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Schedule optimization under fuzzy constraints of vehicle capacity

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Abstract

The objective of designing timetables for public transportation is twofold: to ensure an efficient use of limited resources and to provide a comfortable ride for passengers. Two models for timetable optimization are investigated in this study. Model 1 uses a crisp constraint on the rate of vehicle capacity usage. Model 2 improves on model 1 by translating the crisp constraint into a fuzzy goal representing passenger satisfaction, and a fuzzy constraint, representing the extent of vehicle usage. Both, the fuzzy goal and the fuzzy constraint, are fuzzy sets on the number of on-board passengers. Heuristic methods together with linear programming are proposed for finding the optimal headway. Model 1 selects the largest time interval under the bound on vehicle size. The set of optimal time intervals in model 2 is decided by the simultaneous level cuts of the fuzzy goal and constraint. Experimental results show that fuzzy-set based model 2 is the most flexible and effective way to generate an optimal timetable.

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Notes

  1. In China, \(\mathcal {B}\) is defined very precisely as the number of seats plus the effective standing area on the bus(sq.m.) times 8 (this assumes that up to 8 people can stand on a square-meter surface).

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Acknowledgements

Yanan Zhang’s work for this study was supported by the China Scholarship Council (Grant No. 201506250051) while visiting the MLCI Laboratory led by Anca Ralescu, in the EECS Department, College of Engineering and Applied Science, University of Cincinnati.

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Authors and Affiliations

  1. School of Computer Software, Tianjin University, Tianjin, 300350, China

    Yanan Zhang, Zhaopeng Meng & Yan Zheng

  2. EECS Department, University of Cincinnati, Cincinnati, OH, 45221-0030, USA

    Anca Ralescu

Authors
  1. Yanan Zhang
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  2. Zhaopeng Meng
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  3. Yan Zheng
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  4. Anca Ralescu
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Correspondence to Anca Ralescu.

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Zhang, Y., Meng, Z., Zheng, Y. et al. Schedule optimization under fuzzy constraints of vehicle capacity. Fuzzy Optim Decis Making 18, 131–150 (2019). https://doi.org/10.1007/s10700-018-9289-0

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  • DOI: https://doi.org/10.1007/s10700-018-9289-0

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