BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem
Computers and Operations Research
Computers and Operations Research
Record breaking optimization results using the ruin and recreate principle
Journal of Computational Physics
Bin-Packing with Fragile Objects
TCS '02 Proceedings of the IFIP 17th World Computer Congress - TC1 Stream / 2nd IFIP International Conference on Theoretical Computer Science: Foundations of Information Technology in the Era of Networking and Mobile Computing
Dynamic Programming and Strong Bounds for the 0-1 Knapsack Problem
Management Science
Heuristics and lower bounds for the bin packing problem with conflicts
Computers and Operations Research
Using Extra Dual Cuts to Accelerate Column Generation
INFORMS Journal on Computing
Special issue on mathematical contributions to metaheuristics editorial
Journal of Heuristics
An Optimization Algorithm for the Ordered Open-End Bin-Packing Problem
Operations Research
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Approximation algorithms for combinatorial problems
Journal of Computer and System Sciences
Algorithms for the Bin Packing Problem with Conflicts
INFORMS Journal on Computing
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We are given a set of items, each characterized by a weight and a fragility, and a large number of uncapacitated bins. Our aim is to find the minimum number of bins needed to pack all items, in such a way that in each bin the sum of the item weights is less than or equal to the smallest fragility of an item in the bin. The problem is known in the literature as the Bin Packing Problem with Fragile Objects, and appears in the telecommunication field, when one has to assign cellular calls to available channels by ensuring that the total noise in a channel does not exceed the noise acceptance limit of a call. We propose several techniques to compute lower and upper bounds for this problem. For what concerns lower bounds, we present combinatorial techniques with guaranteed worst case and a more complex bound based on a column generation algorithm. We also present a technique to compute, in a fast heuristic way, dual information that is used to strengthen the convergence of the column generation. For what concerns upper bounds, we present a large set of constructive heuristics followed by a Variable Neighborhood Search algorithm. Our heuristic techniques are aimed at both computing upper bounds and strengthening the behavior of the lower bounds in a matheuristic fashion. Extensive computational tests show the effectiveness of the proposed algorithms.