Combinatorial Optimization: finding an optimal solution from a finite set of solutions
Countless practical optimization problems are, in fact, combinatorial optimization problems: they have an optimal solution that needs to be found amongst a finite set of possible solutions. The aim of combinatorial optimization (CO) is to rapidly and efficiently find such an optimal solution.
CO is related to discrete mathematics, theoretical computer science, applied mathematics, operations research, algorithm theory and computational complexity theory and has important applications in several fields. These include scheduling, production planning, logistics, network design, communication and routing in networks, health care, artificial intelligence, machine learning, auction theory, and software engineering.
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Our most recent peer reviewed publications
Building Kidney Exchange Programmes in EuropeTransplantation (2019)
Online interval scheduling on two related machinesJournal of Combinatorial Optimization (2019)
On a generalization of iterated and randomized rounding51st Annual ACM SIGACT Symposium on Theory of Computing, STOC 2019 (2019)
Scheduling parallel batching machines in a sequenceJournal of Scheduling (2019)
The sport teams grouping problemAnnals of Operations Research (2019)