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online and hard constrained adaptive dynamic programming algorithm for energy storage control in smart buildings

OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第3期44卷 1074-1091页

作者： Chen, Zitao Chen, Si-zhe Zhang, Yun Deng, Quanbin Zeng, Xianxian Guangdong Univ Technol Sch Automat Guangzhou 510006 Guangdong Peoples R China

The high computational complexity caused by static optimization is the key factor to hinder the development of energy management systems. Adaptive dynamic programming (ADP) is an effective dynamic optimization method to break through the bottleneck. However, the hard constraints of energy system have not been fully considered due to the non-convexity and nonlinearity of the value function, which makes the theoretical analysis complicated and brings safe security problems in battery systems. In this article, a systematic online ADP control framework is proposed for smart buildings control to ensure hard constraints to be satisfied. The second-order local expansion at the current state is used to replace the nonlinear value function to simplify the theoretical analysis with the error of the reminder term. Based on the local property of value function, a method for the determination of adaptive parameters is first designed. It is proven that the solution of adaptive parameters not only prevents over-charged and over-discharged of the battery but also limits the charging and discharging power of the battery to be less than the rated power. In addition, long-short term memory (LSTM) neural networks, a type dynamic network with memory characteristics, are used for the implementation of the present algorithm instead of the static networks to help realize the algorithm online. Due to the hidden state of LSTM, the performance of the online algorithm is improved after running the energy system. Numerical results verify the effectiveness of the proposed online control algorithm.

关键词： adaptive dynamic programming constrained dynamic optimization energy storage control online algorithm smart buildings

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A comprehensive study of an online packet scheduling algorithm

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THEORETICAL COMPUTER SCIENCE 2013年 497卷 31-38页

作者： Li, Fei George Mason Univ Dept Comp Sci Fairfax VA 22030 USA

We study the bounded-delay model for Qualify-of-Service buffer management. Time is discrete. There is a buffer. Unit-length jobs (also called packets) arrive at the buffer over time. Each packet has an integer release time, an integer deadline, and a positive real value. A packet's characteristics are not known to an online algorithm until the packet actually arrives. In each time step, at most one packet can be sent out of the buffer. The objective is to maximize the total value of the packets sent by their respective deadlines in an online manner. An online algorithm's performance is usually measured in terms of competitive ratio, when this online algorithm is compared with a clairvoyant algorithm achieving the maximum total value. In this paper, we study a simple and intuitive online algorithm. We analyze its performance in terms of competitive ratio for the general model and a few important variants. (C) 2012 Elsevier B.V. All rights reserved.

关键词： online algorithm Competitive analysis Buffer management Packet scheduling

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Bin-Packing-Based online Dynamic Bandwidth and Wavelength Allocation algorithm in Super-PON

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IEEE ACCESS 2021年 9卷 139379-139392页

作者： Garg, Sukriti Dixit, Abhishek Bharti Sch Telecommun Technol & Management Indian Inst Technol Delhi New Delhi 110016 India

Super passive optical network (Super-PON) is a next-generation Ethernet PON (NG-EPON) candidate that is envisaged to provide high data rate and low latency. For NG-EPON, there are two types of algorithms that manages bandwidth and wavelength scheduling, namely offline and online. The latter method is more scalable and efficient than the former method. Several online algorithms exist that propose schemes to manage wavelength and bandwidth scheduling. However, these algorithms lack in efficient wavelength utilization and switching. In this work, we propose a novel online bin-packing based dynamic bandwidth and wavelength allocation (DBWA) algorithm for Super-PON, namely updated best fit bin-packing (UBF-BP). This algorithm limits the wavelength switching per cycle, and uses the modified version of best fit bin-packing (BF-BP) technique for optimal wavelength allocation. Simulation results show that the proposed DBWA algorithm has a low complexity and overcomes the inefficiencies of wavelength utilization and switching. This results in lower network delay and higher channel utilization than the state-of-the-art DBWA. Furthermore, to verify the correctness of the proposed algorithm, we propose an analytical model and validate these simulation results with the analytical results.

关键词： Optical network units Heuristic algorithms Delays Bandwidth Resource management Prediction algorithms Passive optical networks Bin-packing DBWA delay analysis NG-EPON online algorithm super-PON

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Two online algorithms for the Ambulance Systems

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Journal of Computer Science & Technology 2001年第2期16卷 176-181页

作者：眭跃飞 Institute of Software The Chinese Academy of Sciences Beijing P.R. China

An ambulance system consists of a collection S = {s1,...,sm ) sm} of emergency centers in a metric space M. Each emergency center si has a positive integral capacity ci to denote, for example, the number of ambulances at the center. There are n = =1, ci patients requiring ambulances at different times tj and every patient is associated with a number bj, the longest time during which the patient can wait for ambulance. An online algorithm A will decide which emergency center sends an ambulance to serve a request for ambulance from a patient at some time. If algorithm A sends an ambulance in si to serve a patient rj, then it must be observed that di,j/v < bj, where di,j is the distance between emergency center si and patient rj, and v is the velocity of ambulance. A fault of algorithm A is such that to a request for ambulance at some time tj with j ≤n, for every i with di,j/v < bj, there is no ambulance available in si. The cost of an algorithm A is the number of faults A makes. This paper gives two algorithms B and C, where B is the local greedy algorithm and C is a variant of balancing costs, and proves that both B and C have no bounded competitive ratios. Moreover, given any sequence a of requests for ambulances without optimal faults, the cost of C on σis less than or equal to [n/3] and that of B is less than or equal to [n/2].

