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Journal articles on the topic 'Computer bus scheduling'

Author: Grafiati

Published: 4 June 2021

Last updated: 3 February 2022

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1

Momber, Ilan, German Morales-Espana, Andres Ramos, and Tomas Gomez. "PEV Storage in Multi-Bus Scheduling Problems." IEEE Transactions on Smart Grid 5, no. 2 (2014): 1079–87. http://dx.doi.org/10.1109/tsg.2013.2290594.

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2

Houbbadi, Adnane, Rochdi Trigui, Serge Pelissier, Eduardo Redondo-Iglesias, and Tanguy Bouton. "Optimal Scheduling to Manage an Electric Bus Fleet Overnight Charging." Energies 12, no. 14 (2019): 2727. http://dx.doi.org/10.3390/en12142727.

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Electro-mobility is increasing significantly in the urban public transport and continues to face important challenges. Electric bus fleets require high performance and extended longevity of lithium-ion battery at highly variable temperature and in different operating conditions. On the other hand, bus operators are more concerned about reducing operation and maintenance costs, which affects the battery aging cost and represents a significant economic parameter for the deployment of electric bus fleets. This paper introduces a methodological approach to manage overnight charging of an electric

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3

Hao Xiaoni, Jin Wenzhou, and Yang Yazao. "Bus Scheduling Combination and Headway Optimization of a Bus Lane Line." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 5, no. 6 (2013): 767–76. http://dx.doi.org/10.4156/aiss.vol5.issue6.91.

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4

Li, Ming, Linlin Wang, Jingfeng Yang, et al. "Passenger flow forecast for customized bus based on time series fuzzy clustering algorithm." Interaction Studies 20, no. 1 (2019): 42–60. http://dx.doi.org/10.1075/is.18040.li.

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Abstract Customized bus services are conducive to improving urban traffic and environment, and have attracted widespread attention. However, the problems encountered in the new customized bus mode include the large difference between the basis of customized bus passenger flow data analysis and the basis of the traditional bus passenger flow data analysis, and the difficulty in different vehicle scheduling caused by the combination of traditional and customized bus modes. We propose a customized bus passenger flow analysis algorithm and multi-destination customized bus line capacity scheduling

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5

Yıldırım, Şule, and Barış Yıldız. "Electric bus fleet composition and scheduling." Transportation Research Part C: Emerging Technologies 129 (August 2021): 103197. http://dx.doi.org/10.1016/j.trc.2021.103197.

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6

Arar, Chafik. "Superposed Redundancy Approach for Building Reliable Communication in Multi-Bus Heterogeneous Systems." International Journal of Embedded and Real-Time Communication Systems 10, no. 1 (2019): 1–21. http://dx.doi.org/10.4018/ijertcs.2019010101.

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In this article, the author uses a new variant of passive redundancy, which allows for a fictitious dual assignment by simultaneously scheduling two backup copies that overlap on the same communication bus at a given time. The proposed reliable fault tolerant greedy list scheduling algorithm is based on a superposed backup copy. This scheduling algorithm is considering up to n communication buses faults, caused by hardware faults and compensated by software redundancy solutions. it allows a reliable communication and efficient use of buses. In the experiments, the proposed methods are evaluate

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7

H. Bookbinder, James, and Steven H. Edwards. "School-bus routing for program scheduling." Computers & Operations Research 17, no. 1 (1990): 79–94. http://dx.doi.org/10.1016/0305-0548(90)90030-b.

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8

Bocewicz, Grzegorz, and Zbigniew Banaszak. "Multimodal processes scheduling in mesh-like network environment." Archives of Control Sciences 25, no. 2 (2015): 237–61. http://dx.doi.org/10.1515/acsc-2015-0016.

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Abstract Multimodal processes planning and scheduling play a pivotal role in many different domains including city networks, multimodal transportation systems, computer and telecommunication networks and so on. Multimodal process can be seen as a process partially processed by locally executed cyclic processes. In that context the concept of a Mesh-like Multimodal Transportation Network (MMTN) in which several isomorphic subnetworks interact each other via distinguished subsets of common shared intermodal transport interchange facilities (such as a railway station, bus station or bus/tram stop

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9

Lehoczky, John P., and Lui Sha. "Performance of real-time bus scheduling algorithms." ACM SIGMETRICS Performance Evaluation Review 14, no. 1 (1986): 44–53. http://dx.doi.org/10.1145/317531.317538.

