Developing Just-In-Time and Network Flow Models For Urban Snow Removal Problem

Authors

  • Zeinab Bandpey Northern Virginia Community college https://orcid.org/0000-0001-7670-4790
  • Ahlam Tannouri Morgan State University
  • Isabelle Kemajou Brown Morgan State University
  • Asamoah Nkwanta Morgan State University

DOI:

https://doi.org/10.29020/nybg.ejpam.v14i4.4082

Keywords:

Just InTime, Urban Snow Removal, MATLAB®, Network flow, Multi-objective optimization

Abstract

Winter urban traffic issues and performance present critical problems in large cities and metropolitan areas. In urban areas, there is a critical need for efficient methods for snow removal while considering the impact on the transportation infrastructure of a city. Several proposals and approaches on modeling snow removal that heuristically deals with finding solutions to this wideopen problem have been studied and published in recent years. In this paper, we developed a new mathematical model that uses the Just-In-Time (JIT) method to optimize a transportation problem. The paper’s main objective is to design a model for establishing efficient truck routes for snow removal by optimizing cost and time, which implicitly minimizes the impact on a city’s transportation infrastructure. We applied the network flow problem for snow removal to minimize time and cost of cleaning urban streets just in time. We ran several simulations of the models using the MATLAB®.

Author Biographies

  • Zeinab Bandpey, Northern Virginia Community college
    Associate Professor, department of Mathematics
  • Ahlam Tannouri, Morgan State University
    Department of Mathematics
  • Isabelle Kemajou Brown, Morgan State University
    Acturial Science and Mathematics
  • Asamoah Nkwanta, Morgan State University
    Chair of Department of Mathematics

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Published

2021-11-10

Issue

Section

Nonlinear Analysis

How to Cite

Developing Just-In-Time and Network Flow Models For Urban Snow Removal Problem. (2021). European Journal of Pure and Applied Mathematics, 14(4), 1112-1131. https://doi.org/10.29020/nybg.ejpam.v14i4.4082

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