Accelerating column generation schemes : applications to routing problems

Abstract: Many integer optimization problems of great practical importance are today attacked with column generation. Merits of column generation is that it enables the use of compact and flexible formulations of many complex optimization problems, and that it often gives rise to good (strong) formulations. A potential drawback with column generation is the well-known tailing-off phenomenon, that is, that the objective value is improved rapidly in early iterations but very slowly in the late iterations.We study various techniques for accelerating column generation methods for (integer) linear programs. We evaluate the use of stabilized column generation on a Traveling Salesman Subtour Problem, TSSP, a problem which is closely related to the Prize Collecting Traveling Salesman Problem. We further study how subgradient optimization can be used with the purpose of predicting optimal columns (and, optionally, non-binding restrictions). This technique is tested on the TSSP and the multicommodity network flow problem.Another idea evaluated in this thesis is the use of over-generation of columns in a column generation scheme for an integer programming problem, in this case a vehicle routing problem with a heterogeneous fleet of vehicles.The thesis also includes a note on a class of relatives to the Held and Karp 1–tree problem. It is shown that two subclasses have polynomial time-complexity. Further, we note a mistake in an earlier work and provide a counter-example to the erroneous result.