Large Scale Optimization

Purpose -The purpose of this paper is to compare the effectiveness of two meta-heuristics in solving the problem of scheduling maintenance operations and jobs processing on a single machine. Design/methodology/approach -The two meta-heuristic algorithms, tabu search and simulated annealing are hybridized using the properties of an optimal schedule identified in the existing literature to the problem. A lower bound is also suggested utilizing these properties. Finding -In a numerical experimentation with large size problems, the best-known heuristic algorithm to the problem is compared with the tabu search and simulated annealing algorithms. The study shows that the meta-heuristic algorithms outperform the heuristic algorithm. In addition, the developed meta-heuristics tend to be more robust against the problem-related parameters than the existing algorithm. Research limitations/implications -A future work may consider the possibility of machine failure along with the preventive maintenance. This relaxes the assumption that the machine cannot fail but it is rather maintained preventively. The multi-criteria scheduling can also be considered as an avenue of future work. The problem can also be considered with stochastic parameters such that the processing times of the jobs and the maintenance related parameters are random and follow a known probability distribution function. Practical implications -The usefulness of meta-heuristic algorithms is demonstrated for solving a large scale NP-hard combinatorial optimization problem. The paper also shows that the utilization of the directed search methods such as hybridization could substantially improve the performance of a meta-heuristic. Originality/value -This research highlights the impact of utilizing the directed search methods to cause hybridization in meta-heuristic and the resulting improvement in their performance for large-scale optimization.

... M., Turner, A., Watanabe, K., and Ziemba, WT (1994), "The RussellYasuda Kasai model: An asset liability model for a Japanese insurance company using ... Dahl, H., Meerus, A., and Zenios, SA (1993), "Some financial optimization models: Risk management and financial ...

Matching Techniques are becoming a very attractive research topic. With the development and the use of a large variety of data (e.g. DB schemas, ontologies, taxonomies), in many domains (e.g. libraries, life science, etc), Matching Techniques are called to overcome the challenge of aligning and reconciling these different interrelated representations. In this paper, we are interested in studying large scale matching approaches. We define a quality of Matching (QoM) that can be used to evaluate large scale Matching systems. We survey the techniques of large scale matching, when a large number of schemas/ontologies and attributes are involved. We attempt to cover a variety of techniques for schema matching called Pair-wise and Holistic, as well as a set of useful optimization techniques. One can acknowledge that this domain is on top of effervescence and large scale matching need much more advances. So, we propose a contribution that deals with the creation of a hybrid approach that combines these techniques.

Optimization of multi-reservoir systems operations is typically a very large scale optimization problem. The following are the three types of optimization problems solved using linear programming (LP) : (i) deterministic optimization for multiple periods involving fine stage intervals, for example, from an hour to a week (ii) implicit stochastic optimization using multiple years of inflow data, and (iii) explicit stochastic optimization using probability distributions of inflow data. Until recently, the revised simplex method has been the most efficient solution method available for solving large scale LP problems. In this paper, we show that an implementation of the Karmarkar's interior-point LP algorithm with a newly developed stopping criterion solves optimization problems of large multi-reservoir operations more efficiently than the simplex method. For example, using a MicroVAX II minicomputer, a 40 year, monthly stage, two-reservoir system optimization problem is solved 7.8 times faster than the advanced simplex code in MINOS 5.0. The advantage of this method is expected to be greater as the size of the problem grows from two reservoirs to multiples of reservoirs. This paper presents the details of the implementation and testing and in addition, some other features of the Karmarkar's algorithm which makes it a valuable optimization tool are illuminated.

We propose a novel approach to solving the electro-/ magnetoencephalographic (EEG / MEG) inverse problem which is based upon a decomposition of the current density into a small number of spatial basis fields. It is designed to recover multiple sources of possibly different extent and depth, while being invariant with respect to phase angles and rotations of the coordinate system. We demonstrate the method's ability to reconstruct simulated sources of random shape and show that the accuracy of the recovered sources can be increased, when interrelated field patterns are co-localized. Technically, this leads to large-scale mathematical problems, which are solved using recent advances in convex optimization. We apply our method for localizing brain areas involved in different types of motor imagery using real data from Brain-Computer Interface (BCI) sessions. Our approach based on single-trial localization of complex Fourier coefficients yields class-specific focal sources in the sensorimotor cortices.

465 k Wh tonne À1 CO 2 . The approach presented is, therefore, very promising and quite useful for evaluating the suitability of different adsorbents, feedstocks, and operating strategies for PSA, and assessing its usefulness for CO 2 capture. Figure 3. Realization of 4-step Skarstrom cycle from the superstructure.

SUMMARY The standard formulations of the Kalman fllter (KF) and extended Kalman fllter (EKF) require storing and multiplication of matrices of size n£n, where n is the size of the state space, and the inversion of matrices of size m £ m, where m is the size of the observation space. For large dimensions implementation issues arise. In this paper