5 research outputs found
Effective and efficient estimation of distribution algorithms for permutation and scheduling problems.
Estimation of Distribution Algorithm (EDA) is a branch of evolutionary computation that learn a probabilistic model of good solutions. Probabilistic models are used to represent relationships between solution variables which may give useful, human-understandable insights into real-world problems. Also, developing an effective PM has been shown to significantly reduce function evaluations needed to reach good solutions. This is also useful for real-world problems because their representations are often complex needing more computation to arrive at good solutions. In particular, many real-world problems are naturally represented as permutations and have expensive evaluation functions. EDAs can, however, be computationally expensive when models are too complex. There has therefore been much recent work on developing suitable EDAs for permutation representation. EDAs can now produce state-of-the-art performance on some permutation benchmark problems. However, models are still complex and computationally expensive making them hard to apply to real-world problems. This study investigates some limitations of EDAs in solving permutation and scheduling problems. The focus of this thesis is on addressing redundancies in the Random Key representation, preserving diversity in EDA, simplifying the complexity attributed to the use of multiple local improvement procedures and transferring knowledge from solving a benchmark project scheduling problem to a similar real-world problem. In this thesis, we achieve state-of-the-art performance on the Permutation Flowshop Scheduling Problem benchmarks as well as significantly reducing both the computational effort required to build the probabilistic model and the number of function evaluations. We also achieve competitive results on project scheduling benchmarks. Methods adapted for solving a real-world project scheduling problem presents significant improvements
Investigating Constraint Programming and Hybrid Methods for Real World Industrial Test Laboratory Scheduling
In this paper we deal with a complex real world scheduling problem closely
related to the well-known Resource-Constrained Project Scheduling Problem
(RCPSP). The problem concerns industrial test laboratories in which a large
number of tests has to be performed by qualified personnel using specialised
equipment, while respecting deadlines and other constraints. We present
different constraint programming models and search strategies for this problem.
Furthermore, we propose a Very Large Neighborhood Search approach based on our
CP methods. Our models are evaluated using CP solvers and a MIP solver both on
real-world test laboratory data and on a set of generated instances of
different sizes based on the real-world data. Further, we compare the exact
approaches with VLNS and a Simulated Annealing heuristic. We could find
feasible solutions for all instances and several optimal solutions and we show
that using VLNS we can improve upon the results of the other approaches
Sequenciamento de embarque de granéis sólidos por meio de terminais portuários utilizando algoritmos genéticos /
Orientador: Celso CarnieriCo-orientador: Marcos MasnikInclui bibliografia e anexosÁrea de concentraçăo: Programaçăo matemátic