A lagrangian relaxation-based heuristic to solve large extended graph partitioning problems

Publication Type:
Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, 9627 pp. 327 - 338
Issue Date:
Full metadata record
© Springer International Publishing Switzerland 2016. The paper is concerned with the planning of training sessions in large organisations requiring periodic retraining of their staff. The allocation of students must take into account student preferences as well as the desired composition of study groups. The paper presents a bicriteria Quadratic Multiple Knapsack formulation of the considered practical problem, and a novel solution procedure based on Lagrangian relaxation. The paper presents the results of computational experiments aimed at testing the optimisation procedure on real world data originating from Australia’s largest electricity distributor. Results are compared and validated against a Genetic Algorithm based matheuristic.
Please use this identifier to cite or link to this item: