20,280 research outputs found
Non-linear great deluge with learning mechanism for solving the course timetabling problem
International audienc
High-Level Object Oriented Genetic Programming in Logistic Warehouse Optimization
DisertaÄnĂ prĂĄce je zamÄĹena na optimalizaci prĹŻbÄhu pracovnĂch operacĂ v logistickĂ˝ch skladech a distribuÄnĂch centrech. HlavnĂm cĂlem je optimalizovat procesy plĂĄnovĂĄnĂ, rozvrhovĂĄnĂ a odbavovĂĄnĂ. JelikoĹž jde o problĂŠm patĹĂcĂ do tĹĂdy sloĹžitosti NP-teĹžkĂ˝, je vĂ˝poÄetnÄ velmi nĂĄroÄnĂŠ nalĂŠzt optimĂĄlnĂ ĹeĹĄenĂ. MotivacĂ pro ĹeĹĄenĂ tĂŠto prĂĄce je vyplnÄnĂ pomyslnĂŠ mezery mezi metodami zkoumanĂ˝mi na vÄdeckĂŠ a akademickĂŠ pĹŻdÄ a metodami pouĹžĂvanĂ˝mi v produkÄnĂch komerÄnĂch prostĹedĂch. JĂĄdro optimalizaÄnĂho algoritmu je zaloĹženo na zĂĄkladÄ genetickĂŠho programovĂĄnĂ ĹĂzenĂŠho bezkontextovou gramatikou. HlavnĂm pĹĂnosem tĂŠto prĂĄce je a) navrhnout novĂ˝ optimalizaÄnĂ algoritmus, kterĂ˝ respektuje nĂĄsledujĂcĂ optimalizaÄnĂ podmĂnky: celkovĂ˝ Äas zpracovĂĄnĂ, vyuĹžitĂ zdrojĹŻ, a zahlcenĂ skladovĂ˝ch uliÄek, kterĂŠ mĹŻĹže nastat bÄhem zpracovĂĄnĂ ĂşkolĹŻ, b) analyzovat historickĂĄ data z provozu skladu a vyvinout sadu testovacĂch pĹĂkladĹŻ, kterĂŠ mohou slouĹžit jako referenÄnĂ vĂ˝sledky pro dalĹĄĂ vĂ˝zkum, a dĂĄle c) pokusit se pĹedÄit stanovenĂŠ referenÄnĂ vĂ˝sledky dosaĹženĂŠ kvalifikovanĂ˝m a trĂŠnovanĂ˝m operaÄnĂm manaĹžerem jednoho z nejvÄtĹĄĂch skladĹŻ ve stĹednĂ EvropÄ.This work is focused on the work-flow optimization in logistic warehouses and distribution centers. The main aim is to optimize process planning, scheduling, and dispatching. The problem is quite accented in recent years. The problem is of NP hard class of problems and where is very computationally demanding to find an optimal solution. The main motivation for solving this problem is to fill the gap between the new optimization methods developed by researchers in academic world and the methods used in business world. The core of the optimization algorithm is built on the genetic programming driven by the context-free grammar. The main contribution of the thesis is a) to propose a new optimization algorithm which respects the makespan, the utilization, and the congestions of aisles which may occur, b) to analyze historical operational data from warehouse and to develop the set of benchmarks which could serve as the reference baseline results for further research, and c) to try outperform the baseline results set by the skilled and trained operational manager of the one of the biggest warehouses in the middle Europe.
An efficient memetic, permutation-based evolutionary algorithm for real-world train timetabling
Train timetabling is a difficult and very tightly constrained combinatorial
problem that deals with the construction of train schedules. We focus on the
particular problem of local reconstruction of the schedule following a small
perturbation, seeking minimisation of the total accumulated delay by adapting
times of departure and arrival for each train and allocation of resources
(tracks, routing nodes, etc.). We describe a permutation-based evolutionary
algorithm that relies on a semi-greedy heuristic to gradually reconstruct the
schedule by inserting trains one after the other following the permutation.
