A school bus scheduling problem

2012 ◽  
Vol 218 (2) ◽  
pp. 577-585 ◽  
Author(s):  
Byung-In Kim ◽  
Seongbae Kim ◽  
Junhyuk Park
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

scholarly journals Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

PLoS ONE ◽  
2015 ◽  
Vol 10 (7) ◽  
pp. e0132600 ◽  
Author(s):  
Xiaopan Chen ◽  
Yunfeng Kong ◽  
Lanxue Dang ◽  
Yane Hou ◽  
Xinyue Ye
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

A comparison of heuristics for a school bus scheduling problem

1986 ◽  
Vol 20 (2) ◽  
pp. 175-182 ◽  
Author(s):  
Derek Graham ◽  
Henry L.W. Nuttle
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

scholarly journals Balanced Scheduling of School Bus Trips using a Perfect Matching Heuristic

2018 ◽  
Vol 2672 (48) ◽  
pp. 1-11 ◽  
Author(s):  
Ali Shafahi ◽  
Sanaz Aliari ◽  
Ali Haghani
Keyword(s):  
Heuristic Algorithm ◽  
School Setting ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
School Bus ◽  
Heuristic Solution ◽  
Mip Model ◽  
Bus Scheduling ◽  
Minimum Number ◽  
The Cost

In the school bus scheduling problem, the main contributing factor to the cost is the number of buses needed for the operations. However, when subcontracting the pupils’ transportation, unbalanced tours can increase the costs significantly as the lengths of some tours can exceed the daily fixed driving goal and will result in over-hour charges. This paper proposes a mixed integer programming (MIP) model and a matching-based heuristic algorithm to solve the “balanced” school bus scheduling problem with fixed start times in a multi-school setting. The heuristic solution always has the minimum number of buses as it starts with a minimal number of tours and does not alter the number of tours during its balancing stage. The effectiveness of the heuristic is tested by comparing its solutions with results from solving the MIP using commercial solvers whenever solvers could find a good solution. To illustrate the performance of the MIP and the heuristic, 11 problems were examined with different numbers of trips which are all based on two real-world problems: a California case study with 54 trips and the Howard County Public School System with 994 trips. Our numerical results indicate the proposed heuristic algorithm can find reasonable solutions in a significantly shorter time. The balanced solutions of our algorithm can save up to 16% of school bus operation costs compared with the best solution found by solvers from optimizing the MIP model after 40 hours. The balancing stage of the heuristic decreases the standard deviation of the tour durations by up to 47%.

scholarly journals Solving the School Bus Routing Problem by Maximizing Trip Compatibility

2017 ◽  
Vol 2667 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Ali Shafahi ◽  
Zhongxiang Wang ◽  
Ali Haghani
Keyword(s):  
Data Sets ◽  
Scheduling Problem ◽  
School Bus ◽  
Routing Problems ◽  
Routing Problem ◽  
Routing And Scheduling ◽  
Bus Routing ◽  
Bus Scheduling ◽  
Total Travel Time ◽  
New Formulation

School bus planning is usually divided into routing and scheduling because of the complexity of solving them concurrently. However, the separation between these two steps may lead to worse solutions with higher overall costs than from solving them together. When the minimal number of trips in the routing problem is being determined, neglecting trip compatibility could increase the number of buses needed in the scheduling problem. This paper proposes a new formulation for the multischool homogeneous fleet routing problem that maximizes trip compatibility while minimizing total travel time. This plan incorporates the trip compatibility for the scheduling problem in the routing problem. A proposed heuristic algorithm for solving this problem decomposes the problem by schools. To compare the performance of the model with traditional routing problems, eight midsize data sets were generated. Importing the generated trips of the routing problems into the bus scheduling (blocking) problem shows that the proposed model can reduce the buses needed by up to 25%. A sensitivity analysis on coefficients of the model illustrates the effect of the weight of trip compatibility.

Addendum to “a comparison of heuristics for a school bus scheduling problem”

1987 ◽  
Vol 21 (4) ◽  
pp. 311-315
Author(s):  
Jeffrey C. Dauler ◽  
Henry L.W. Nuttle
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

A Meta-heuristic Algorithm to Solve the Large-Scale School Bus Scheduling Problem

2013 ◽  
Vol 15 (6) ◽  
pp. 879
Author(s):  
Xiaopan CHEN ◽  
Lanxue DANG ◽  
Yunfeng KONG
Keyword(s):  
Heuristic Algorithm ◽  
Large Scale ◽  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling

scholarly journals Correction: Exact and Metaheuristic Approaches for a Bi-Objective School Bus Scheduling Problem

PLoS ONE ◽  
2016 ◽  
Vol 11 (4) ◽  
pp. e0153614
Author(s):  
Xiaopan Chen ◽  
Yunfeng Kong ◽  
Lanxue Dang ◽  
Yane Hou ◽  
Xinyue Ye
Keyword(s):  
Scheduling Problem ◽  
School Bus ◽  
Bus Scheduling
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