2001, Comput. Oper. Res..

[2]  Hanif D. Sherali,et al.  Linear Programming and Network Flows , 1977 .

[3]  Horst W. Hamacher,et al.  A note onK best network flows , 1995, Ann. Oper. Res..

[4]  José Rui Figueira,et al.  Finding non-dominated solutions in bi-objective integer network flow problems , 2009, Comput. Oper. Res..

[5]  Anthony Przybylski,et al.  The biobjective integer minimum cost flow problem - incorrectness of Sedeño-Noda and Gonzàlez-Martin's algorithm , 2006, Comput. Oper. Res..

[6]  Heinz Isermann,et al.  The Enumeration of the Set of All Efficient Solutions for a Linear Multiple Objective Program , 1977 .

[7]  R. J. Gallagher,et al.  A combined constraint-space, objective-space approach for determining high-dimensional maximal efficient faces of multiple objective linear programs , 1996 .

[8]  R. S. Laundy,et al.  Multiple Criteria Optimisation: Theory, Computation and Application , 1989 .

[9]  Günther Ruhe,et al.  Complexity results for multicriterial and parametric network flows using a pathological graph of Zadeh , 1988, ZOR Methods Model. Oper. Res..

[10]  T. Gal A general method for determining the set of all efficient solutions to a linear vectormaximum problem , 1977 .

[11]  Ravindra K. Ahuja,et al.  Network Flows: Theory, Algorithms, and Applications , 1993 .

[12]  Horst W. Hamacher,et al.  Multiple objective minimum cost flow problems: A review , 2007, Eur. J. Oper. Res..

[13]  Matthias Ehrgott,et al.  Multicriteria Optimization , 2005 .

[14]  Matthias Ehrgott,et al.  A primal–dual simplex algorithm for bi-objective network flow problems , 2009, 4OR.

[15]  P. S. Pulat,et al.  Bicriteria network flow problems: Integer case , 1993 .