The domination heuristic for LP-type problems

Деякі задачі геометричної оптимізації, наприклад пошук найменшого покриваючого еліпса множини точок, можуть бути розв'язані за лінійний час, використовуючи нескладні випадкові (чи складні детерміновані) комбінаторні алгоритми. На практиці ці алгоритми поліпшуються чи заміняються варіантами евристик, що працюють швидше, але теоретичні оцінки часу роботи для них не доведені. У цій статті ми пропонуємо нову прискорюючу евристику, що може бути легко застосована до відомих лінійних алгоритмів, без зменшення їх швидкості у найгіршому випадку. Ми показуємо, що ця евристика може бути визначена для будь-якої задачі з добре відомого класу задач лінійного програмування. Її ефективність на практиці залежить від того, чи можлива, і якщо мож¬ лива, то наскільки швидкою виявиться реалізація предиката для конкретної задачі. Ми наводимо результати експериментів, які показують, що для двох задач нова евристика може значно приско¬ рити існуючі реалізації алгоритмів (з бібліотеки геометричних алгоритмів CGAL)

Solving a robust airline crew pairing problem with column generation

In this study, we solve a robust version of the airline crew pairing problem. Our concept of robustness was partially shaped during our discussions with small local airlines in Turkey which may have to add a set of extra flights into their schedule at short notice during operation. Thus, robustness in this case is related to the ability of accommodating these extra flights at the time of operation by disrupting the original plans as minimally as possible. We focus on the crew pairing aspect of robustness and prescribe that the planned crew pairings incorporate a number of predefined recovery solutions for each potential extra flight. These solutions are implemented only if necessary for recovery purposes and involve either inserting an extra flight into an existing pairing or partially swapping the flights in two existing pairings in order to cover an extra flight. The resulting mathematical programming model follows the conventional set covering formulation of the airline crew pairing problem typically solved by column generation with an additional complication. The model includes constraints that depend on the columns due to the robustness consideration and grows not only column-wise but also row-wise as new columns are generated. To solve this dicult model, we propose a row and column generation approach. This approach requires a set of modifications to the multi-label shortest path problem for pricing out new columns (pairings) and various mechanisms to handle the simultaneous increase in the number of rows and columns in the restricted master problem during column generation. We conduct computational experiments on a set of real instances compiled from a local airline in Turkey

Column generation approaches to ship scheduling with flexible cargo sizes

We present a Dantzig-Wolfe procedure for the ship scheduling problem with flexible cargo sizes. This problem is similar to the well-known pickup and delivery problem with time windows, but the cargo sizes are defined by an interval instead of a fixed value. We show that the introduction of flexible cargo sizes to the column generation framework is not straightforward, and we handle the flexible cargo sizes heuristically when solving the subproblems. This leads to convergence issues in the branch-and-price search tree, and the optimal solution cannot be guaranteed. Hence we have introduced a method that generates an upper bound on the optimal objective. We have compared our method with an a priori column generation approach, and our computational experiments on real world cases show that the Dantzig-Wolfe approach is faster than the a priori generation of columns, and we are able to deal with larger or more loosely constrained instances. By using the techniques introduced in this paper, a more extensive set of real world cases can be solved either to optimality or within a small deviation from optimalityTransportation; integer programming; dynamic programming