Combinational Scheduling Model Considering Multiple Vehicle Sizes

Abstract

Urban public transport is an effective way to solve urban traffic problems and promote sustainable development of urban traffic. A scientific operation scheduling system has an important guiding significance for optimizing the configuration of urban public transport capacity resources, improving the level of operation organization and management, and providing for the sustainability of the transportation system. According to the inhomogeneous distribution of passenger flow along transit lines, this study develops a combinational scheduling model in which the enterprise supplies zonal service based on regular service. The objective function minimizes the sum of passenger travel cost and operation cost, and the simulated annealing algorithm is designed to solve the optimization model. This paper abstracts an ideal example by taking a real-world case of Bus Line 131 in Lanzhou, China. The numerical example is used to verify the validity of the model and algorithm. Results show that the combinational operation scheme can effectively satisfy passengers’ demand and reduce the total cost by 7.03% in comparison with the regular operation system. The optimal combinational system with the lowest total cost can increase the vehicle load factor and improve the utilization ratio.

A Comprehensive Survey and Future Directions on Optimising Sustainable Urban Mobility

“…For the network design, studies sought to determine public transport service area [147], bus routes [76,90,195], route length [68,74], number of lines [74], and stop/station location [74,76,199]. For time and vehicle related modelling, studies focused on frequencies setting problems [53], vehicle scheduling [62,67,110,185], and timetabling strategies [79,169]. Some authors modelled more than one aspect, for example routes and frequencies setting [194], routes and vehicle scheduling of a customised bus service [89], routes and timetabling [187], frequencies setting and timetable development [193], and service area, frequencies, and vehicle scheduling [164]; Nesheli et al [127] focused on real-time operation aspects of bus systems, as vehicle holding time and boarding limit at stops, and also deciding if the vehicle could skip certain stops.…”

“…For multi-objective or multi level problems, the most frequent combination of functions was minimising passenger's travel time and operators' costs. Nevertheless, the whole range of functions aimed at: minimising costs or maximising revenue [53,63,67,83,89,94,110,148,150,164,179,186,[194][195][196]199], minimising unsatisfied demand costs [110,196] and user costs to access the service [199], maximising covered demand or ridership [52,62,76,78,153], maximising accessibility [148] and network fairness [53], and minimising distances [78], fleet size [169], vehicle's transportation time [82], passengers' waiting time [63,79,82,169], passengers' travel time [51,90,169,193,194], vehicle mileage [193], energy consumption [79], vulnerability [51], intersection delay [70], train infeasibility [63] and train delay [172]. Lastly, only two studies considered all the three perspe...…”

Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies

“…Timetabling design and vehicle scheduling problems have been extensively studied mainly from four aspects [4][5][6][7][8], which are all-stop scheduling with conventional buses, all-stop scheduling with EBs, conventional buses under multi-mode operation strategies, and the charging strategies of EBs [9][10][11][12][13][14].…”

Minimizing Makespan in A Two-Machine Flowshop Problem with Processing Time Linearly Dependent on Job Waiting Time

“…It is beneficial for environmental sustainability. For some other scheduling problems related to sustainability, the reader is referred to the works of [2][3][4][5][6][7][8].…”

“…0 < α [5] = 0.1 < 1. C [5],2 = C [4],2 +b[5] +α[5] •(C[5],1 + d[5] − C[4],2 ) = 16 + 1 + 0.1(11 + 1−16) = 16.6.Go toStep 2.…”