Solving school bus routing problem with mixed-load allowance for multiple schools

Shang, Pan; Yang, Liya; Zeng, Ziling; Lu, Tong

doi:10.1016/j.cie.2020.106916

Cited by 16 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is due to the fact that our upper bound is derived from the lower bound solution, which is consistent with Ref. [37]. We modify the solution of the lower bound in three cases: (1) If the pick-up requests are being served by more than one EB; (2) If both the pick-up and drop-off requests are not being served; (3) If the pick-up and drop-off requests are partially served.…”

Section: A Real-world Scenariosupporting

confidence: 89%

Optimization of Electric Bus Scheduling for Mixed Passenger and Freight Flow in an Urban-Rural Transit System

Zeng

2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Transport accessibility and urban-rural connectivity are seen as critical aspects of rural economic development. In the transit network, passenger flow between urban-rural corridors demonstrates directional imbalances and low utilization of scarce resources. Freight transportation, on the other hand, lags due to poor geography, high operating costs, and scattered demand. This paper proposes a new mode of public transit that integrates passenger and freight transport, providing a carrier for logistics while compensating for the low utilization of passenger transport. In this mode, each timetabled round trip is divided into one dedicated passenger trip with high demand and one mixed-flow trip with on-demand requests. A space-time-state network is constructed considering the picking-up time window, loading/unloading service time, and electric bus energy replenishment. A mixed-integer linear programming model is developed to optimize the bus schedule that covers the travel demands and the charging requests with minimized travel costs. A Lagrangian relaxation framework with a dynamic programming algorithm and sub-gradient method is presented for problem-solving. The real-life rural-urban transport instance and a simulated network demonstrate the operation of the new mode and validate the efficiency of the proposed method. The innovative concept and the optimization framework are expected to serve as a reference for public administration to alleviate passenger and freight transportation bottlenecks in the urban-rural context.

show abstract

Section: A Real-world Scenariosupporting

confidence: 89%

Optimization of Electric Bus Scheduling for Mixed Passenger and Freight Flow in an Urban-Rural Transit System

Zeng

2023

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…They formulated the interactive mechanism between passengers and the operator in an integrated decision-making framework. Shang et al (2021) proposed a timediscretized dynamic programming-based school bus routing problem to design students commuting to different schools.…”

Section: Related Workmentioning

confidence: 99%

Environmental and social implications of incorporating carpooling service on a customized bus system

Asghari

Al-e-hashem

Rekik

2022

Computers & Operations Research

View full text Add to dashboard Cite

“…Mokhtari and Ghezavati [8] introduced a novel mixed-load SBRP and used a biobjective mixed integer linear programming formulation to model it. Shang et al [9] proposed a time-discretized multicommodity network flow model and transformed the SBRP into a quadratic 0-1 programming model with linear flow balance constraints. Ren et al [10] considered both school bus stop location and mixed-load routing problem and developed a mixed integer programming model for them.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Accurate algorithms are suitable for the problem which involves less data. Shang et al [9] decomposed the model into a series of linear multicommodity network flow subproblems when dealing with mixed-load SBRP and used dynamic programming algorithms to solve them. Owing to the continuous expansion of research data and the requirements placed upon computational efficiency, heuristic algorithms are a current research hotspot; examples of such algorithms include the tabu search, ant colony, and genetic algorithms, as well as PSO.…”

Section: Literature Reviewmentioning

confidence: 99%

Dynamic Path Optimization in Sharing Mode to Relieve Urban Traffic Congestion

Chen

Zhang

2021

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

In recent years, a new mode of transportation, referred to as “shared commuter buses,” has been introduced, which can considerably reduce the dependence of office workers on private cars and relieve urban traffic congestion, but is not widely used owing to efficiency issues. Improving the efficiency of shared commuter buses was set as a goal, and in this study, several research objects were selected to optimize the driving routes of shared commuter buses. The problem of stop location is combined with dynamic route optimization; the “shortest walking distance” model is established to select the most appropriate location for stops. The vehicle routing problem model and “key point updating strategy” are employed to plan routes with real-time traffic information. Finally, we conduct an empirical study to validate our conclusions. The results show that both the model and algorithm are reliable and effective; thus, travel efficiency can be effectively improved and traffic jams can be alleviated.

show abstract

Solving school bus routing problem with mixed-load allowance for multiple schools

Cited by 16 publications

References 30 publications

Optimization of Electric Bus Scheduling for Mixed Passenger and Freight Flow in an Urban-Rural Transit System

Optimization of Electric Bus Scheduling for Mixed Passenger and Freight Flow in an Urban-Rural Transit System

Environmental and social implications of incorporating carpooling service on a customized bus system

Dynamic Path Optimization in Sharing Mode to Relieve Urban Traffic Congestion

Contact Info

Product

Resources

About