153 research outputs found
Queueing systems with different types of renovation mechanism and thresholds as the mathematical models of active queue management mechanism
This article is devoted to some aspects of using the renovation mechanism (different types of renovation are considered, definitions and brief overview are also given) with one or several thresholds as the mathematical models of active queue management mechanisms. The attention is paid to the queuing systems in which a threshold mechanism with renovation is implemented. This mechanism allows to adjust the number of packets in the system by dropping (resetting) them from the queue depending on the ratio of a certain control parameter with specified thresholds at the moment of the end of service on the device (server) (in contrast to standard RED-like algorithms, when a possible drop of a packet occurs at the time of arrivals of next packets in the system). The models with one, two and three thresholds with different types of renovation are under consideration. It is worth noting that the thresholds determine not only from which place in the buffer the packets are dropped, but also to which the reset of packets occurs. For some of the models certain analytical and numerical results are obtained (the references are given), some of them are only under investigation, so only the mathematical model and current results may be considered. Some results of comparing classic RED algorithm with renovation mechanism are presented.Π Π°Π±ΠΎΡΠ° ΠΏΠΎΡΠ²ΡΡΠ΅Π½Π° Π½Π΅ΠΊΠΎΡΠΎΡΡΠΌ Π°ΡΠΏΠ΅ΠΊΡΠ°ΠΌ ΠΈΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΡ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠ° ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ (ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ Π²Π°ΡΠΈΠ°Π½ΡΡ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ ΡΠ°ΡΡΠΌΠΎΡΡΠ΅Π½Ρ, ΠΎΠΏΡΠ΅Π΄Π΅Π»Π΅Π½ΠΈΡ ΠΈ ΠΊΡΠ°ΡΠΊΠΈΠΉ ΠΎΠ±Π·ΠΎΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ) Ρ ΠΎΠ΄Π½ΠΈΠΌ ΠΈΠ»ΠΈ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΈΠΌΠΈ ΠΏΠΎΡΠΎΠ³Π°ΠΌΠΈ Π² ΠΊΠ°ΡΠ΅ΡΡΠ²Π΅ ΠΌΠ°ΡΠ΅ΠΌΠ°ΡΠΈΡΠ΅ΡΠΊΠΈΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠ² Π°ΠΊΡΠΈΠ²Π½ΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»Π΅Π½ΠΈΡ ΠΎΡΠ΅ΡΠ΅Π΄ΡΠΌΠΈ. ΠΠΏΠΈΡΠ°Π½Ρ ΡΠΈΡΡΠ΅ΠΌΡ ΠΌΠ°ΡΡΠΎΠ²ΠΎΠ³ΠΎ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ, Π² ΠΊΠΎΡΠΎΡΡΡ
ΡΠ΅Π°Π»ΠΈΠ·ΠΎΠ²Π°Π½ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ Ρ ΠΏΠΎΡΠΎΠ³Π°ΠΌΠΈ, ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡΡΠΈΠΉ ΡΠΏΡΠ°Π²Π»ΡΡΡ ΡΠΈΡΠ»ΠΎΠΌ Π·Π°ΡΠ²ΠΎΠΊ Π² ΡΠΈΡΡΠ΅ΠΌΠ΅ ΠΏΡΡΠ΅ΠΌ ΠΈΡ
ΡΠ±ΡΠΎΡΠ° ΠΈΠ· Π½Π°ΠΊΠΎΠΏΠΈΡΠ΅Π»Ρ Π² Π·Π°Π²ΠΈΡΠΈΠΌΠΎΡΡΠΈ ΠΎΡ Π·Π½Π°ΡΠ΅Π½ΠΈΡ Π½Π΅ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ ΡΠΏΡΠ°Π²Π»ΡΡΡΠ΅Π³ΠΎ ΠΏΠ°ΡΠ°ΠΌΠ΅ΡΡΠ° ΠΈ ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΡΡ
Π·Π½Π°ΡΠ΅Π½ΠΈΠΉ. Π‘Π±ΡΠΎΡ Π·Π°ΡΠ²ΠΎΠΊ ΠΈΠ· Π½Π°ΠΊΠΎΠΏΠΈΡΠ΅Π»Ρ ΠΏΡΠΎΠΈΡΡ
ΠΎΠ΄ΠΈΡ Π² ΠΌΠΎΠΌΠ΅Π½Ρ ΠΎΠΊΠΎΠ½ΡΠ°Π½ΠΈΡ ΠΎΠ±ΡΠ»ΡΠΆΠΈΠ²Π°Π½ΠΈΡ Π·Π°ΡΠ²ΠΊΠΈ Π½Π° ΠΏΡΠΈΠ±ΠΎΡΠ΅, ΡΡΠΎ ΠΎΡΠ»ΠΈΡΠ°Π΅Ρ Π΄Π°Π½Π½ΡΠΉ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌ ΡΠ±ΡΠΎΡΠ° ΠΎΡ RED-ΠΏΠΎΠ΄ΠΎΠ±Π½ΡΡ
Π°Π»Π³ΠΎΡΠΈΡΠΌΠΎΠ², Π΄Π»Ρ ΠΊΠΎΡΠΎΡΡΡ
ΡΠ±ΡΠΎΡ Π²ΠΎΠ·ΠΌΠΎΠΆΠ΅Π½ Π² ΠΌΠΎΠΌΠ΅Π½Ρ ΠΏΠΎΡΡΡΠΏΠ»Π΅Π½ΠΈΡ Π² ΡΠΈΡΡΠ΅ΠΌΡ. ΠΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΠΌΠΎΠ΄Π΅Π»ΠΈ Ρ ΠΎΠ΄Π½ΠΈΠΌ, Π΄Π²ΡΠΌΡ ΠΈΠ»ΠΈ ΡΡΠ΅ΠΌΡ ΠΏΠΎΡΠΎΠ³Π°ΠΌΠΈ. Π ΡΡΠΈΡ
