Optimal bin packing with items of random sizes III
SIAM Journal on Computing
Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
On line bin packing with items of random size
Mathematics of Operations Research
A Heuristic Algorithm for the Auto-Carrier Transportation Problem
Transportation Science
Stochastic Load Balancing and Related Problems
FOCS '99 Proceedings of the 40th Annual Symposium on Foundations of Computer Science
Bin packing with items uniformly distributed over intervals [a,b]
SFCS '83 Proceedings of the 24th Annual Symposium on Foundations of Computer Science
Efficient lower bounds and heuristics for the variable cost and size bin packing problem
Computers and Operations Research
| Hi-index | 0.04 |
The Generalized Bin Packing Problem (GBPP) is a recently introduced packing problem where, given a set of bins characterized by volume and cost and a set of items characterized by volume and profit (which also depends on bins), we want to select a subset of items to be loaded into a subset of bins which maximizes the total net profit, while satisfying the volume and bin availability constraints. The total net profit is given by the difference between the total profit of the loaded items and the total cost of the used bins. In this paper, we consider the stochastic version of the GBPP (S-GBPP), where the item profits are random variables to take into account the profit oscillations due to the handling operations for bin loading. The probability distribution of these random variables is assumed to be unknown. By using the asymptotic theory of extreme values a deterministic approximation for the S-GBPP is derived.