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10

Lin, Dung-Ying, Chieh-Ju Juan, and Ching-Chih Chang. "A Branch-and-Price-and-Cut Algorithm for the Integrated Scheduling and Rostering Problem of Bus Drivers." Journal of Advanced Transportation 2020 (February 25, 2020): 1–19. http://dx.doi.org/10.1155/2020/3153201.

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In the transportation industry, crew management is typically decomposed into two phases: crew scheduling and crew rostering. Due to the complexity of scheduling and rostering, bus transportation is not an exception and many relevant studies do not consider both procedures simultaneously. However, such a decomposition can yield inferior schedules/rosters. To address this issue, this paper proposes an integrated scheduling and rostering model for bus drivers and devises a branch-and-price-and-cut (BPC) algorithm to solve the complex problem. The proposed solution framework is empirically applied

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11

Paixão, J. Pinto, and I. M. Branco. "A quasi-assignment algorithm for bus scheduling." Networks 17, no. 3 (1987): 249–69. http://dx.doi.org/10.1002/net.3230170302.

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12

Kang, Liujiang, Shukai Chen, and Qiang Meng. "Bus and driver scheduling with mealtime windows for a single public bus route." Transportation Research Part C: Emerging Technologies 101 (April 2019): 145–60. http://dx.doi.org/10.1016/j.trc.2019.02.005.

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13

Yan, Shangyao, Yu-Jung Tung, and Ching-Hui Tang. "Intercity Bus Scheduling Model Incorporating Variable Market Share." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 37, no. 6 (2007): 921–32. http://dx.doi.org/10.1109/tsmca.2007.904824.

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14

Kyngas, Jari, and Kimmo Nurmi. "Days-off scheduling for a bus transportation company." International Journal of Innovative Computing and Applications 3, no. 1 (2011): 42. http://dx.doi.org/10.1504/ijica.2011.037950.

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15

Gkiotsalitis, Konstantinos, Oskar A. L. Eikenbroek, and Oded Cats. "Robust Network-Wide Bus Scheduling With Transfer Synchronizations." IEEE Transactions on Intelligent Transportation Systems 21, no. 11 (2020): 4582–92. http://dx.doi.org/10.1109/tits.2019.2941847.

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16

Wang, Shiyao, Yuanwei Qi, Zhonghua Han, and Jingyuan Zhang. "Bus manufacturing workshop scheduling method with routing buffer." International Journal of Simulation and Process Modelling 15, no. 3 (2020): 225. http://dx.doi.org/10.1504/ijspm.2020.10029336.

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17

Han, Zhonghua, Jingyuan Zhang, Shiyao Wang, and Yuanwei Qi. "Bus manufacturing workshop scheduling method with routing buffer." International Journal of Simulation and Process Modelling 15, no. 3 (2020): 225. http://dx.doi.org/10.1504/ijspm.2020.107325.

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18

Chen, Der-San, Henry A. Kallsen, and Richard C. Snider. "School bus routing and scheduling: An expert system approach." Computers & Industrial Engineering 15, no. 1-4 (1988): 179–83. http://dx.doi.org/10.1016/0360-8352(88)90082-4.

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19

Li, Lu, Hong K. Lo, and Feng Xiao. "Mixed bus fleet scheduling under range and refueling constraints." Transportation Research Part C: Emerging Technologies 104 (July 2019): 443–62. http://dx.doi.org/10.1016/j.trc.2019.05.009.

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20

Guedes, Pablo Cristini, Denis Borenstein, Monize Sâmara Visentini, Olinto César Bassi de Araújo, and Alberto Francisco Kummer Neto. "Vehicle scheduling problem with loss in bus ridership." Computers & Operations Research 111 (November 2019): 230–42. http://dx.doi.org/10.1016/j.cor.2019.07.002.

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21

Yang, Xinfeng, and Yicheng Qi. "Research on Optimization of Multi-Objective Regional Public Transportation Scheduling." Algorithms 14, no. 4 (2021): 108. http://dx.doi.org/10.3390/a14040108.

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The optimization of bus scheduling is a key method to improve bus service. So, the purpose of this paper is to address the regional public transportation dispatching problem, while taking into account the association between the departure time of buses and the waiting time of passengers. A bi-objective optimization model for regional public transportation scheduling is established to minimize the total waiting cost of passengers and to maximize the comprehensive service rate of buses. Moreover, a NSGA-II algorithm with adaptive adjusted model for crossover and mutation probability is designed

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22

Pan, Yan, Shining Li, Qianwu Chen, et al. "Efficient Schedule of Energy-Constrained UAV Using Crowdsourced Buses in Last-Mile Parcel Delivery." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 5, no. 1 (2021): 1–23. http://dx.doi.org/10.1145/3448079.