This algorithm can be hybridised with ILOG commercial MIP programming tool
CPLEX in a coarse-grained manner: the evolutionary part is used to quickly
obtain a good but suboptimal solution and this intermediate solution is refined
using CPLEX. Experimental results are presented on a large real-world case
involving more than one million variables and 2 million constraints. Results
are surprisingly good as the evolutionary algorithm, alone or hybridised,
produces excellent solutions much faster than CPLEX alone
A memetic algorithm for the university course timetabling problem
This article is posted here with permission from IEEE - Copyright @ 2008 IEEEThe design of course timetables for academic institutions is a very hectic job due to the exponential number of possible feasible timetables with respect to the problem size. This process involves lots of constraints that must be respected and a huge search space to be explored, even if the size of the problem input is not significantly large. On the other hand, the problem itself does not have a widely approved definition, since different institutions face different variations of the problem. This paper presents a memetic algorithm that integrates two local search methods into the genetic algorithm for solving the university course timetabling problem (UCTP). These two local search methods use their exploitive search ability to improve the explorative search ability of genetic algorithms. The experimental results indicate that the proposed memetic algorithm is efficient for solving the UCTP
Design and Analysis of an Estimation of Distribution Approximation Algorithm for Single Machine Scheduling in Uncertain Environments
In the current work we introduce a novel estimation of distribution algorithm
to tackle a hard combinatorial optimization problem, namely the single-machine
scheduling problem, with uncertain delivery times. The majority of the existing
research coping with optimization problems in uncertain environment aims at
finding a single sufficiently robust solution so that random noise and
unpredictable circumstances would have the least possible detrimental effect on
the quality of the solution. The measures of robustness are usually based on
various kinds of empirically designed averaging techniques. In contrast to the
previous work, our algorithm aims at finding a collection of robust schedules
that allow for a more informative decision making. The notion of robustness is
measured quantitatively in terms of the classical mathematical notion of a norm
on a vector space. We provide a theoretical insight into the relationship
between the properties of the probability distribution over the uncertain
delivery times and the robustness quality of the schedules produced by the
algorithm after a polynomial runtime in terms of approximation ratios
A hybrid genetic algorithm and tabu search approach for post enrolment course timetabling
Copyright @ Springer Science + Business Media. All rights reserved.The post enrolment course timetabling problem (PECTP) is one type of university course timetabling problems, in which a set of events has to be scheduled in time slots and located in suitable rooms according to the student enrolment data. The PECTP is an NP-hard combinatorial optimisation problem and hence is very difficult to solve to optimality. This paper proposes a hybrid approach to solve the PECTP in two phases. In the first phase, a guided search genetic algorithm is applied to solve the PECTP. This guided search genetic algorithm, integrates a guided search strategy and some local search techniques, where the guided search strategy uses a data structure that stores useful information extracted from previous good individuals to guide the generation of offspring into the population and the local search techniques are used to improve the quality of individuals. In the second phase, a tabu search heuristic is further used on the best solution obtained by the first phase to improve the optimality of the solution if possible. The proposed hybrid approach is tested on a set of benchmark PECTPs taken from the international timetabling competition in comparison with a set of state-of-the-art methods from the literature. The experimental results show that the proposed hybrid approach is able to produce promising results for the test PECTPs.This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01 and Grant EP/E060722/02
Evolutionary design of a fullâenvelope flight control system for an unstable fighter aircraft
The use of an evolutionary algorithm in the framework of Hâ control theory is being considered as a means for synthesizing controller gains that minimize a weighted combination of the infinite-norm of the sensitivity function (for disturbance attenuation requirements) and complementary sensitivity function (for robust stability requirements) at the same time. The case study deals with the stability and control augmentation of an unstable high-performance jet aircraft. Constraints on closed-loop response are also enforced, that represent typical requirements on airplane handling qualities, that makes the control law synthesis process more demanding. Gain scheduling is required, in order to obtain satisfactory performance over the whole flight envelope, so that the synthesis is performed at different reference trim conditions, for several values of the dynamic pressure, Q, used as the scheduling parameter. Nonetheless, the dynamic behaviour of the aircraft may exhibit significant variations when flying at different altitudes h, even for the same value of the dynamic pressure, so that a trade-off is required between different feasible controllers synthesized for a given value of Q, but different h. A multi-objective search is thus considered for the determination of the best suited solution to be introduced in the scheduling of the control law. The obtained results are then tested on a longitudinal nonlinear model of the aircraft
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