Load optimization in a planar network

Business to consumer e-commerce (B2C) has increased sharply in recent years driven by a growing online population and changes in consumer behavior. In metropolitan areas, the “Amazon effect” (online retailers’ vast selection, fast shipping, free returns, and low prices) has led to an increased use of light goods vehicles. This is affecting the rational functioning of the transport system, including a high degree of fragmentation, low load optimization, and, among other externalities, higher traffic congestion. This paper investigates the potential of a metro system, in a big city like Madrid, to provide delivery services by leveraging its existing carrying capacity and using the metro stations to collect parcels in lockers. It would be a new mixed distribution model for last-mile deliveries associated with e-commerce. To that end, the paper evaluates the cost and impacts of two alternative scenarios for managing the unused space in rolling stock (shared trains) or specific full train services (dedicated trains) on existing lines. The external costs of the proposed scenarios are compared with current e-commerce delivery scenario (parcel delivery by road). The results show that underground transport of parcels could significantly reduce congestion costs, accidents, noise, GHG emissions, and air pollution.

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Deep Learning-Based Approaches to Optimize the Electricity Contract Capacity Problem for Commercial Customers

Energies ◽

10.3390/en14082181 ◽

2021 ◽

Vol 14 (8) ◽

pp. 2181

Author(s):

Rafik Nafkha ◽

Tomasz Ząbkowski ◽

Krzysztof Gajowniczek

Keyword(s):

Deep Learning ◽

Power Grid ◽

Hybrid Approach ◽

Electricity Consumption ◽

Maximum Power ◽

Learning Approaches ◽

Two Stage ◽

Load Optimization ◽

Optimal Capacity ◽

Maximal Load

The electricity tariffs available to customers in Poland depend on the connection voltage level and contracted capacity, which reflect the customer demand profile. Therefore, before connecting to the power grid, each consumer declares the demand for maximum power. This amount, referred to as the contracted capacity, is used by the electricity provider to assign the proper connection type to the power grid, including the size of the security breaker. Maximum power is also the basis for calculating fixed charges for electricity consumption, which is controlled and metered through peak meters. If the peak demand exceeds the contracted capacity, a penalty charge is applied to the exceeded amount, which is up to ten times the basic rate. In this article, we present several solutions for entrepreneurs based on the implementation of two-stage and deep learning approaches to predict maximal load values and the moments of exceeding the contracted capacity in the short term, i.e., up to one month ahead. The forecast is further used to optimize the capacity volume to be contracted in the following month to minimize network charge for exceeding the contracted level. As confirmed experimentally with two datasets, the application of a multiple output forecast artificial neural network model and a genetic algorithm (two-stage approach) for load optimization delivers significant benefits to customers. As an alternative, the same benefit is delivered with a deep learning architecture (hybrid approach) to predict the maximal capacity demands and, simultaneously, to determine the optimal capacity contract.

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Correction to: Industrial-iot-hardware security-improvement using plan load optimization method in cloud

International Journal of Systems Assurance Engineering and Management ◽

10.1007/s13198-021-01223-7 ◽

2021 ◽

Author(s):

Shakila Basheer ◽

Magesh Gopu ◽

Rincy Merlin Mathew ◽

Maryam Aysha Bivi ◽

M. Prabu

Keyword(s):

Hardware Security ◽

Optimization Method ◽

Load Optimization ◽

Industrial Iot

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Load-Optimization in Reconfigurable Networks

ACM SIGMETRICS Performance Evaluation Review ◽

10.1145/3453953.3453962 ◽

2021 ◽

Vol 48 (3) ◽

pp. 39-44 ◽

Cited By ~ 1

Author(s):

Wenkai Dai ◽

Klaus-Tycho Foerster ◽

David Fuchssteiner ◽

Stefan Schmid

Keyword(s):

Data Centers ◽

Algorithmic Problem ◽

Reconfigurable Networks ◽

Load Optimization ◽

Clos Networks ◽

Optical Circuit ◽

Tree Topologies ◽

Packet Switch ◽

Routing Policies

Emerging reconfigurable data centers introduce the unprecedented flexibility in how the physical layer can be programmed to adapt to current traffic demands. These reconfigurable topologies are commonly hybrid, consisting of static and reconfigurable links, enabled by e.g. an Optical Circuit Switch (OCS) connected to top-of-rack switches in Clos networks. Even though prior work has showcased the practical benefits of hybrid networks, several crucial performance aspects are not well understood. In this paper, we study the algorithmic problem of how to jointly optimize topology and routing in reconfigurable data centers with a known traffic matrix, in order to optimize a most fundamental metric, maximum link load. We chart the corresponding algorithmic landscape by investigating both un-/splittable flows and (non-)segregated routing policies. We moreover prove that the problem is not submodular for all these routing policies, even in multi-layer trees, where a topological complexity classification of the problem reveals that already trees of depth two are intractable. However, networks that can be abstracted by a single packet switch (e.g., nonblocking Fat-Tree topologies) can be optimized efficiently, and we present optimal polynomialtime algorithms accordingly. We complement our theoretical results with trace-driven simulation studies, where our algorithms can significantly improve the network load in comparison to the state of the art.

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Minimize the Deflection of Laminated Composite Plates under the External Load Using Fiber Orientation Angle

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.709.144 ◽

2014 ◽

Vol 709 ◽

pp. 144-147

Author(s):

Ying Tao Chen ◽

Song Xiang ◽

Wei Ping Zhao

Keyword(s):

Fiber Orientation ◽

External Load ◽

Optimization Problem ◽

Orientation Angle ◽

Laminated Composite ◽

Composite Plates ◽

Laminated Composite Plates ◽

Load Optimization ◽

Fiber Orientation Angle ◽

Optimization Parameters

Optimization of fiber orientation angle is studied to minimize the deflection of the laminated composite plates by the genetic algorithm. The objective function of optimization problem is the minimum deflection of laminated composite plates under the external load; optimization parameters are fiber orientation angle of laminated composite plates. The results for the optimal fiber orientation angle and the minimum deflection of the 4-layer plates are presented to demonstrate the validity of present method.

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