Approximation algorithms for bin packing: a survey
Approximation algorithms for NP-hard problems
Approximation algorithms
Tight bounds for online class-constrained packing
Theoretical Computer Science - Latin American theorotical informatics
Approximation schemes for knapsack problems with shelf divisions
Theoretical Computer Science
The constrained compartmentalised knapsack problem
Computers and Operations Research
The class constrained bin packing problem with applications to video-on-demand
Theoretical Computer Science
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Given bins of size B, non-negative values d and @D, and a list L of items, each item e@?L with size s"e and class c"e, we define a shelf as a subset of items packed inside a bin with total item sizes at most @D such that all items in this shelf have the same class. Two subsequent shelves must be separated by a shelf division of size d. The size of a shelf is the total size of its items plus the size of the shelf division. The class constrained shelf bin packing problem (CCSBP) is to pack the items of L into the minimum number of bins, such that the items are divided into shelves and the total size of the shelves in a bin is at most B. We present hybrid algorithms based on the First Fit (Decreasing) and Best Fit (Decreasing) algorithms, and an APTAS for the problem CCSBP when the number of different classes is bounded by a constant C.