D
David Pisinger
Researcher at Technical University of Denmark
Publications - 175
Citations - 12726
David Pisinger is an academic researcher from Technical University of Denmark. The author has contributed to research in topics: Knapsack problem & Network planning and design. The author has an hindex of 45, co-authored 175 publications receiving 10799 citations. Previous affiliations of David Pisinger include University of Copenhagen & University of Copenhagen Faculty of Science.
Papers
More filters
Book ChapterDOI
Introduction to NP-Completeness of Knapsack Problems
TL;DR: The reader may have noticed that for all the considered variants of the knapsack problem, no polynomial time algorithm have been presented which solves the problem to optimality.
Journal ArticleDOI
The Vessel Schedule Recovery Problem (VSRP) – A MIP model for handling disruptions in liner shipping
TL;DR: The Vessel Schedule Recovery Problem (VSRP) is presented to evaluate a given disruption scenario and to select a recovery action balancing the trade off between increased bunker consumption and the impact on cargo in the remaining network and the customer service level.
Journal ArticleDOI
An exact algorithm for large multiple knapsack problems
TL;DR: A new exact algorithm for the Multiple Knapsack Problem is presented, which is specially designed for solving large problem instances and has a stable performance even for instances with coefficients in a moderately large range.
Journal ArticleDOI
An expanding-core algorithm for the exact 0–1 knapsack problem
TL;DR: A new branch-and-bound algorithm for the exact solution of the 0–1 Knapsack Problem is presented, which is based on solving an ‘expanding core’, which intially only contains the break item, but which is expanded each time the branch- and- bound algorithm reaches the border of the core.
Journal ArticleDOI
The two-dimensional bin packing problem with variable bin sizes and costs
David Pisinger,Mikkel M. Sigurd +1 more
TL;DR: An integer-linear formulation of the 2DVSBPP is presented and several lower bounds for the problem are introduced, by using Dantzig-Wolfe decomposition, to obtain lower bounds of very good quality.