Very Large-Scale Neighborhood Search for the K-Constraint Multiple Knapsack Problem

Authors:
Ravindra K. Ahuja;Claudio B. Cunha
Affiliations:
Department of Industrial and Systems Engineering, University of Florida, Gainesville, USA 32611;Department of Transportation Engineering, Escola Politécnica, University of São Paulo, São Paulo, Brazil
Venue:
Journal of Heuristics
Year:
2005

Citing 14
Cited 2

A simulated annealing approach to the multiconstraint zero-one knapsack problem

Computing
Knapsack problems: algorithms and computer implementations

Knapsack problems: algorithms and computer implementations
Network flows: theory, algorithms, and applications

Network flows: theory, algorithms, and applications
Cyclic transfer algorithms for multivehicle routing and scheduling problems

Operations Research
TSP ejection chains

GO-II Meeting Proceedings of the second international colloquium on Graphs and optimization
A greedy genetic algorithm for the quadratic assignment problem

Computers and Operations Research
Tabu Search

Tabu Search
A Genetic Algorithm for the Multidimensional Knapsack Problem

Journal of Heuristics
A survey of very large-scale neighborhood search techniques

Discrete Applied Mathematics
An Exact Algorithm for the Two-Constraint 0--1 Knapsack Problem

Operations Research
A Multi-Exchange Heuristic for the Single-Source Capacitated Facility Location Problem

Management Science
Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem

INFORMS Journal on Computing
A composite very large-scale neighborhood structure for the capacitated minimum spanning tree problem

Operations Research Letters
A scheme for exact separation of extended cover inequalities and application to multidimensional knapsack problems

Operations Research Letters

Enabling rich mobile applications: joint computation and communication scheduling

ACM SIGMOBILE Mobile Computing and Communications Review
The Express heuristic for probabilistically constrained integer problems

Journal of Heuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

The K-Constraint Multiple Knapsack Problem (K-MKP) is a generalization of the multiple knapsack problem, which is one of the representative combinatorial optimization problems known to be NP-hard. In K-MKP, each item has K types of weights and each knapsack has K types of capacity. In this paper, we propose several very large-scale neighborhood search (VLSN) algorithms to solve K-MKP. One of the VLSN algorithms incorporates a novel approach that consists of randomly perturbing the current solution in order to efficiently produce a set of simultaneous non-profitable moves. These moves would allow several items to be transferred from their current knapsacks and assigned to new knapsacks, which makes room for new items to be inserted through multi-exchange movements and allows for improved solutions. Computational results presented show that the method is effective, and provides better solutions compared to exact algorithms run for the same amount of time.