The study of theoretical and practical combinatorial optimization problems applied...
Combinatorial Variable-fixing and Lower-bound Techniques for the Bin Packing Problem
Approximation Algorithms for Packing and Independent Set Problems
Grant number: | 16/23552-7 |
Support Opportunities: | Regular Research Grants |
Start date: | June 01, 2017 |
End date: | November 30, 2019 |
Field of knowledge: | Physical Sciences and Mathematics - Computer Science - Theory of Computation |
Principal Investigator: | Rafael Crivellari Saliba Schouery |
Grantee: | Rafael Crivellari Saliba Schouery |
Host Institution: | Instituto de Computação (IC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil |
Associated researchers: | Eduardo Candido Xavier ; Flávio Keidi Miyazawa ; Lehilton Lelis Chaves Pedrosa |
Abstract
In this research project our goal is to contribute to the advancement of the Combinatorial Optimization field focusing, in particular, in cutting and packing problems.These problems were introduced by Kantorovich in 1939 and Brooks et al. in 1940 with applications in the industry in mind and are very common in situations where it is necessary to cut materials (as metal, glass, paper, etc) in smaller items in order to supply a specific demand or when it is necessary to load vehicles and containers. Other applications for packing and cutting problems include the scheduling of computational tasks and the dissemination of advertisements in web pages. Even though common, even simple versions of cutting and packing problems are NP-hard, that is, there is no algorithm for those problems which finds optimal solutions in polynomial time unless P=NP. Thus, in light of the practical importance of those problems, it is necessary to solve cutting and packing problems within an acceptable time limit, since that, in practice, it is very common that we have an urgency in the computation of the solution. Due to the complexity of the problems considered in this project, it is imperative to propose new heuristics, exact and approximation algorithms and to adapt techniques already utilized with success in other problems in order to obtain new baselines in the resolution of such problems. (AU)
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