بهینه سازی چند هدفه متوسط زمان سفر در خطوط مترو با در نظر گرفتن توام اثرات مکانی و پراکندگی

نوع مقاله: پژوهشی

نویسندگان

1 دانشگاه قم

2 دانشگاه تربیت مدرس

چکیده

تنظیم زمان‌های پیشروی قطارها (فاصله زمانی میان شروع حرکت دو قطار متوالی) یک مسئله مهم برای شرکت‌های راه‌آهن شهری محسوب می‌گردد. در این مسئله با دو هدف متضاد متوسط زمان سفر مسافران و نرخ پربودن واگن مواجه هستیم. تا کنون روش‌های مختلف بهینه‌سازی چندهدفه برای حل این مسئله مورد بررسی قرار گرفته است اما هیچکدام از این رویکردها واریانس اهداف و همچنین همبستگی میان آن‌ها را در فرآیند بهینه‌سازی لحاظ ننموده‌اند. از این رو، این مطالعه یک رویکرد مدل‌سازی و حل بر اساس شبیه‌سازی گسسته – پیشامد و متدولوژی سطح پاسخ برای این مسئله ارائه می‌نماید که نه‌تنها میانگین اهداف را در یک ناحیه مطلوب قرار می‌دهد بلکه همچنین سعی می‌نماید حساسیت آن‌ها را نسبت به متغیرهای اختلال کمینه کند و در عین حال همبستگی میان اهداف را نیز در نظر ‌گیرد. به منظور بررسی عملکرد رویکرد پیشنهادی، خط 4 مترو تهران مورد ارزیابی قرار گرفته است. نتایج بدست آمده برتری رویکرد پیشنهادی را نسبت به تکنیک‌های موجود نشان می‌دهد

کلیدواژه‌ها


عنوان مقاله [English]

Multi-objective optimization of average travel time for a metro line by considering both location and dispersion effects

نویسندگان [English]

  • Ali Salmasnia 1
  • Seyed Amir Hamed Hosseinzadeh 2
  • Behnam Abdzadeh 1
چکیده [English]

Headways (i.e. the time period between the departure times of two consecutive transportation vehicles) is an important issue for urban railway companies. In this problem we are facing with two conflicting objectives, average passenger travel time and rate of carriage fullness. Until now different multi-response optimization procedure for solving this problem is studied but these approaches do not consider responses variance and covariance between them in optimization process. Therefore this research presents a modeling and solution approach based on discrete-event simulation and response surface methodology that not only puts average of response in satisfactory region but also try to minimize the variance of responses relative to noise variable and also consider covariance between them. In order to evaluate the performance of the proposed approach, Tehran metro line 4 has been assessed. The results show that the proposed approach is superior to existing techniques.

کلیدواژه‌ها [English]

  • Average travel time
  • Optimization
  • Simulation
  • Response surface methodology
  • Robust parameter design

