2009
Authors
Vasil'ev, IL; Klimentova, KB; Kochetov, YA;
Publication
Computational Mathematics and Mathematical Physics
Abstract
A bilevel facility location problem in which the clients choose suppliers based on their own preferences is studied. It is shown that the coopertative and anticooperative statements can be reduced to a particular case in which every client has a linear preference order on the set of facilities to be opened. For this case, various reductions of the bilevel problem to integer linear programs are considered. A new statement of the problem is proposed that is based on a family of valid inequalities that are related to the problem on a pair of matrices and the set packing problem. It is shown that this formulation is stronger than the other known formulations from the viewpoint of the linear relaxation and the integrality gap. © 2009 Pleiades Publishing, Ltd.
2010
Authors
Vasilyev, IL; Klimentova, KB;
Publication
Journal of Applied and Industrial Mathematics
Abstract
Numerical study is provided of the methods for solving the facility location problem when the clients choose some suppliers by their own preferences. Various formulations of this problem as an integer linear programming problem are considered. The authors implement a cutting plane method based on the earlier proposed family of valid inequalities which arises from connection with the problem for a pair of matrices. The results of numerical experiment are presented for testing this method. An optimal solution is obtained by the two versions of the branch and cut method with the suggested cutting plane method. The simulated annealing method is proposed for obtaining the upper bounds of the optimal solution used in exact methods. Numerical experiment approves the efficiency of the implemented approach in comparison with the previously available methods. © 2010 Pleiades Publishing, Ltd.
2023
Authors
Klimentova, X; Biro, P; Viana, A; Costa, V; Pedroso, JP;
Publication
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Abstract
Kidney exchange programs (KEPs) represent an additional possibility of transplant for patients suffering from end-stage kidney disease. If a patient has a willing living donor with whom the patient is not compatible, the pair recipient-donor can join a pool of incompatible pairs and, if compatibility between recipient and donor in two or more pairs exists, organs can be exchanged between them. The problem can be modelled as an integer program that in general aims at finding the pairs that should be selected for transplant such that maximum number of transplants is performed. In this paper, we consider that for each recipient there may exist a preference order over the organs that he/she can receive, since a recipient may be compatible with several donors but the level of compatibility with the recipient might vary for different donors. Under this setting, the aim is to find the maximum cardinality stable exchange, a solution where no blocking cycle exists, i.e., there is no cycle such that all recipients prefer the donor in that cycle rather than that in the exchange. For this purpose we propose four novel integer programming models based on the well-known edge and cycle formulations, and also on the position-indexed formulation. These formulations are adjusted for both finding stable and strongly stable exchanges under strict preferences and for the case when ties in preferences may exist. Further-more, we study a situation when the stability requirement can be relaxed by addressing the trade-off between maximum cardinality versus number of blocking cycles allowed in a solution. The effectiveness of the proposed models is assessed through extensive computational experiments on a wide set of in-stances. Results show that the cycle-edge and position-indexed formulations outperform the other two formulations. Another important practical outcome is that targeting strongly stable solutions has a much higher negative impact on the number of transplants (with an average reduction of up to 20% for the bigger instances), when compared to stable solutions.
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