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Stimulated by the dramatical service demand in the logistics industry, logistics trucks employed in last-mile parcel delivery bring critical public concerns, such as heavy cost burden, traffic congestion and air pollution. Unmanned Aerial Vehicles (UAVs) are a promising alternative tool in last-mile delivery, which is however limited by insufficient flight range and load capacity. This paper presents an innovative energy-limited logistics UAV schedule approach using crowdsourced buses. Specifically, when one UAV delivers a parcel, it first lands on a crowdsourced social bus to parcel destinati

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23

Markenscoff, Pauline. "Bus scheduling for a multiple-processor system with shared buses." IEE Proceedings E Computers and Digital Techniques 134, no. 6 (1987): 288. http://dx.doi.org/10.1049/ip-e.1987.0049.

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24

Li, Boda, Ying Chen, Wei Wei, Shaowei Huang, and Shengwei Mei. "Resilient Restoration of Distribution Systems in Coordination With Electric Bus Scheduling." IEEE Transactions on Smart Grid 12, no. 4 (2021): 3314–25. http://dx.doi.org/10.1109/tsg.2021.3060801.

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25

Bögl, Michael, Karl F. Doerner, and Sophie N. Parragh. "The school bus routing and scheduling problem with transfers." Networks 65, no. 2 (2015): 180–203. http://dx.doi.org/10.1002/net.21589.

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26

SUN, Chuanjiao, Wei ZHOU, and Yuanqing WANG. "Scheduling Combination and Headway Optimization of Bus Rapid Transit." Journal of Transportation Systems Engineering and Information Technology 8, no. 5 (2008): 61–67. http://dx.doi.org/10.1016/s1570-6672(08)60039-2.

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27

Khoo, Hooi Ling, and Ghim Ping Ong. "Bi-Objective Optimization Approach for Exclusive Bus Lane Scheduling Design." Journal of Computing in Civil Engineering 29, no. 5 (2015): 04014056. http://dx.doi.org/10.1061/(asce)cp.1943-5487.0000355.

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28

Tang, Chunyan, Avishai Ceder, Shengchuan Zhao, and Ying-En Ge. "Vehicle Scheduling of Single-Line Bus Service Using Operational Strategies." IEEE Transactions on Intelligent Transportation Systems 20, no. 3 (2019): 1149–59. http://dx.doi.org/10.1109/tits.2018.2841061.

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29

HUSSIN, F. A., T. YONEDA, A. ORAILOGLU, and H. FUJIWARA. "Scheduling Power-Constrained Tests through the SoC Functional Bus." IEICE Transactions on Information and Systems E91-D, no. 3 (2008): 736–46. http://dx.doi.org/10.1093/ietisy/e91-d.3.736.

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30

Liu, Tao, Avishai Ceder, Jihui Ma, Wei Guan, and Lijie Zhou. "Graphical Human–Machine Interactive Approach for Integrated Bus Transit Scheduling: Lessons Gained From a Large Bus Company." IEEE Transactions on Intelligent Transportation Systems 18, no. 4 (2017): 1023–28. http://dx.doi.org/10.1109/tits.2016.2604392.

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31

Finta, Lucian, and Zhen Liu. "Complexity of Task Graph Scheduling with Fixed Communication Capacity." International Journal of Foundations of Computer Science 08, no. 01 (1997): 43–66. http://dx.doi.org/10.1142/s0129054197000045.

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Consider a scheduling problem of parallel computations in multiprocessor systems. Let a parallel program be modeled by a task graph, where vertices represent tasks and arcs the communications between tasks. An interprocessor communication time incurs when two tasks assigned to two different processors have to communicate. Such a scheduling problem has recently been studied in the literature, mostly for the case where interprocessor communication times are fully determined. In this paper, we consider the scheduling problem with communication resource constraints. More specifically, we consider

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32

Yan, Shengyu, Jibiao Zhou, and Zhuanzhuan Zhao. "Expression and Validation of Online Bus Headways considering Passenger Crowding." Journal of Advanced Transportation 2021 (September 6, 2021): 1–10. http://dx.doi.org/10.1155/2021/4738177.

