Green intermodal freight transportation: bi-objective modeling and analysis

Efficient planning of freight transportation requires a comprehensive look at wide range of factors in the operation and management of any transportation mode to achieve safe, fast, and environmentally suitable movement of goods. In this regard, a combination of transportation modes offers flexible and environmentally friendly alternatives to transport high volumes of goods over long distances. In order to reflect the advantages of each transportation mode, it is the challenge to develop models and algorithms in Transport Management System software packages. This paper discusses the principles of green logistics required in designing such models and algorithms which truly represent multiple modes and their characteristics. Thus, this research provides a unique practical contribution to green logistics literature by advancing our understanding of the multi-objective planning in intermodal freight transportation. Analysis based on a case study from hinterland intermodal transportation in Europe is therefore intended to make contributions to the literature about the potential benefits from combining economic and environmental criteria in transportation planning. An insight derived from the experiments conducted shows that there is no need to greatly compromise on transportation costs in order to achieve a significant reduction in carbon-related emissions

A geometrical approach to control and controllability of nonlinear dynamical networks

abstract: In spite of the recent interest and advances in linear controllability of complex networks, controlling nonlinear network dynamics remains an outstanding problem. Here we develop an experimentally feasible control framework for nonlinear dynamical networks that exhibit multistability. The control objective is to apply parameter perturbation to drive the system from one attractor to another, assuming that the former is undesired and the latter is desired. To make our framework practically meaningful, we consider restricted parameter perturbation by imposing two constraints: it must be experimentally realizable and applied only temporarily. We introduce the concept of attractor network, which allows us to formulate a quantifiable controllability framework for nonlinear dynamical networks: a network is more controllable if the attractor network is more strongly connected. We test our control framework using examples from various models of experimental gene regulatory networks and demonstrate the beneficial role of noise in facilitating control.The final version of this article, as published in Nature Communications, can be viewed online at: https://www.nature.com/articles/ncomms1132

A flexible information service for management of virtualized software-defined infrastructures

Summary There is a major shift in the Internet towards using programmable and virtualized network devices, offering significant flexibility and adaptability. New networking paradigms such as software-defined networking and network function virtualization bring networks and IT domains closer together using appropriate architectural abstractions. In this context, new and novel information management features need to be introduced. The deployed management and control entities in these environments should have a clear, and often global, view of the network environment and should exchange information in alternative ways (e.g. some may have real-time constraints, while others may be throughput sensitive). Our work addresses these two network management features. In this paper, we define the research challenges in information management for virtualized highly dynamic environments. Along these lines, we introduce and present the design details of the virtual infrastructure information service, a new management information handling framework that (i) provides logically centralized information flow establishment, optimization, coordination, synchronization and management with respect to the diverse management and control entity demands; (ii) is designed according to the characteristics and requirements of software-defined networking and network function virtualization; and (iii) inter-operates with our own virtualized infrastructure framework. Evaluation results demonstrating the flexible and adaptable behaviour of the virtual infrastructure information service and its main operations are included in the paper. Copyright © 2016 John Wiley & Sons, Ltd

Economic linear parameter varying model predictive control of the aeration system of a wastewater treatment plant

This work proposes an economic model predictive control (EMPC) strategy in the linear parameter varying (LPV) framework for the control of dissolved oxygen concentrations in the aerated reactors of a wastewater treatment plant (WWTP). A reduced model of the complex nonlinear plant is represented in a quasi-linear parameter varying (qLPV) form to reduce computational burden, enabling the real-time operation. To facilitate the formulation of the time-varying parameters which are functions of system states, as well as for feedback control purposes, a moving horizon estimator (MHE) that uses the qLPV WWTP model is proposed. The control strategy is investigated and evaluated based on the ASM1 simulation benchmark for performance assessment. The obtained results applying the EMPC strategy for the control of the aeration system in the WWTP of Girona (Spain) show its effectiveness.This work has been co-financed by the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERFD) through the project SaCoAV (ref. MINECO PID2020- 114244RB-I00), by the European Regional Development Fund of the European Union in the framework of the ERDF Operational Program of Catalonia 2014–2020 (ref. 001-P-001643 Looming Factory), and by the DGR of Generalitat de Catalunya (SAC group ref. 2017/SGR/482).Peer ReviewedPostprint (author's final draft

ScienceSDS: A Novel Software Defined Security Framework for Large-scale Data-intensive Science

Experimental science workflows from projects such as Compact Muon Solenoid (CMS) [6] and Laser Interferometer Gravitational Wave Observatory (LIGO) [2] are characterized by data-intensive computational tasks over large datasets transferred over encrypted channels. The Science DMZ [7] approach to network design favors lossless packet forwarding through a separate isolated network over secure lossy forwarding through stateful packet processors (e.g. fire-walls). We propose ScienceSDS, a novel software denied security framework for securely monitoring large-scale science datasets over a software defined networking and network functions virtualization (SDN/NFV) infrastructure

Modeling and control design of a contact-based, electrostatically actuated rotating sphere

The performance of micromirrors in terms of their maximum deflection is often limited due to mechanical constraints in the design. To increase the range of achievable deflection angles, we present a novel concept in which a free-lying sphere with a flat side as reflector can be rotated. Due to the large forces needed to move the sphere, multiple electrostatic actuators are used to cooperatively rotate the sphere in iterative steps by impacts and friction. A parameterized system-level model of the configuration is derived, which considers arbitrary multi-contact scenarios and can be used for simulation, analysis, and control design purposes. Due to the complex, indirect relation between the actuator voltages and the sphere motion, model-based numerical optimization is applied to obtain suitable system inputs. This results in rotation sequences, which can be understood as a sequence of motion primitives, thus transforming the continuous time model into an abstract discrete time model. Based on this, we propose a feedback control strategy for trajectory following, considering model uncertainties by a learning scheme. High precision is achieved by an extension controlling the angular change of each rotation step. The suitability of the overall approach is demonstrated in simulation for maximum angles of 40°, achieving angular velocities of approximately 10°/s

Hybrid simulation and optimization approach for green intermodal transportation problem with travel time uncertainty

The increasing volumes of road transportation contribute to congestion on road, which leads to delays and other negative impacts on the reliability of transportation. Moreover, transportation is one of the main contributors to the growth of carbon dioxide equivalent emissions, where the impact of road transportation is significant. Therefore, governmental organizations and private commercial companies are looking for greener transportation solutions to eliminate the negative externalities of road transportation. In this paper, we present a novel solution framework to support the operational-level decisions for intermodal transportation networks using a combination of an optimization model and simulation. The simulation model includes stochastic elements in form of uncertain travel times, whereas the optimization model represents a deterministic and linear multi-commodity service network design formulation. The intermodal transportation plan can be optimized according to different objectives, including costs, time and CO2e emissions. The proposed approach is successfully implemented to real-life scenarios where differences in transportation plans for alternative objectives are presented. The solutions for transportation networks with up to 250 services and 20 orders show that the approach is capable of delivering reliable solutions and identifying possible disruptions and alternatives for adapting the unreliable transportation plans