SIAM Journal on Computing
Near-optimal solutions to one-dimensional cutting stock problems
Computers and Operations Research
An efficient approximation scheme for variable-sized bin packing
SIAM Journal on Computing
Bin packing with divisible item sizes
Journal of Complexity
Optimization of roll cutting in clothing industry
Computers and Operations Research
Using Extra Dual Cuts to Accelerate Column Generation
INFORMS Journal on Computing
Dual-Optimal Inequalities for Stabilized Column Generation
Operations Research
Solving the variable size bin packing problem with discretized formulations
Computers and Operations Research
Strips minimization in two-dimensional cutting stock of circular items
Computers and Operations Research
Relaxations and exact solution of the variable sized bin packing problem
Computational Optimization and Applications
New Stabilization Procedures for the Cutting Stock Problem
INFORMS Journal on Computing
The min-conflict packing problem
Computers and Operations Research
LP bounds in various constraint programming approaches for orthogonal packing
Computers and Operations Research
Efficient algorithms for real-life instances of the variable size bin packing problem
Computers and Operations Research
A note on branch-and-cut-and-price
Operations Research Letters
Shadow price based genetic algorithms for the cutting stock problem
International Journal of Artificial Intelligence and Soft Computing
Computers and Industrial Engineering
One-Dimensional Cutting Stock Optimization with Usable Leftover: A Case of Low Stock-to-Order Ratio
International Journal of Decision Support System Technology
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Many heuristic approaches have been proposed in the literature for the multiple length cutting stock problem, while only few results have been reported for exact solution algorithms. In this paper, we present a new branch-and-price-and-cut algorithm which outperforms other exact approaches proposed so far. Our conclusions are supported on many computational experiments conducted on instances from the literature. In the second part of the paper, we investigate the use of valid dual inequalities within the previous algorithm. We show how column generation can be accelerated in all the nodes of our branching tree using such inequalities. Until now, dual inequalities have been applied only for original linear programming relaxations. Their validity within a branch-and-bound procedure has never been investigated. Our computational experiments show that extending these inequalities to the whole branch-and-bound tree can significantly improve the convergence of our branch-and-price-and-cut algorithm.