[1] W.O. Assis, B.E.A. Milani, "Generation of optimal schedules for metro lines using model predictive control", Automatica, Vol. 40, No. 8, 2004, pp. 1397–1404.
[2] G. Gentile, S. Nguyen, S. Pallottino, "Route choice on transit networks with online information at stops", Transportation Science, Vol. 39, No. 3, 2005, pp. 289–297.
[3] J. Hu, X. Shi, J. Song, Y. Xu, "Optimal design for urban mass transit network based on evolutionary algorithms", Lecture Notes in Computer Science, Vol. 3611, 2005, pp.1089–1100.
[4] J.F. Guan, H. Yang, S.C. Wirasinghe, "Simultaneous optimization of transit line configuration and passenger line assignment", Transportation Research Part B, Vol. 40, No. 10, 2006, pp. 885–902.
[5] F. Zhao, X. Zeng, "Optimization of transit network layout and headway with a combined genetic algorithm and simulated annealing method", Engineering Optimization, Vol. 38, No. 6, 2006, pp. 701–722.
[6] F. Zhao, X. Zeng, "Optimization of transit route network, vehicle headways and timetables for large-scale transit networks", European Journal of Operational Research, Vol. 186, No. 2, 2008, pp. 841–855.
[7] M. Flamini, D. Pacciarelli, "Real time management of a metro rail terminus", European Journal of Operational Research, Vol. 189, No. 3, 2008, pp. 746–761.
[ 8 [ غ. شفابخش، ا. شاهحسینی، "آنالیز حساسیت جریان ترافیک بزرگراههای برونشهری نسبت به عوامل هندسی مسیر"، مجله مدلسازی
در مهندسی، دوره 5 ، شماره 19 ، 1388 ، صفحه 35 - 23 .
[9] G. Gutiérrez-Jarpa, C. Obreque, G. Laporte, and V. Marianov, "Rapid transit network design for optimal cost and origin–destination demand capture", Computers & Operations Research, Vol. 40, No. 12, 2013, pp.3000-3009.
[10] G. Gutiérrez-Jarpa, G. Laporte, V. Marianov, & L. Moccia, "Multi-objective rapid transit network design with modal competition: The case of Concepción, Chile", Computers & Operations Research, Vol. 78, 2017, pp. 27-43.
[11] E. Hassannayebi, A. Sajedinejad, & S. Mardani, "Urban rail transit planning using a two-stage simulation-based optimization approach", Simulation Modelling Practice and Theory, Vol. 49, 2014, pp.151-166.
[12] S.M. Amiripour, A.A. Ceder, and A.S. Mohaymany, "Designing large-scale bus network with seasonal variations of demand", Transportation Research Part C: Emerging Technologies, VOL. 48, 2014, pp.322-338.
[ 13 [ آ. ساجدینژاد، ع. حسن نایبی، ج. حیدری، ج. رزمی، "مدلسازی تردد ناوگان اتوبوسرانی شهری بر اساس دادههای موقعیت مکانی
موردکاوی: خطوط اتوبوسرانی شهری تهران"، مجله مدلسازی در مهندسی، دوره 13 ، شماره 42 ، ص 118 - 103 .
[14] S. Li, L. Yang, Z. Gao, and K. Li, "Robust train regulation for metro lines with stochastic passenger arrival flow", Information Sciences, Vol. 373, 2016, pp.287-307.
[15] D. Canca, A. De-Los-Santos, G. Laporte, and J.A. Mesa, "An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem", Computers & Operations Research, Vol. 78, 2017, pp.1-14.
[16] R.H. Myers, D.C. Montgomery, "Response Surface Methodology: Process and Product Optimization using Designed Experiments", John Wiley and Sons, Inc., New York, 1995.
[17] G.E.P. Box, K.B. Wilson, "On the experimental attainment of optimum conditions", Journal of Royal Statistical Society Series B, Vol. 13, No. 1, 1951, pp. 1–38.
[18] E.D. Castillo, D.C. Montgomery, D.R. McCarvile, "Modified desirability functions for multiple response optimization", Journal of Quality Technology, Vol. 28, No. 3, 1996, pp. 337–345.
[19] A. D’Angelo, M. Gastaldi, N. Levialdi, "Performance analysis of a flexible manufacturing system: A statistical approach", International Journal of Production Economics, Vol. 56–57, 1998, pp. 47–59.
[20] A. Gharbi, J.P. Kenne, "Production and preventive maintenance rates control for a manufacturing system: An experimental design approach", International Journal of Production Economics, Vol. 65, No. 3, 2000, pp.275–287.
[21] M.D.L.A. Irizarry, J.R. Wilson, J. Trevino, "A flexible simulation tool for manufacturing-cell design. II: Response surface analysis and case study", IIE Transactions, Vol. 33, No. 10, 2001, pp.837–846.
[22] G. Miro Quesada, E. Del Castillo, "A Dual Response Approach to the Multivariate Robust Parameter Design Problem", Technometrics, Vol. 46, No. 2, 2004, pp. 176-187.
[23] D.C. Montgomery, "Design and Analysis of Experiments", John Wiley and Sons, Inc., New York, 2001.
[24] R.H. Myers, D.C. Montgomery, "Response surface methodology", 2nd edition., John Wiley& Sons, New York, 2002.
[25] E. Del Castillo, "Process optimization, A statistical approach", Springer Science+ Business Media LLC,ISBN 978-0-387 71435-6, 2007.
[26] S.K. Fan, "A generalized global optimization algorithm for dual response systems", Journal of Quality Technology, Vol. 32, No. 4, 2000, pp. 444-456.
[27] G.C. Derringer, R. Suich, "Simultaneous optimization of several response variables", Journal of Quality Technology, Vol. 12, No. 4, 1980, pp. 214–219.
[28] O. Yalcınkaya, G. M. Bayhan, "Modelling and optimization of average travel time for a metro line by simulation and response surface methodology", European Journal of Operational Research, Vol. 196, No. 1, 2009, pp. 225–233.