@inproceedings{ce541fb8b52845dea523e5fd090d0019,
title = "Maximum concurrent flow with incomplete data",
abstract = "The Maximum Concurrent Flow Problem (MCFP) is often used in the planning of transportation and communication networks. We discuss here the MCFP with incomplete data. We call this new problem the Incomplete Maximum Concurrent Flow Problem (IMCFP). The main objective of IMCFP is to complete the missing information assuming the known and unknown data form a MCFP and one of its optimal solutions. We propose a new solution technique to solve the IMCFP which is based on a linear programming formulation involving both primal and dual variables, which optimally decides values for the missing data so that they are compatible with a set of scenarios of different incomplete data sets. We prove the correctness of our formulation and benchmark it on many different instances.",
keywords = "Incomplete data, Inverse optimization, Maximum concurrent flow, Multi-commodity flow problems, Transportation systems, Uncertainty, Unknown data",
author = "Bauguion, \{Pierre Olivier\} and Claudia D{\textquoteright}Ambrosio and Leo Liberti",
note = "Publisher Copyright: {\textcopyright} 2018, Springer International Publishing AG, part of Springer Nature.; 5th International Symposium on Combinatorial Optimization, ISCO 2018 ; Conference date: 11-04-2018 Through 13-04-2018",
year = "2018",
month = jan,
day = "1",
doi = "10.1007/978-3-319-96151-4\_7",
language = "English",
isbn = "9783319961507",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "77--88",
editor = "Giovanni Rinaldi and Mahjoub, \{A. Ridha\} and Jon Lee",
booktitle = "Combinatorial Optimization - 5th International Symposium, ISCO 2018, Revised Selected Papers",
}