10,198 research outputs found
Comparative study of different approaches to solve batch process scheduling and optimisation problems
Effective approaches are important to batch process scheduling problems, especially those with complex constraints. However, most research focus on improving optimisation techniques, and those concentrate on comparing their difference are inadequate. This study develops an optimisation model of batch process scheduling problems with complex constraints and investigates the performance of different optimisation techniques, such as Genetic Algorithm (GA) and Constraint Programming (CP). It finds that CP has a better capacity to handle batch process problems with complex constraints but it costs longer time
Project scheduling under undertainty – survey and research potentials.
The vast majority of the research efforts in project scheduling assume complete information about the scheduling problem to be solved and a static deterministic environment within which the pre-computed baseline schedule will be executed. However, in the real world, project activities are subject to considerable uncertainty, that is gradually resolved during project execution. In this survey we review the fundamental approaches for scheduling under uncertainty: reactive scheduling, stochastic project scheduling, stochastic GERT network scheduling, fuzzy project scheduling, robust (proactive) scheduling and sensitivity analysis. We discuss the potentials of these approaches for scheduling projects under uncertainty.Management; Project management; Robustness; Scheduling; Stability;
Scheduling aircraft landings - the static case
This is the publisher version of the article, obtained from the link below.In this paper, we consider the problem of scheduling aircraft (plane) landings at an airport. This problem is one of deciding a landing time for each plane such that each plane lands within a predetermined time window and that separation criteria between the landing of a plane and the landing of all successive planes are respected. We present a mixed-integer zero–one formulation of the problem for the single runway case and extend it to the multiple runway case. We strengthen the linear programming relaxations of these formulations by introducing additional constraints. Throughout, we discuss how our formulations can be used to model a number of issues (choice of objective function, precedence restrictions, restricting the number of landings in a given time period, runway workload balancing) commonly encountered in practice. The problem is solved optimally using linear programming-based tree search. We also present an effective heuristic algorithm for the problem. Computational results for both the heuristic and the optimal algorithm are presented for a number of test problems involving up to 50 planes and four runways.J.E.Beasley. would like to acknowledge the financial support of the Commonwealth Scientific and Industrial Research Organization, Australia
A Parallel Adaptive P3M code with Hierarchical Particle Reordering
We discuss the design and implementation of HYDRA_OMP a parallel
implementation of the Smoothed Particle Hydrodynamics-Adaptive P3M (SPH-AP3M)
code HYDRA. The code is designed primarily for conducting cosmological
hydrodynamic simulations and is written in Fortran77+OpenMP. A number of
optimizations for RISC processors and SMP-NUMA architectures have been
implemented, the most important optimization being hierarchical reordering of
particles within chaining cells, which greatly improves data locality thereby
removing the cache misses typically associated with linked lists. Parallel
scaling is good, with a minimum parallel scaling of 73% achieved on 32 nodes
for a variety of modern SMP architectures. We give performance data in terms of
the number of particle updates per second, which is a more useful performance
metric than raw MFlops. A basic version of the code will be made available to
the community in the near future.Comment: 34 pages, 12 figures, accepted for publication in Computer Physics
Communication
Welcome to OR&S! Where students, academics and professionals come together
In this manuscript, an overview is given of the activities done at the Operations Research and Scheduling (OR&S) research group of the faculty of Economics and Business Administration of Ghent University. Unlike the book published by [1] that gives a summary of all academic and professional activities done in the field of Project Management in collaboration with the OR&S group, the focus of the current manuscript lies on academic publications and the integration of these published results in teaching activities. An overview is given of the publications from the very beginning till today, and some of the topics that have led to publications are discussed in somewhat more detail. Moreover, it is shown how the research results have been used in the classroom to actively involve students in our research activities
Alternating Maximization: Unifying Framework for 8 Sparse PCA Formulations and Efficient Parallel Codes
Given a multivariate data set, sparse principal component analysis (SPCA)
aims to extract several linear combinations of the variables that together
explain the variance in the data as much as possible, while controlling the
number of nonzero loadings in these combinations. In this paper we consider 8
different optimization formulations for computing a single sparse loading
vector; these are obtained by combining the following factors: we employ two
norms for measuring variance (L2, L1) and two sparsity-inducing norms (L0, L1),
which are used in two different ways (constraint, penalty). Three of our
formulations, notably the one with L0 constraint and L1 variance, have not been
considered in the literature. We give a unifying reformulation which we propose
to solve via a natural alternating maximization (AM) method. We show the the AM
method is nontrivially equivalent to GPower (Journ\'{e}e et al; JMLR
11:517--553, 2010) for all our formulations. Besides this, we provide 24
efficient parallel SPCA implementations: 3 codes (multi-core, GPU and cluster)
for each of the 8 problems. Parallelism in the methods is aimed at i) speeding
up computations (our GPU code can be 100 times faster than an efficient serial
code written in C++), ii) obtaining solutions explaining more variance and iii)
dealing with big data problems (our cluster code is able to solve a 357 GB
problem in about a minute).Comment: 29 pages, 9 tables, 7 figures (the paper is accompanied by a release
of the open-source code '24am'
Squeaky Wheel Optimization
We describe a general approach to optimization which we term `Squeaky Wheel'
Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution
which is then analyzed to find the trouble spots, i.e., those elements, that,
if improved, are likely to improve the objective function score. The results of
the analysis are used to generate new priorities that determine the order in
which the greedy algorithm constructs the next solution. This
Construct/Analyze/Prioritize cycle continues until some limit is reached, or an
acceptable solution is found. SWO can be viewed as operating on two search
spaces: solutions and prioritizations. Successive solutions are only indirectly
related, via the re-prioritization that results from analyzing the prior
solution. Similarly, successive prioritizations are generated by constructing
and analyzing solutions. This `coupled search' has some interesting properties,
which we discuss. We report encouraging experimental results on two domains,
scheduling problems that arise in fiber-optic cable manufacturing, and graph
coloring problems. The fact that these domains are very different supports our
claim that SWO is a general technique for optimization
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