Design and analysis of a tool for planning and simulating dynamic vertical transport

Nowadays, most of the main companies in the vertical transport industry are researching tools capable of providing support for the design process of elevator systems. Numerous decisions have to be taken to obtain an accurate, comfortable, and high-quality service. Effectively, the optimization algorithm is a key factor in the design process, but so are the number of cars being installed, their technical characteristics, the kinematics of the elevator group, and some other design parameters, which cause the selection task of the elevator system to be a complex one. In this context, the design of decision support tools is becoming a real necessity that most important companies are including as part of their strategic plans. In this article, the authors present a user-friendly planning and simulating tool for dynamic vertical traffic. The tool is conceptualized for giving support in the planning and design stage of the elevator system, in order to collaborate in the selection of the type of elevator (number, type of dynamic, capacity, etc.) and the optimization algorithm

Algoritmos de optimización en sistemas de transporte vertical

El encarecimiento del suelo, especialmente en los centros de las grandes ciudades conduce a su mayor aprovechamiento mediante la construcción de elevados edificios. En estas situaciones el análisis de los sistemas de transporte vertical se convierte en una necesidad, especialmente en los grandes edificios destinados a oficinas de profesionales. En los sistemas de transporte vertical concurren diferentes objetivos, desde el nivel de servicio ofrecido al usuario, hasta la optimización del consumo energético o la capacidad manejada por el sistema. Así son necesarias técnicas flexibles que satisfagan la mayor parte de los requerimientos deseados en función de cada tipo de edificio

Analysis of an elevator system using discrete event simulation: case study

This paper documents the work conducted to simulate an elevator system, using SIMIO software. The modelled system represents a case study that was analyzed in a hospital at Braga, Portugal. A previ-ous work on the same case study concluded that the best dwell time configuration would be around 10 seconds, however it did not consider the impact of different client demand on the elevator system. In this sense, this paper analyses the impact of both parameters on the performance of the system. This will be achieved by analyzing the impact on the total time spent by clients in the system, the number of clients inside the system, and waiting for the elevator, waiting time, average elevator occupation and number of elevator movements. Conclusions and future work agenda were discussed in the conclusions section.This work has been supported by FCT -Fundacao para a Ciencia e Tecnologia in the scope of the project: PEstOE/EEI/UI0319/2014 (ALGORITMI)

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

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

Discrete Event Simulation of Elevator Systems

The intent of this paper is to present the reader with a simple comparison of two systems of vertical transportation. Vertical transportation is a a relatively new field and is the subject of much interest in today\u27s world. As buildings get taller and real estate becomes more expensive, the need to find a quick, efficient system with a small footprint becomes important. By performing a simulation and subjecting the two systems under study to similar traffic conditions, one can determine the effectiveness of one system relative to the other. Additionally, we look at the effects of changing various system attributes to gain a better understanding of the primary drivers of average travel time

Ascensors

Minimitzar el temps d'espera de l'usuari que vol utilitzar un ascensor a l'ETSEIB. Dissenyar un cicle de treball per els ascensors

A Universal Methodology for Generating Elevator Passenger Origin-Destination Pairs for Calculation and Simulation

The origin-destination matrix is a two-dimensional matrix that describes the probability of a passenger travelling from one floor in the building to another. It is a two-dimensional square matrix. The row index denotes the origin floor and the row index denotes the destination floor for the passenger journey. A previous chapter described the methodology for constructing the origin-destination matrix (OD matrix) from the user requirements. However, that chapter placed the restriction that any floor must either be assigned as an entrance floor or an occupant floor, but not both. This chapter relaxes this restriction and shows a method for developing the origin-destination matrix that allows any floor to either be an entrance floor; an occupant floor; or both. The origin destination matrix can be compiled using three sets of parameters: the mix of traffic (incoming traffic, outgoing traffic, inter-floor traffic; and inter-entrance traffic); the floor populations; and the entrance percentage bias (i.e., the relative strength of the arrivals at the entrance floors). The origin-destination matrix can be used for the generation of random passenger origin-destination pairs (which is necessary when using the Monte Carlo Simulation (MCS) method to calculate the round-trip time or in elevator traffic software)

Optimal Planning of Container Terminal Operations

Due to globalization and international trade, moving goods using a mixture of transportation modes has become a norm; today, large vessels transport 95% of the international cargos. In the first part of this thesis, the emphasis is on the sea-land intermodal transport. The availability of different modes of transportation (rail/road/direct) in sea-land intermodal transport and container flows (import, export, transhipment) through the terminal are considered simultaneously within a given planning time horizon. We have also formulated this problem as an Integer Programming (IP) model and the objective is to minimise storage cost, loading and transportation cost from/to the customers. To further understand the computational complexity and performance of the model, we have randomly generated a large number of test instances for extensive experimentation of the algorithm. Since, CPLEX was unable to find the optimal solution for the large test problems; a heuristic algorithm has been devised based on the original IP model to find near „optimal‟ solutions with a relative error of less than 4%. Furthermore, we developed and implemented Lagrangian Relaxation (LR) of the IP formulation of the original problem. The bounds derived from LR were improved using sub-gradient optimisation and computational results are presented. In the second part of the thesis, we consider the combined problems of container assignment and yard crane (YC) deployment within the container terminal. A new IP formulation has been developed using a unified approach with the view to determining optimal container flows and YC requirements within a given planning time horizon. We designed a Branch and Cut (B&C) algorithm to solve the problem to optimality which was computationally evaluated. A novel heuristic approach based on the IP formulation was developed and implemented in C++. Detailed computational results are reported for both the exact and heuristic algorithms using a large number of randomly generated test problems. A practical application of the proposed model in the context of a real case-study is also presented. Finally, a simulation model of container terminal operations based on discrete-event simulation has been developed and implemented with the view of validating the above optimisation model and using it as a test bed for evaluating different operational scenarios