关键词： online algorithm k-server problem competitive analysis

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A BEST POSSIBLE algorithm FOR AN online SCHEDULING PROBLEM WITH DETERIORATING EFFECT IN STEEL BOX GIRDER SECTION PRODUCTION

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2023年第8期19卷 5969-5983页

作者： Yang, Xiaohua Ma, Ran Zhang, Yuzhong Qingdao Univ Technol Sch Management Engn Qingdao 266520 Peoples R China Qufu Normal Univ Inst Operat Res Sch Management Rizhao 276826 Peoples R China

The paper focuses on an online scheduling problem with the effect of deterioration to minimize the maximum delivery time in steel box girder sections. This deterioration is considered in terms of job processing and delivery. Specifically, the processing time of job Jj is defined by an increasing function of its starting time t, i.e., pj = alpha j(A + Bt), where alpha j > 0 is the processing deterioration rate of job Jj. Moreover, the delivery time of job Jj is determined with an increasing function of its completion time, i.e., qj = bjCj, where bj > 0 is the delivery deterioration rate of job Jj. For this problem, we prove the property of optimal scheduling and show that the performance ratio of any deterministic online algorithm is not less than 2+ B alpha max. Furthermore, we provide a best possible online algorithm LDDR with a competitive ratio of 2 + B alpha max. And then a concise computational simulation study is presented to show that our algorithm performs very well in practice.

关键词： Scheduling online algorithm delivery time deteriorating effect sec-tion of steel box girder

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Vessel Trajectory online Multi-Dimensional Simplification algorithm

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JOURNAL OF NAVIGATION 2020年第2期73卷 342-363页

作者： Zhang, Yuan-qiang Shi, Guo-you Li, Song Zhang, Shu-kai Dalian Maritime Univ Nav Coll Dalian 116026 Peoples R China Ningbo Univ Fac Maritime & Transportat Ningbo 315211 Peoples R China Shanghai Maritime Univ Merchant Marine Coll Shanghai 201306 Peoples R China

Facilitated by the establishment of terrestrial networks and satellite constellations of Automatic Identification System (AIS) receivers, large quantities of spatial and temporal information that trace ships' paths have been collected. The exponential increase in the amount of AIS data has caused expensive and time-consuming transmission, calculation and storage problems. Using appropriate trajectory simplification methods in a timely manner to compress redundant information while minimising the loss of importation information is important. To minimise the simplification error, this paper proposes an online multi-dimensional simplification algorithm for AIS trajectory streaming data. The simplification algorithm takes into account position, direction and speed preservation. Finally, a comparison experiment with other algorithms is made to examine the effectiveness of this algorithm. The results indicate that the proposed online multi-dimensional simplification algorithm can effectively preserve a ship's motion state, including its position, speed and course.

关键词： AIS Trajectories Trajectory simplification online algorithm DOTS algorithm

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online Rectangle Packing algorithm for Swapped Battery Charging Dispatch Model Considering Continuous Charging Power

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2024年第1期21卷 320-331页

作者： Bai, Jiawen Ding, Tao Jia, Wenhao Zhu, Shanying Bai, Linquan Li, Fangxing Xi An Jiao Tong Univ Dept Elect Engn Xian 710049 Shaanxi Peoples R China Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Univ North Carolina Charlotte Dept Syst Engn & Engn Charlotte NC 27606 USA Univ Tennessee Knoxville UTK Dept Elect Engn & Comp Sci Knoxville TN 37996 USA

The vigorous development of electric vehicles (EVs) is an important means of reducing carbon emissions and mitigating environmental problems such as the greenhouse effect. Battery swapping stations (BSSs) can both provide battery swapping services for large-scale EVs and charge batteries centrally. As the supply of fully charged batteries in the BSS shrinks, it becomes necessary to schedule the charging of the depleted batteries rapidly that users have swapped for fully-charged ones. The charging schedule for depleted batteries must be made without knowledge of future battery arrivals. In this context, this paper develops a mathematical model for online charging scheduling of BSSs, formulates the charging strategy as a two-dimensional rectangle packing problem, and quickly calculates the scheduling arrangement of batteries by partitioning the remaining available capacity of a BSS. Since there are limited battery types within the BSS which can provide battery replacement services, this paper supplements the proposed model with known battery types, which improves the utilization of the available capacity of BSSs. Finally, numerical results verify the effectiveness of the proposed model. Note to Practitioners-Electric vehicles (EVs) are becoming an alternative way to reduce carbon emissions in transportation systems. Herein, the optimal battery charging problem is the core problem when it comes to dispatching a huge number of EVs. Up to now, battery-swapping is widely used for EVs due to its simple, convenient way. Furthermore, a business model for the battery swapping stations (BSSs) is brought up, where EV users send their depleted batteries to the BSS and the BSS provides the users with a fully charged replacement battery from its warehouse, which only takes a few minutes. Since the maximum charging power of the BSS is limited by the capacity of the transformer connecting the BSS to the power grid, the BSS will adopt an optimal charging schedule that maximizes the c