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Passenger crowding in a city bus is uneven and the most crowded area always appears in the wheelbase of the carriage. The present study aimed to provide a sensitive indicator of the most crowded area to schedule bus headways online using a binocular camera sensor. The algorithm of standee density in the wheelbase area (SDWA) was given by a nonlinear regression model considering standees’ preferences for the standing area, and its goodness of fit and continuity were tested. Considering the characteristics of city bus operation, the proportion of the number of interstops determined from the SDWA

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33

Lin, Dung‐Ying, and Ching‐Lan Hsu. "A column generation algorithm for the bus driver scheduling problem." Journal of Advanced Transportation 50, no. 8 (2016): 1598–615. http://dx.doi.org/10.1002/atr.1417.

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34

Bie, Yiming, Ruru Tang, Zhiyuan Liu, and Dongfang Ma. "Mixed Scheduling Strategy for High Frequency Bus Routes With Common Stops." IEEE Access 8 (2020): 34442–54. http://dx.doi.org/10.1109/access.2020.2974740.

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35

Li, Xinghua, Tianzuo Wang, Lingjie Li, Feiyu Feng, Wei Wang, and Cheng Cheng. "Joint Optimization of Regular Charging Electric Bus Transit Network Schedule and Stationary Charger Deployment considering Partial Charging Policy and Time-of-Use Electricity Prices." Journal of Advanced Transportation 2020 (December 19, 2020): 1–16. http://dx.doi.org/10.1155/2020/8863905.

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Electric buses (EBs) have been implemented worldwide and exhibited great potential for air pollution reduction and traffic noise control. In regular charging scenarios, the deployment of charging facilities and the operational scheduling of the transit system is crucial to bus transit system management. In this paper, we proposed a joint optimization model of regular charging electric bus transit network schedule and stationary charger deployment considering partial charging policy and time-of-use electricity prices. The objective of the model is to minimize the total investment cost of the tr

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36

Cao, Zhichao, and Avishai (Avi) Ceder. "Autonomous shuttle bus service timetabling and vehicle scheduling using skip-stop tactic." Transportation Research Part C: Emerging Technologies 102 (May 2019): 370–95. http://dx.doi.org/10.1016/j.trc.2019.03.018.

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37

Ghasedi, Meisam, Mahyar Ghorbanzadeh, and Iraj Bargegol. "Robust Optimization of Bus Stop Placement Based on Dynamic Demand Using Meta Heuristic Approaches: A Case Study in a Developing Country." Transport and Telecommunication Journal 22, no. 1 (2021): 39–52. http://dx.doi.org/10.2478/ttj-2021-0004.

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Abstract The operating condition of bus transit system has not been efficient in most cities of Iran, and many management methods such as regular bus scheduling, assigning exclusive bus lanes, etc., which are necessary for increasing the efficiency of this system, were not regarded enough. Thus, achieving a method for locating the bus stops and optimizing the number of such stops based on a non-homogeneous spatial and temporal distribution of passengers as well as the local traffic patterns are important to be investigated. As such, the present study aims to investigate the modeling of a bus t

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38

LIU, Zhiqiang, Li ZHANG, Xue LV, and Jie CHEN. "Evaluation Method about Bus Scheduling Based on Discrete Hopfield Neural Network." Journal of Transportation Systems Engineering and Information Technology 11, no. 2 (2011): 77–83. http://dx.doi.org/10.1016/s1570-6672(10)60114-6.

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39

WEI, Ming, Bo SUN, and Wenzhou JIN. "A Bi-level Programming Model for Uncertain Regional Bus Scheduling Problems." Journal of Transportation Systems Engineering and Information Technology 13, no. 4 (2013): 106–12. http://dx.doi.org/10.1016/s1570-6672(13)60120-8.

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40

Luo, Qingyu, Jinge Song, Tianyao Zheng, and Lili Yang. "Passenger evacuation at a malfunctioning urban rail station based on interdependent networks." International Journal of Modern Physics C 30, no. 11 (2019): 1950098. http://dx.doi.org/10.1142/s0129183119500980.

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In the context of urban rail evacuation analysis, little work addresses the mechanism of passenger propagation between urban rail and bus systems. This paper attempts to quantify the propagation mechanism to make evacuation scheduling reasonable and effective. First, it provides a set of methods for characterizing the law of passenger propagation in the region affected by a rail station failure, based on the theory of interdependent networks. An interdependent public transport network between the rail transit and buses in the affected region is constructed using the methods of Space L and Spac

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41

Wang, Xiaoqiang, and Ren Qing-dao-er-ji. "Application of optimized genetic algorithm based on big data in bus dynamic scheduling." Cluster Computing 22, S6 (2018): 15439–46. http://dx.doi.org/10.1007/s10586-018-2625-x.