ΠΌΠΎΠ΄Π΅Π»ΡΡ
ΠΏΠΎΡΠΎΠ³ΠΎΠ²ΡΠ΅ Π·Π½Π°ΡΠ΅Π½ΠΈΡ ΠΎΠΏΡΠ΅Π΄Π΅Π»ΡΡΡ Π½Π΅ ΡΠΎΠ»ΡΠΊΠΎ ΠΌΠ΅ΡΡΠΎ, Ρ ΠΊΠΎΡΠΎΡΠΎΠ³ΠΎ Π² Π½Π°ΠΊΠΎΠΏΠΈΡΠ΅Π»Π΅ Π½Π°ΡΠΈΠ½Π°Π΅ΡΡΡ ΡΠ±ΡΠΎΡ Π·Π°ΡΠ²ΠΎΠΊ, Π½ΠΎ ΠΈ Π΄ΠΎ ΠΊΠ°ΠΊΠΎΠΉ ΠΏΠΎΠ·ΠΈΡΠΈΠΈ Π·Π°ΡΠ²ΠΊΠΈ ΠΌΠΎΠ³ΡΡ Π±ΡΡΡ ΡΠ±ΡΠΎΡΠ΅Π½Ρ. ΠΠ»Ρ Π½Π΅ΠΊΠΎΡΠΎΡΡΡ
ΠΈΠ· ΠΎΠΏΠΈΡΡΠ²Π°Π΅ΠΌΡΡ
ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ ΡΠΆΠ΅ ΠΏΠΎΠ»ΡΡΠ΅Π½Ρ Π°Π½Π°Π»ΠΈΡΠΈΡΠ΅ΡΠΊΠΈΠ΅ ΠΈ ΡΠΈΡΠ»Π΅Π½Π½ΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ (ΡΡΡΠ»ΠΊΠΈ Π½Π° ΡΠ°Π±ΠΎΡΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ), Π½ΠΎ Π±ΠΎΠ»ΡΡΠ°Ρ ΡΠ°ΡΡΡ ΠΌΠΎΠ΄Π΅Π»Π΅ΠΉ Π½Π°Ρ
ΠΎΠ΄ΠΈΡΡΡ Π² ΠΏΡΠΎΡΠ΅ΡΡΠ΅ ΠΈΠ·ΡΡΠ΅Π½ΠΈΡ, ΠΏΠΎΡΡΠΎΠΌΡ ΠΏΡΠ΅Π΄ΡΡΠ°Π²Π»Π΅Π½Ρ ΡΠΎΠ»ΡΠΊΠΎ ΠΎΠΏΠΈΡΠ°Π½ΠΈΡ ΠΈ Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ΅ΠΊΡΡΠΈΠ΅ Π΄Π°Π½Π½ΡΠ΅. ΠΡΠΈΠ²Π΅Π΄Π΅Π½Ρ Π½Π΅ΠΊΠΎΡΠΎΡΡΠ΅ ΡΠ΅Π·ΡΠ»ΡΡΠ°ΡΡ ΡΡΠ°Π²Π½Π΅Π½ΠΈΡ ΠΊΠ»Π°ΡΡΠΈΡΠ΅ΡΠΊΠΎΠ³ΠΎ Π°Π»Π³ΠΎΡΠΈΡΠΌΠ° RED Ρ ΠΌΠ΅Ρ
Π°Π½ΠΈΠ·ΠΌΠΎΠΌ ΠΎΠ±Π½ΠΎΠ²Π»Π΅Π½ΠΈΡ
Fuelling the zero-emissions road freight of the future: routing of mobile fuellers
The future of zero-emissions road freight is closely tied to the sufficient availability of new and clean fuel options such as electricity and Hydrogen. In goods distribution using Electric Commercial Vehicles (ECVs) and Hydrogen Fuel Cell Vehicles (HFCVs) a major challenge in the transition period would pertain to their limited autonomy and scarce and unevenly distributed refuelling stations. One viable solution to facilitate and speed up the adoption of ECVs/HFCVs by logistics, however, is to get the fuel to the point where it is needed (instead of diverting the route of delivery vehicles to refuelling stations) using "Mobile Fuellers (MFs)". These are mobile battery swapping/recharging vans or mobile Hydrogen fuellers that can travel to a running ECV/HFCV to provide the fuel they require to complete their delivery routes at a rendezvous time and space. In this presentation, new vehicle routing models will be presented for a third party company that provides MF services. In the proposed problem variant, the MF provider company receives routing plans of multiple customer companies and has to design routes for a fleet of capacitated MFs that have to synchronise their routes with the running vehicles to deliver the required amount of fuel on-the-fly. This presentation will discuss and compare several mathematical models based on different business models and collaborative logistics scenarios
Reports to the President
A compilation of annual reports for the 1988-1989 academic year, including a report from the President of the Massachusetts Institute of Technology, as well as reports from the academic and administrative units of the Institute. The reports outline the year's goals, accomplishments, honors and awards, and future plans
Advances in Computational Intelligence Applications in the Mining Industry
This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners
Modelling and optimisation of resource usage in an IoT enabled smart campus
University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory.
The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms
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