Juan Larrañeta

Genetic algorithm for controllers in elevator groups: analysis and simulation during lunchpeak traffic

Abstract A genetic algorithm (GAHCA) is proposed to control elevator groups of professional buildings. The genetic algorithm is compared with the universal controller algorithm in industry applications. In order to do so an ARENA simulation scenario has been generated during heavy lunchpeak traffic conditions. The results allow us to affirm that our genetic algorithm reaches a better performance attending to the system waiting times than traditional duplex algorithms.

Kohonen maps for solving a class of location-allocation problems

Location-Allocation problems occur whenever more than one facility need be located to serve a set of demand centers and it is not known or fixed a priori their allocation to the supply centers. This paper deals with a continuous space problem in which demand centers are independently served from a given number of independent, uncapacitated supply centers. Installation costs are assumed not to depend on neither the actual location nor the actual throughput of the supply centers. Transportation costs are considered to be proportional to the square Euclidean distance travelled and a minisum criterium is adopted. The problem is recognized as identical to certain Cluster Analysis and Vector Quantization problems. Such a relationship leads to applying Kohonen Maps, which are Artificial Neural Networks capable of extracting the main features, i.e. the structure, of the input data through a self-organizing process based on local adaptation rules. This approach has previously been applied to other combinatorial problems such as the Travelling Salesperson Problem.

Primal-dual approach to the single level capacitated lot-sizing problem

Abstract The Lagrangean relaxation of the single level capacitated dynamic lot-sizing problem can be solved using the primal-dual method. The algorithm has monotone and finite convergence properties. It works as a steepest ascent method. A variant of this approach is also studied. A heuristic routine used to obtain a feasible solution in each iteration is presented. Computational experiences show that this method usually yields better solutions than the subgradient method although it requires greater CPU times.

Modified fuzzy C-means algorithm for cellular manufacturing

Several studies have used the fuzzy C-means (FCM) algorithm for part-machine grouping in cellular manufacturing. However, the application of the standard FCM algorithm to this problem has a number of drawbacks. This paper proposes a modified FCM (MFCM) algorithm that groups components and machines in parallel and through an annealing process with the weighting exponent arrives at a crisp solution and an objective function value which can be interpreted in terms of the number of voids and intercellular movements of the part-machine grouping obtained. Computational experiences show that although MFCM may sometimes require slightly more computing time than other methods, not only is it able to find better solutions but also it has higher discriminating power for determining the number of cells.

A Genetic Algorithm for Controlling Elevator Group Systems

The efficient performance of elevator group system controllers becomes a first order necessity when the buildings have a high utilisation ratio of the elevators, such as in professional buildings. We present a genetic algorithm that is compared with traditional controller algorithms in industry applications. An ARENA simulation scenario is created during heavy lunchpeak traffic conditions. The results allow us to affirm that our genetic algorithm reaches a better performance attending to the system waiting times than THV algorithm.

Cell formation using a Fuzzy Min-Max neural network

This paper proposes the application of a Fuzzy Min-Max neural network for part family formation in a cellular manufacturing environment. Once part families have been formed, a minimum cost flow model is used to form the corresponding machine cells. For simplicity, the input data are in the form of a binary part- machine incidence matrix, although the algorithm can work with an incidence matrix with continuous values. The application of Fuzzy Min-Max is interpreted in physical terms and compared with a related neural network applied previously for cell formation, the Fuzzy ART network. Both neural networks have similarities and differences that are outlined. The algorithms have been programmed and applied to a large set of problems from the literature. Fuzzy Min-Max generally outperforms Fuzzy ART, and the computational times are small and similar in both algorithms.

Genetic algorithm for planning cable telecommunication networks

Abstract Optical fiber networks over synchronous digital hierarchy operating on ATM mode appear as one of the best alternatives for the new high-speed telecommunications networks. The network management requires planning studies so that the designed networks allow efficient operation in real time. A quantitative model evaluating the global topological design of an optical fiber network over synchronous digital hierarchy is described. The model includes the economic aspects involved in the project, the civil works, the capacity evaluation costs and the introduction of reliability conditions. A genetic approach based on the exploitation of the Kuhn–Tucker optimality conditions is proposed to solve the model.

Estimation of an origin-destination matrix for urban freight transport. Application to the city of Seville

This chapter presents the process followed to estimate an origin-destination matrix for freight transport in the city of Seville. The focus of the paper lies specially on the data acquisition process, which is normally one of the main obstacles to be faced by this type of analysis. The model used, based on entropy maximization, and the solution algorithm, including Frank-Wolfe’s linear approximations, are equally described. Results are presented graphically and then compared with vehicle flows observed from reality, allowing to extract conclusions for the validation of the model.

Decision support system for planning telecommunication networks: a case study applied to the Andalusian region

Network planning is essential to design real broadband integrated services digital networks (B-ISDN). This paper presents an operations research application to the design of an optic fibre network for the Andalusian region. The economical appraisal is the main consideration in order to take the appropriate decisions: hub location, region sizes and selection of the urban nodes that will receive telecommunication contents. A decision support system with a graphic interface that allows interactive analysis of different scenarios is presented. The system contains a set of mathematical programming models and it has the capability to dynamically construct and solve instances of those models. In addition, it provides data preparation and reports. The system is an integrated, user-friendly and powerful tool to make planning studies by firms developing cable network systems in the telecommunications market.

Examination of the sensitivity of an operation schedule with perturbation analysis

Abstract An algorithm is presented to calculate the validity range of deterministic similarity of a sample path of a discrete event dynamic system when a single perturbation is introduced at the operation times. Based on the calculated range, sensitivity analysis concerning both the gradient of the throughput and some special technological feasibility of an operation schedule can be analysed. The algorithm is recommended in a manufacturing environment, when technology intensive products are produced in small lots. The implementation of the algorithm in an automated steel manufacturing process is also presented.