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42

Arif, Syed Muhammad, Tek Tjing Lie, Boon Chong Seet, Syed Muhammad Ahsan, and Hassan Abbas Khan. "Plug-In Electric Bus Depot Charging with PV and ESS and Their Impact on LV Feeder." Energies 13, no. 9 (2020): 2139. http://dx.doi.org/10.3390/en13092139.

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Plug-in electric buses (PEBs) are a promising alternative to conventional buses to provide a sustainable, economical, and efficient mode of transportation. However, electrification of public transportation leads to a phenomenon of peak load that impacts the stability of low voltage (LV) feeders. In this context, the effective integration of an energy storage system (ESS) and photovoltaic (PV) in a bus depot charging ecosystem can lead to i) peak load reduction and ii) charging cost reduction with low carbon emission. Therefore, a limited PEB charge scheduling algorithm is proposed for: i) bus

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43

Shen, Yindong, and Jiahong Xia. "Integrated bus transit scheduling for the Beijing bus group based on a unified mode of operation." International Transactions in Operational Research 16, no. 2 (2009): 227–42. http://dx.doi.org/10.1111/j.1475-3995.2009.00673.x.

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44

Min, W. H., and B. Veeravalli. "Aligning Biological Sequences on Distributed Bus Networks: A Divisible Load Scheduling Approach." IEEE Transactions on Information Technology in Biomedicine 9, no. 4 (2005): 489–501. http://dx.doi.org/10.1109/titb.2005.855559.

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45

Mukherjee, B. "Performance of a dual-bus unidirectional broadcast network operating under probabilistic scheduling strategy." ACM SIGMETRICS Performance Evaluation Review 17, no. 1 (1989): 118–26. http://dx.doi.org/10.1145/75372.75385.

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46

Ma, Jihui, Yang Yang, Wei Guan, et al. "Large-Scale Demand Driven Design of a Customized Bus Network: A Methodological Framework and Beijing Case Study." Journal of Advanced Transportation 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3865701.

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In recent years, an innovative public transportation (PT) mode known as the customized bus (CB) has been proposed and implemented in many cities in China to efficiently and effectively shift private car users to PT to alleviate traffic congestion and traffic-related environmental pollution. The route network design activity plays an important role in the CB operation planning process because it serves as the basis for other operation planning activities, for example, timetable development, vehicle scheduling, and crew scheduling. In this paper, according to the demand characteristics and opera

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47

Bharadwaj, V., Xiaolin Li, and Chi Chung Ko. "Efficient partitioning and scheduling of computer vision and image processing data on bus networks using divisible load analysis." Image and Vision Computing 18, no. 11 (2000): 919–38. http://dx.doi.org/10.1016/s0262-8856(99)00085-2.

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48

Wang, Yuan, Dongxiang Zhang, Lu Hu, Yang Yang, and Loo Hay Lee. "A Data-Driven and Optimal Bus Scheduling Model With Time-Dependent Traffic and Demand." IEEE Transactions on Intelligent Transportation Systems 18, no. 9 (2017): 2443–52. http://dx.doi.org/10.1109/tits.2016.2644725.

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49

Hajibandeh, Neda, Miadreza Shafie-khah, Sobhan Badakhshan, Jamshid Aghaei, Sílvio Mariano, and João Catalão. "Multi-Objective Market Clearing Model with an Autonomous Demand Response Scheme." Energies 12, no. 7 (2019): 1261. http://dx.doi.org/10.3390/en12071261.

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Demand response (DR) is known as a key solution in modern power systems and electricity markets for mitigating wind power uncertainties. However, effective incorporation of DR into power system operation scheduling needs knowledge of the price–elastic demand curve that relies on several factors such as estimation of a customer’s elasticity as well as their participation level in DR programs. To overcome this challenge, this paper proposes a novel autonomous DR scheme without prediction of the price–elastic demand curve so that the DR providers apply their selected load profiles ranked in the h

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50

Wang, Chunlu, Hongyi Shi, and Xingquan Zuo. "A multi-objective genetic algorithm based approach for dynamical bus vehicles scheduling under traffic congestion." Swarm and Evolutionary Computation 54 (May 2020): 100667. http://dx.doi.org/10.1016/j.swevo.2020.100667.

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