Luis Onieva

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.

A flexible costing system for flexible manufacturing systems using activity based costing

Flexible manufacturing systems (FMSs) are designed to integrate the flexibility of job shops and the efficiency of mass production systems. Product costing methods have to adapt to this new technological environment. On one hand, the high production overhead cost of these systems requires a special attention to overhead allocation. On the other hand, the constantly changing setup configuration and production plans require a constant recalculation of overhead allocation and an a priori estimation of the expected production cost. This paper introduces the concept of flexible costing in FMSs, and proposes a method that modifies the overhead allocation based on the results of the production plan and on the simulated performance of the process. This approach is illustrated with some numerical examples.

Berth allocation planning in Seville inland port by simulation and optimisation

We study the problems associated with allocating berths for containerships in the port of Seville. It is the only inland port in Spain and it is located on the Guadalquivir River. This paper addresses the berth allocation planning problems using simulation and optimisation with Arena software. We propose a mathematical model and develop a heuristic procedure based on genetic algorithm to solve non-linear problems. Allocation planning aims to minimise the total service time for each ship and considers a first-come-first-served allocation strategy. We conduct a large amount of computational experiments which show that the proposed model improves the current berth management strategy.

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.

Modeling Freight Delivery Flows: Missing Link of Urban Transport Analysis

Urban freight transport has barely incited any modeling efforts when compared to passenger cars and public transport, which is mainly due to the lack of available data and the complexity of the delivery route patterns and the involved decision making. We present here a modeling approach consisting of a demand model followed by an entropy maximization procedure to estimate an origin-destination matrix for urban freight transport vehicles, both for business to business and home deliveries, during the morning peak hour. This approach requires relatively few data inputs in comparison with other existing models and represents an initial step toward the inclusion of freight delivery models in overall urban transport planning. The application of the model is illustrated with a case study in the city of Seville, with its efficiency tested by the validation of the results using actual traffic counts.

Estimation of Daily Vehicle Flows for Urban Freight Deliveries

Given its contribution to congestion, pollution, and energy consumption and the complex and changing characteristics of delivery routes, the modeling of urban freight transport is a difficult, highly data-demanding and often unreliable task. Extending other previous works that focused only on the morning peak hour, the authors have developed a trip generation model by using the available data to their maximum extent and adding other parameters that can be found through simple surveys. This trip generation model is then included as part of a four-stage process, with the trip distribution solved through entropy maximization and resulting in the estimation of an origin-destination matrix for freight transport in a city. The application to a case study in the city of Seville and the validation with on-street vehicle counts shows reasonably robust results and provides a simple and effective tool to analyze urban freight deliveries from a macroscopic point of view.

Cell design and loading in the presence of alternative routing

This paper deals with the design and loading of a cellular manufacturing system in the presence of alternative routing. The problem is decomposed into a cell design phase performed once and a cell loading phase performed on a recurrent basis. Two alternatives for the cell design problem are proposed: either the process plans of each part type are treated as if they were separate part types; or aggregate part types are considered. In either case, a conventional cell formation method is used to group machines. The cell loading problem is modelled as a multi-period LP formulation that determines the quantity of each part type that will follow each alternative route in each period of the planning horizon in order to minimize total transportation and holding costs while keeping both machine and cell utilizations approximately balanced.

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.