Modelling and strong linear programs for mixed integer programming
Algorithms and model formulations in mathematical programming
Solving binary cutting stock problems by column generation and branch-and-bound
Computational Optimization and Applications
Computing Partitions with Applications to the Knapsack Problem
Journal of the ACM (JACM)
Branch-And-Price: Column Generation for Solving Huge Integer Programs
Operations Research
A Note on Branch-and-Price Algorithms for the One-Dimensional Cutting Stock Problems
Computational Optimization and Applications
Evaluation of algorithms for one-dimensional cutting
Computers and Operations Research
A genetic algorithm for 1,5 dimensional assortment problems with multiple objectives
IEA/AIE'2003 Proceedings of the 16th international conference on Developments in applied artificial intelligence
A Set-Covering-Based Heuristic Approach for Bin-Packing Problems
INFORMS Journal on Computing
The Co-Printing Problem: A Packing Problem with a Color Constraint
Operations Research
Selected Topics in Column Generation
Operations Research
An improved algorithm for optimal bin packing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
An Inexact Bundle Approach to Cutting-Stock Problems
INFORMS Journal on Computing
On the one-dimensional stock cutting problem in the paper tube industry
Journal of Scheduling
Solution approaches for the cutting stock problem with setup cost
Computers and Operations Research
An analytical approach for column generation for one-dimensional cutting stock problem
Proceedings of the CUBE International Information Technology Conference
New Hybrid Discrete PSO for Solving Non Convex Trim Loss Problem
International Journal of Applied Evolutionary Computation
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We compare two branch-and-price approaches for the cutting stock problem. Each algorithm is based on a different integer programming formulation of the column generation master problem. One formulation results in a master problem with 0–1 integer variables while the other has general integer variables. Both algorithms employ column generation for solving LP relaxations at each node of a branch-and-bound tree to obtain optimal integer solutions. These different formulations yield the same column generation subproblem, but require different branch-and-bound approaches. Computational results for both real and randomly generated test problems are presented.