关键词： Electric vehicles battery swapping station uninterrupted discrete-rate charging rectangle packing problem online algorithm

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An online relevant set algorithm for statistical machine translation

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IEEE TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING 2008年第7期16卷 1274-1286页

作者： Tillmann, Christoph Zhang, Tong IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA Rutgers State Univ Dept Stat Piscataway NJ 08854 USA

This paper presents a novel online relevant set algorithm for a linearly scored block sequence translation model. The key component is a new procedure to directly optimize the global scoring function used by a statistical machine translation (SMT) decoder. This training procedure treats the decoder as a black-box, and thus can be used to optimize any decoding scheme. The novel algorithm is evaluated using different feature types: 1) commonly used probabilistic features, such as translation, language, or distortion model probabilities, and 2) binary features. In particular, encouraging results on a standard Arabic-English translation task are presented for a translation system that uses only binary feature functions. To further demonstrate the effectiveness of the novel training algorithm, a detailed comparison with the widely used minimum-error-rate (MER) training algorithm is presented using the same decoder and feature set. The online algorithm is simplified by introducing so-called "seed" block sequences which enable the training to be carried out without a gold standard block translation. While the online training algorithm is extremely fast, it also improves translation scores over the MER algorithm in some experiments.

关键词： discriminative learning online algorithm statistical machine translation

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VMSA: a performance preserving online VM splitting and placement algorithm in dynamic cloud environments

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JOURNAL OF SUPERCOMPUTING 2016年第8期72卷 3169-3193页

作者： Liu, Liu Xu, Jie Yu, Hongfang Li, Lemin Qiao, Chunming Univ Elect Sci & Technol China Sch Commun & Informat Engn Chengdu 611731 Peoples R China George Mason Univ Dept Syst Engn & Operat Res Fairfax VA 22030 USA SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14226 USA

Server consolidation schemes whereby each server is replaced with a virtual machine (VM) and multiple such VMs are run on a single physical server can reduce the number of physical servers needed, and in turn, both the cost and energy consumption in data centers. However, existing schemes have not fully exploited the flexibility in the usage and allocation of virtualization resources, so as to allow one application originally deployed on a single large VM (LVM) to be split and hosted by multiple smaller VMs (SVM). Using multiple SVMs instead of an LVM enables resource allocation at a smaller granularity and thus may further increase the utilization and reduce the number of physical servers. However, a major challenge to overcome when deploying multiple SVMs for one application is to preserve the performance of the application in terms of response delay. In this paper, we show through theoretical analysis and experiments that in order to preserve the performance of the application, one needs to allocate sufficient resources to each SVM, and the total amount of resources required by all the SVMs will exceed that required by the LVM. Nevertheless, we also show that by using the proposed heuristic algorithm called VM splitting and assignment (VMSA), we can substantially improve the utilization and reduce the number of physical servers.

关键词： Cloud computing Requirement splitting Virtual machine placement online algorithm Dynamic resource demand

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A BEST POSSIBLE algorithm FOR AN online SCHEDULING PROBLEM WITH POSITION-BASED LEARNING EFFECT

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JOURNAL OF INDUSTRIAL AND MANAGEMENT OPTIMIZATION 2022年第6期18卷 3989-4010页

作者： Ma, Ran Zhang, Lu Zhang, Yuzhong Qingdao Univ Technol Sch Management Engn Qingdao 266525 Peoples R China Univ Res Ctr Smart City Construct & Management Sh Qingdao 266525 Peoples R China Qufu Normal Univ Sch Management Inst Operat Res Rizhao 276826 Peoples R China

In this paper, we focus on an online scheduling problem with position-based learning effect on a single machine, where the jobs are released online over time and preemption is not allowed. The information about each job J(j), including the basic processing time p(j) and the release time r(j), is only available when it arrives. The actual processing time p '(j) of each job J(j) is defined as a function related to its position r, i.e., p '(j) = p(j)(alpha-r beta), where alpha and beta are both nonnegative learning index. Our goal is to minimize the sum of completion time of all jobs. For this problem, we design a deterministic polynomial time online algorithm Delayed Shortest Basic Processing Time (DSBPT). In order to facilitate the understanding of the online algorithm, we present a relatively common and simple example to describe the execution process of the algorithm, and then by competitive analysis, we show that online algorithm DSBPT is a best possible online algorithm with a competitive ratio of 2.

关键词： scheduling online algorithm competitive ratio single machine learning effect

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