RYNSORD: a novel decentralized algorithm for railway networks with "soft reservation"

Lee, T.S.; Ghosh, Sumit

doi:10.1109/25.728526

Cited by 13 publications

(6 citation statements)

References 15 publications

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“…The potential benefits of ADDM systems include scalability, high throughput, and robustness. Examples of ADDM systems include the distributed routing algorithm for ATM networks [22], YADDES [23], NO-VAHID [24], DICAF [25], DARYN [26], RYNSORD [27], MFAD [28], and ATOS [29]. The ATOS: Autonomous Decentralized Transport Operation System, controls the world's largest transportation system-East Japan Railway Company.…”

Section: Formal Definition Of Stability In Addm Systemsmentioning

confidence: 99%

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The concept of "stability" in asynchronous distributed decision-making systems

Lee

Ghosh

2000

IEEE Trans. Syst., Man, Cybern. B

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Asynchronous distributed decision-making (ADDM) systems constitute a special class of distributed problems and are characterized as large, complex systems wherein the principal elements are the geographically dispersed entities that communicate among themselves, asynchronously, through message passing and are permitted autonomy in local decision making. Such systems generally offer significant advantages over the traditional, centralized algorithms in the form of concurrency, scalability, high throughput, efficiency, low vulnerability to catastrophic failures, and robustness. A fundamental property of ADDM systems is stability that refers to their behavior under representative perturbations to their operating environments, given that such systems are intended to be real, complex, and to some extent, mission-critical, and are subject to unexpected changes in their operating conditions. This paper introduces the concept of stability in ADDM systems and proposes an intuitive yet practical and usable definition that is inspired by those used in control systems and physics. An ADDM system is defined as a stable system if it returns to a steady state in finite time, following perturbation, provided that it is initiated in a steady state. Equilibrium or steady state is defined through placing bounds on the measured error in the system. Where the final steady state is equivalent to the initial one, a system is referred to as strongly stable. If the final steady state is potentially worse then the initial one, a system is deemed marginally stable. When a system fails to return to steady state following the perturbation, it is unstable. The perturbations are classified as either changes in the input pattern or changes in one or more environmental characteristics of the system, such as hardware failures. For a given ADDM system, the definitions are based on the performance indices that must be judiciously identified by the system architect and are likely to be unique. To facilitate the understanding of stability in representative real-world systems, this paper reports the analysis of two basic manifestations of ADDM systems that have been reported in the literature: (1) a decentralized military command and control problem, MFAD and (2) a novel distributed algorithm with soft reservation for efficient scheduling and congestion mitigation in railway networks, RYNSORD. Stability analysis of MFAD and RYNSORD yields key stable and unstable conditions. A system determined to be stable provides the reassurance that the system will perform well under adverse conditions. In contrast, a system deemed unstable reflects the need to address key weaknesses in the system design. Thus, stability analysis is a necessary and critical step in the development of any ADDM system.

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Section: Formal Definition Of Stability In Addm Systemsmentioning

confidence: 99%

“…"A" and "B" constitute the two basic manifestations of ADDM systems. Examples of "A" include RYNSORD [27] and NOVAHID [24] while that of "B" include MFAD [28]. ADDM systems, by nature, are large-scale and complex.…”

Section: Formal Definition Of Stability In Addm Systemsmentioning

confidence: 99%

The concept of "stability" in asynchronous distributed decision-making systems

Lee

Ghosh

2000

IEEE Trans. Syst., Man, Cybern. B

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“…RYNSORD [3,4] is a novel, decentralized algorithm with soft reservation for ecient scheduling and congestion mitigation in railway networks and has been recently introduced in the literature. It has been designed and developed to explore a decentralized approach to railway management.…”

Section: Brief Overview Of Rynsordmentioning

confidence: 99%

“…Excessive use of intermediate stops can lead to poor performance, since RYNSORD's strength lies in the dynamic routing of trains to maximize eciency, optimize resource allocation and avoid congestion. For further details on RYNSORD and a discussion on the issue of validating the RYNSORD software, the reader is referred to references [3] and [4].…”

Section: Brief Overview Of Rynsordmentioning

confidence: 99%

“…A bounded function is one whose magnitude is less than some constant for all time. The application of this de®nition to RYNSORD [3,4] is straightforward in that the input may be represented by the number of trains inserted into the system, at either a single station or any set of stations, at every time instance. The output consists of the time at which each train corresponding to each input leaves the system, i.e.…”

Section: Bounded-input Bounded-output Stabilitymentioning

confidence: 99%

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Stability of RYNSORD, a decentralized algorithm for railway networks, under perturbations

Lee

Ghosh

2000

Proceedings of the Institution of Mechanical Engineers, Part F:

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RYNSORD, a novel, decentralized algorithm with soft reservation for ecient scheduling and congestion mitigation in railway networks, has been recently introduced in the literature. It is a speci®c instance of the asynchronous distributed decision-making (ADDM) class of systems, a fundamental property of which is stability. Stability refers to the behaviour of systems under representative perturbations to their environments, given that ADDM systems are intended to be real, complex and, to some extent, mission-critical systems, and are subject to unexpected changes in their operating conditions. This paper introduces an intuitive de®nition of stability for RYNSORD that re¯ects those used in control systems and physics, and presents an in-depth stability analysis of RYNSORD. The analysis utilizes three types of stability: strongly stable, marginally stable and unstable. The perturbations are classi®ed as either changes in the input pattern or changes in one or more characteristics of the system, such as hardware failures. The study utilizes a large-scale simulation of a subset of the Eastern United States railroad network. Trains are initiated at stochastically generated times at each of the stations and are bound for randomly selected destinations. In the study, ®rstly, a steady-state operating point is identi®ed. Secondly, the system is perturbed by increasing the rate of trains inserted into the system temporarily, i.e. for a ®nite interval. Thirdly, an examination of whether and when the system returns to the previous steady state yields the results of stability analysis. Performance results indicate that, while RYNSORD is strongly stable with respect to input trac rate perturbations of ®nite durations, it is marginally stable to unstable under permanent track segment and communication link failures, re¯ecting a balanced assessment of the strengths and weaknesses of RYNSORD.

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