Model and algorithm for resolving regional bus scheduling problems with fuzzy travel times

Regional bus scheduling is necessary to urban public transport that is complicated by the necessity of assigning trips that belong to several routes to buses located at different depots while reducing fleet size and operating costs. Considering the reality of emergencies that may interfere with the ability of vehicles to complete trips on time, it is reasonable to use fuzzy numbers to express uncertain delay times. Based on this idea, this paper proposes a chance-constrained programming model of regional bus scheduling that will reflect additional constraints such as the capacities of related depots and fueling needs. The objective of this paper is to maximize utilization of fleet vehicles. To overcome the defect of premature convergence in the particle swarm optimization algorithm (PSO), an improved PSO is proposed by using an organic fusion with group search optimization. Finally, an example demonstrates the correctness and effectiveness of the model and algorithm.

[1]  Ming Wei,et al.  A Bi-level Programming Model for Uncertain Regional Bus Scheduling Problems , 2013 .

[2]  Dennis Huisman,et al.  A Robust Solution Approach to the Dynamic Vehicle Scheduling Problem , 2004, Transp. Sci..

[3]  Leena Suhl,et al.  A time-space network based exact optimization model for multi-depot bus scheduling , 2006, Eur. J. Oper. Res..

[4]  Jinsheng Shen,et al.  Regional Bus Operation Bi-level Programming Model Integrating Timetabling and Vehicle Scheduling , 2007 .

[5]  Yang Zhilian.  Overview of particle swarm optimization , 2003 .

[6]  Alan A. Bertossi,et al.  On some matching problems arising in vehicle scheduling models , 1987, Networks.

[7]  Celso C. Ribeiro,et al.  A Column Generation Approach to the Multiple-Depot Vehicle Scheduling Problem , 1991, Oper. Res..

[8]  Juanjuan He,et al.  A Hybrid Distribution Algorithm Based on Membrane Computing for Solving the Multiobjective Multiple Traveling Salesman Problem , 2015, Fundam. Informaticae.

[9]  Sun Bo.  Model and Algorithm for Regional Bus Scheduling with Stochastic Travel Time , 2011 .

[10]  Mohamadreza Banihashemi,et al.  HEURISTIC APPROACHES FOR SOLVING LARGE-SCALE BUS TRANSIT VEHICLE SCHEDULING PROBLEM WITH ROUTE TIME CONSTRAINTS , 2002 .

[11]  Wang Hai-xing.  Research on Situation and Developing Trend for Optimization Theory and Method of Public Transit Vehicle Scheduling Problem , 2008 .

[12]  Denis Borenstein,et al.  A Lagrangian Heuristic for the Real-Time Vehicle Rescheduling Problem , 2009 .

[13]  Ming Wei,et al.  Model and Algorithm of Regional Bus Scheduling with Grey Travel Time , 2012 .

[14]  Q. Henry Wu,et al.  Group Search Optimizer: An Optimization Algorithm Inspired by Animal Searching Behavior , 2009, IEEE Transactions on Evolutionary Computation.

[15]  J. Watada,et al.  Fuzzy Stochastic Optimization: Theory, Models and Applications , 2012 .

[16]  Dana H. Ballard,et al.  An introduction to natural computation , 2000, Complex adaptive systems.

[17]  Haixing Wang,et al.  Heuristic approaches for solving transit vehicle scheduling problem with route and fueling time constraints , 2007, Appl. Math. Comput..

[18]  C. Collin,et al.  An Introduction to Natural Computation , 1998, Trends in Cognitive Sciences.

[19]  Dennis Huisman,et al.  A solution approach for dynamic vehicle and crew scheduling , 2004, Eur. J. Oper. Res..

[20]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[21]  Zhigang Yao,et al.  Clustering Taxi Quantity Regulatory Levels of Chinese Capital Cities , 2012 .

[22]  Shahaboddin Shamshirband,et al.  GELS-GA: Hybrid metaheuristic algorithm for solving Multiple Travelling Salesman Problem , 2014, 2014 14th International Conference on Intelligent Systems Design and Applications.