Innovative Interior Designs for Urban Freight Distribution Using Light Rail Systems

High pollution levels and congestion in urban centres are an increasing concern for local councils in the UK. Heavy goods vehicles delivering urban freight to city centres are a leading cause of this problem. This paper discusses the concept of using light rail networks to deliver freight to city centres from surrounding businesses. Specifically various innovative designs are considered for the interior of the metro carriage and developed into visual models using Autodesk Inventor software. A full evaluation of all the designs developed is completed, resulting in a proposed design for consideration. The conclusion reached is that the proposed interior design is viable and coincides with the future metro fleet designs and concepts

Implementing Parallel Differential Evolution on Spark

[Abstract] Metaheuristics are gaining increased attention as an efficient way of solving hard global optimization problems. Differential Evolution (DE) is one of the most popular algorithms in that class. However, its application to realistic problems results in excessive computation times. Therefore, several parallel DE schemes have been proposed, most of them focused on traditional parallel programming interfaces and infrastruc- tures. However, with the emergence of Cloud Computing, new program- ming models, like Spark, have appeared to suit with large-scale data processing on clouds. In this paper we investigate the applicability of Spark to develop parallel DE schemes to be executed in a distributed environment. Both the master-slave and the island-based DE schemes usually found in the literature have been implemented using Spark. The speedup and efficiency of all the implementations were evaluated on the Amazon Web Services (AWS) public cloud, concluding that the island- based solution is the best suited to the distributed nature of Spark. It achieves a good speedup versus the serial implementation, and shows a decent scalability when the number of nodes grows.[Resumen] Las metaheurísticas están recibiendo una atención creciente como técnica eficiente en la resolución de problemas difíciles de optimización global. Differential Evolution (DE) es una de las metaheurísticas más populares, sin embargo su aplicación en problemas reales deriva en tiempos de cómputo excesivos. Por ello se han realizado diferentes propuestas para la paralelización del DE, en su mayoría utilizando infraestructuras e interfaces de programación paralela tradicionales. Con la aparición de la computación en la nube también se han propuesto nuevos modelos de programación, como Spark, que permiten manejar el procesamiento de datos a gran escala en la nube. En este artículo investigamos la aplicabilidad de Spark en el desarrollo de implementaciones paralelas del DE para su ejecución en entornos distribuidos. Se han implementado tanto la aproximación master-slave como la basada en islas, que son las más comunes. También se han evaluado la aceleración y la eficiencia de todas las implementaciones usando el cloud público de Amazon (AWS, Amazon Web Services), concluyéndose que la implementación basada en islas es la más adecuada para el esquema de distribución usado por Spark. Esta implementación obtiene una buena aceleración en relación a la implementación serie y muestra una escalabilidad bastante buena cuando el número de nodos aumenta.[Resume] As metaheurísticas están recibindo unha atención a cada vez maior como técnica eficiente na resolución de problemas difíciles de optimización global. Differential Evolution (DE) é unha das metaheurísticas mais populares, ainda que a sua aplicación a problemas reais deriva en tempos de cómputo excesivos. É por iso que se propuxeron diferentes esquemas para a paralelización do DE, na sua maioría utilizando infraestruturas e interfaces de programación paralela tradicionais. Coa aparición da computación na nube tamén se propuxeron novos modelos de programación, como Spark, que permiten manexar o procesamento de datos a grande escala na nube. Neste artigo investigamos a aplicabilidade de Spark no desenvolvimento de implementacións paralelas do DE para a sua execución en contornas distribuidas. Implementáronse tanto a aproximación master-slave como a baseada en illas, que son as mais comúns. Tamén se avaliaron a aceleración e a eficiencia de todas as implementacións usando o cloud público de Amazon (AWS, Amazon Web Services), tirando como conclusión que a implementación baseada en illas é a mais acaida para o esquema de distribución usado por Spark. Esta implementación obtén unha boa aceleración en relación á implementación serie e amosa unha escalabilidade bastante boa cando o número de nos aumenta.Ministerio de Economía y Competitividad; DPI2014-55276-C5-2-RXunta de Galicia; GRC2013/055Xunta de Galicia; R2014/04

Towards a European ITS for freight transport and logistics: results of current EU funded research and prospects for the future

Objective The purpose of this paper is to review the developments of the last 15 years in the field of Freight Transport research and innovation, primarily (but not exclusively) in Europe focusing on the “Intelligent Transport Systems” aspects. Document type: Articl

Model of optimization of mining complex for the planning of flow of quarry production of limestone in multiple products and with elements for the analysis of the capacity

Activities in mining complexes contain multiple decisions that affect the operations of the system for the extraction, transformation, transport and storage of various subsoil components. The purpose of this research is the planning of continuous flow production systems for mixed products, in non-metallic mining extraction processes, considering bottlenecks and capacity planning. This paper presents a model for production, based on mathematical optimization, that facilitates the planning and management of operations in the area of extraction, crushing and transformation of a quarry of aggregates for construction, considering the resources and the constraints that allow to define effective strategies in the increase of the productivity of the lines of low production environment by scenarios. This research develops an analysis of bottlenecks and contrasts the nature of the production system by means of a mathematical model of optimization, which considers the capacities and balances in the flows of the Limestone production line. The mathematical model that maximizes profits can be adapted to systems of continuous flow production in mining complexes where their products are part of a reverse logistics process, analysis of alternatives of extraction, transformation and transport

A biologically inspired network design model

A network design problem is to select a subset of links in a transport network that satisfy passengers or cargo transportation demands while minimizing the overall costs of the transportation. We propose a mathematical model of the foraging behaviour of slime mould P. polycephalum to solve the network design problem and construct optimal transport networks. In our algorithm, a traffic flow between any two cities is estimated using a gravity model. The flow is imitated by the model of the slime mould. The algorithm model converges to a steady state, which represents a solution of the problem. We validate our approach on examples of major transport networks in Mexico and China. By comparing networks developed in our approach with the man-made highways, networks developed by the slime mould, and a cellular automata model inspired by slime mould, we demonstrate the flexibility and efficiency of our approach

Human performance and strategies while solving an aircraft routing and sequencing problem: an experimental approach

As airport resources are stretched to meet increasing demand for services, effective use of ground infrastructure is increasingly critical for ensuring operational efficiency. Work in operations research has produced algorithms providing airport tower controllers with guidance on optimal timings and sequences for flight arrivals, departures, and ground movement. While such decision support systems have the potential to improve operational efficiency, they may also affect users’ mental workload, situation awareness, and task performance. This work sought to identify performance outcomes and strategies employed by human decision makers during an experimental airport ground movement control task with the goal of identifying opportunities for enhancing user-centered tower control decision support systems. To address this challenge, thirty novice participants solved a set of vehicle routing problems presented in the format of a game representing the airport ground movement task practiced by runway controllers. The games varied across two independent variables, network map layout (representing task complexity) and gameplay objective (representing task flexibility), and verbal protocol, visual protocol, task performance, workload, and task duration were collected as dependent variables. A logistic regression analysis revealed that gameplay objective and task duration significantly affected the likelihood of a participant identifying the optimal solution to a game, with the likelihood of an optimal solution increasing with longer task duration and in the less flexible objective condition. In addition, workload appeared unaffected by either independent variable, but verbal protocols and visual observations indicated that high-performing participants demonstrated a greater degree of planning and situation awareness. Through identifying human behavior during optimization problem solving, the work of tower control can be better understood, which, in turn, provides insights for developing decision support systems for ground movement management