A branch-and-bound algorithm for the two-dimensional vector packing problem
Computers and Operations Research
Lower bounds and algorithms for the 2-dimensional vector packing problem
Discrete Applied Mathematics
Heuristic Algorithms and Scatter Search for the Cardinality Constrained P|Cmax Problem
Journal of Heuristics
A two-dimensional vector packing model for the efficient use of coil cassettes
Computers and Operations Research
EMO '09 Proceedings of the 5th International Conference on Evolutionary Multi-Criterion Optimization
Metaheuristics: From Design to Implementation
Metaheuristics: From Design to Implementation
Tree-decomposition based heuristics for the two-dimensional bin packing problem with conflicts
Computers and Operations Research
The min-conflict packing problem
Computers and Operations Research
A multi-objective genetic local search algorithm and itsapplication to flowshop scheduling
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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In this paper, we address a bi-objective 2-dimensional vector packing problem (Mo2-DBPP) that calls for packing a set of items, each having two sizes in two independent dimensions, say, a weight and a height, into the minimum number of bins. The weight corresponds to a ''hard'' constraint that cannot be violated while the height is a ''soft'' constraint. The objective is to find a trade-off between the number of bins and the maximum height of a bin. This problem has various real-world applications (computer science, production planning and logistics). Based on the special structure of its Pareto front, we propose two iterative resolution approaches for solving the Mo2-DBPP. In each approach, we use several lower bounds, heuristics and metaheuristics. Computational experiments are performed on benchmarks inspired from the literature to compare the effectiveness of the two approaches.