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11.
Hossam A. Zaki 《Computational Optimization and Applications》1995,4(1):23-45
State-of-the-art computational results have shown that the shortest augmenting path (SAP) methods are more efficient than other primal-dual and primal-simplex based methods for solving the linear assignment problem on uniprocessor computers. There is, however, some controversy concerning their merits when compared with Bertsekas' auction algorithm on multiprocessor computers. In this study we investigate the performance of these competing methods on the Alliant FX/8. For each method, theoretical motivation, sources of parallelism and computational results are presented. 相似文献
12.
Manufacturing cell formation using similarity coefficients and a parallel genetic TSP algorithm: Formulation and comparison 总被引:2,自引:0,他引:2
J. BalakrishnanP. D. Jog 《Mathematical and Computer Modelling》1995,21(12):61-73
Many algorithms have been proposed to form manufacturing cells from component routings. However, many of these do not have the capability of solving large problems. We propose a procedure using similarity coefficients and a parallel genetic implementation of a TSP algorithm that is capable of solving large problems of up to 1000 parts and 1000 machines. In addition, we also compare our procedure with many existing procedures using nine well-known problems from the literature.
The results show that the proposed procedure compares well with the existing procedures and should be useful to practitioners and researchers. 相似文献
13.
In apparel industry, manufacturers developed standard allowed minutes (SAMs) databases on various manufacturing operations in order to facilitate better scheduling, while effective production schedules ensure smoothness of downstream operations. As apparel manufacturing environment is fuzzy and dynamic, rigid production schedules based on SAMs become futile in the presence of any uncertainty. In this paper, a fuzzification scheme is proposed to fuzzify the static standard time so as to incorporate some uncertainties, in terms of both job-specific and human related factors, into the fabric-cutting scheduling problem. A genetic optimisation procedure is also proposed to search for fault-tolerant schedules using genetic algorithms, such that makespan and scheduling uncertainties are minimised. Two sets of real production data were collected to validate the proposed method. Experimental results indicate that the genetically optimised fault-tolerant schedules not only improve the operation performance but also minimise the scheduling risks. 相似文献
14.
Madhu V. Nayakkankuppam 《Mathematical Programming》2007,109(2-3):477-504
We describe an approach to the parallel and distributed solution of large-scale, block structured semidefinite programs using
the spectral bundle method. Various elements of this approach (such as data distribution, an implicitly restarted Lanczos
method tailored to handle block diagonal structure, a mixed polyhedral-semidefinite subdifferential model, and other aspects
related to parallelism) are combined in an implementation called LAMBDA, which delivers faster solution times than previously
possible, and acceptable parallel scalability on sufficiently large problems.
This work was supported in part by NSF grants DMS-0215373 and DMS-0238008. 相似文献
15.
In single-objective optimization it is possible to find a global optimum, while in the multi-objective case no optimal solution is clearly defined, but several that simultaneously optimize all the objectives. However, the majority of this kind of problems cannot be solved exactly as they have very large and highly complex search spaces. Recently, meta-heuristic approaches have become important tools for solving multi-objective problems encountered in industry as well as in the theoretical field. Most of these meta-heuristics use a population of solutions, and hence the runtime increases when the population size grows. An interesting way to overcome this problem is to apply parallel processing. This paper analyzes the performance of several parallel paradigms in the context of population-based multi-objective meta-heuristics. In particular, we evaluate four alternative parallelizations of the Pareto simulated annealing algorithm, in terms of quality of the solutions, and speedup. 相似文献
17.
Ronald H. Nickel Igor Mikolic-Torreira Jon W. Tolle 《Computational Optimization and Applications》2006,35(1):109-126
Deployed US Navy aircraft carriers must stock a large number of spare parts to support the various types of aircraft embarked
on the ship. The sparing policy determines the spares that will be stocked on the ship to keep the embarked aircraft ready
to fly. Given a fleet of ten or more aircraft carriers and a cost of approximately 50 million dollars per carrier plus the
cost of spares maintained in warehouses in the United States, the sparing problem constitutes a significant portion of the
Navy’s resources. The objective of this work is to find a minimum-cost sparing policy that meets the readiness requirements
of the embarked aircraft. This is a very large, nonlinear, integer optimization problem. The cost function is piecewise linear
and convex while the constraint mapping is highly nonlinear. The distinguishing characteristics of this problem from an optimization
viewpoint are that a large number of decision variables are required to be integer and that the nonlinear constraint functions
are essentially “black box” functions; that is, they are very difficult (and expensive) to evaluate and their derivatives
are not available. Moreover, they are not convex. Integer programming problems with a large number of variables are difficult
to solve in general and most successful approaches to solving nonlinear integer problems have involved linear approximation
and relaxation techniques that, because of the complexity of the constraint functions, are inappropriate for attacking this
problem. We instead employ a pattern search method to each iteration of an interior point-type algorithm to solve the relaxed
version of the problem. From the solution found by the pattern search on each interior point iteration, we begin another pattern
search on the integer lattice to find a good integer solution. The best integer solution found across all interations is returned
as the optimal solution. The pattern searches are distributed across a local area network of non-dedicated, heterogeneous
computers in an office environment, thus, drastically reducing the time required to find the solution. 相似文献
18.
Summary The RHF and geometry optimization sections of the ab initio quantum chemistry code, GAMESS, have been optimized for a network of parallel microprocessors, Inmos T800-20 transputers, using both indirect and direct SCF techniques. The results indicate great scope for implementation of such codes on small parallel computer systems, very high efficiencies having been achieved, particularly in the cases of direct SCF and geometry optimization with large basis sets.The work, although performed upon one particular parallel system, the Meiko Computing Surface, is applicable to a wide range of parallel systems with both shared and distributed memory. 相似文献
19.
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