دانلود مقاله ISI انگلیسی شماره 43216
عنوان فارسی مقاله

یک معماری MAS برای تغییر زمان بیدرنگ و یادگیری پویا اعمال شده برای حمل و نقل راه آهن

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
43216 2015 19 صفحه PDF سفارش دهید 13920 کلمه
خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.
عنوان انگلیسی
A MAS architecture for dynamic, realtime rescheduling and learning applied to railway transportation
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Expert Systems with Applications, Volume 42, Issue 5, 1 April 2015, Pages 2638–2656

کلمات کلیدی
جنگ - بن بست - قطع - اولویت پویا - وضوح - سیستم چندعامله (MAS) - ترافیک راه آهن دو جهته تک آهنگ - تغییر زمان زمان واقعی پویا - یادگیری
پیش نمایش مقاله
پیش نمایش مقاله یک معماری MAS برای تغییر زمان بیدرنگ و یادگیری پویا اعمال شده برای حمل و نقل راه آهن

چکیده انگلیسی

Rescheduling disrupted railway traffic is computationally hard even for small problem instances. Disruptions may not be known beforehand and can manifest themselves even when trains are en-route, and they are usually resolved by human experts. Wide geographical distribution, a dynamically changing environment, complex interdependencies between multiple components, operational criticality and uncertainty being characteristic of railway transportation, human resolutions are inconsistent, scale-inefficient and potentially infeasible with deadlocks. We present a multi-agent system (MAS) model for dynamic and real-time rescheduling (DRR) of bi-directional railway traffic on a single track in this paper. A computational framework to dynamically dispatch the disrupted trains in real-time, based on instantaneous system parameters and to reschedule conflicting trains with inherent deadlock avoidance is incorporated in the agents’ model. A learning architecture is implemented as a proof-of concept to resolve disruptions quickly and to enhance autonomy. The model is evaluated against integer optimal solutions generated by a Mixed-Integer Linear Programming (MILP) model using realistic data. Detailed discussions on architecture, implementation using JADE (Java Agent DEvelopment) toolkit, experimental results, performance analysis, evaluation of the model, insights and limitations are reported. The numerical performance measures of the model are total weighted delay of all trains at their destination terminals and computational time for resolution. The distinguishing research contributions in this paper are a MAS architecture for railway rescheduling, dynamic dispatch priority assignment using bidding and a learning procedure that enhances autonomy.

خرید مقاله
پس از پرداخت، فوراً می توانید مقاله را دانلود فرمایید.