761 research outputs found
A Matheuristic for Integrated Timetabling and Vehicle Scheduling
Planning a public transportation system is a complex process, which is usually broken down in several phases, performed in sequence. Most often, the trips required to cover a service with the desired frequency (headway) are decided early on, while the vehicles needed to cover these trips are determined at a later stage. This potentially leads to requiring a larger number of vehicles (and, therefore, drivers) that would be possible if the two decisions were performed simultaneously. We propose a multicommodity-flow type model for integrated timetabling and vehicle scheduling. Since the model is large-scale and cannot be solved by off-the-shelf tools with the efficiency required by planners, we propose a diving-type matheuristic approach for the problem. We report on the efficiency and effectiveness of two variants of the proposed approach, differing on how the continuous relaxation of the problem is solved, to tackle real-world instances of bus transport planning problem originating from customers of M.A.I.O.R., a leading company providing services and advanced decision-support systems to public transport authorities and operators. The results show that the approach can be used to aid even experienced planners in either obtaining better solutions, or obtaining them faster and with less effort, or both
Intermodal Transfer Coordination in Logistic Networks
Increasing awareness that globalization and information technology affect the patterns of transport and logistic activities has increased interest in the integration of intermodal transport resources. There are many significant advantages provided by integration of multiple transport schedules, such as: (1) Eliminating direct routes connecting all origin-destinations pairs and concentrating cargos on major routes; (2) improving the utilization of existing transportation infrastructure; (3) reducing the requirements for warehouses and storage areas due to poor connections, and (4) reducing other impacts including traffic congestion, fuel consumption and emissions.
This dissertation examines a series of optimization problems for transfer coordination in intermodal and intra-modal logistic networks. The first optimization model is developed for coordinating vehicle schedules and cargo transfers at freight terminals, in order to improve system operational efficiency. A mixed integer nonlinear programming problem (MINLP) within the studied multi-mode, multi-hub, and multi-commodity network is formulated and solved by using sequential quadratic programming (SQP), genetic algorithms (GA) and a hybrid GA-SQP heuristic algorithm. This is done primarily by optimizing service frequencies and slack times for system coordination, while also considering loading and unloading, storage and cargo processing operations at the transfer terminals. Through a series of case studies, the model has shown its ability to optimize service frequencies (or headways) and slack times based on given input information.
The second model is developed for countering schedule disruptions within intermodal freight systems operating in time-dependent, stochastic and dynamic environments. When routine disruptions occur (e.g. traffic congestion, vehicle failures or demand fluctuations) in pre-planned intermodal timed-transfer systems, the proposed dispatching control method determines through an optimization process whether each ready outbound vehicle should be dispatched immediately or held waiting for some late incoming vehicles with connecting freight. An additional sub-model is developed to deal with the freight left over due to missed transfers.
During the phases of disruption responses, alleviations and management, the proposed real-time control model may also consider the propagation of delays at further downstream terminals. For attenuating delay propagations, an integrated dispatching control model and an analysis of sensitivity to slack times are presented
Feeder bus service design under spatially heterogeneous demand
In rapidly sprawling urban areas and booming intercity express rail networks,
efficiently designed feeder bus systems are more essential than ever to
transport passengers to and from trunk-line rail terminals. When the feeder
service region is sufficiently large, the spatial heterogeneity in demand
distribution must be considered. This paper develops continuous approximation
models for optimizing a heterogeneous fixed-route feeder network in a
rectangular service region next to a rail terminal. Our work enhances previous
studies by: (i) optimizing heterogeneous stop spacings along with line spacings
and headways; (ii) accounting for passenger boarding and alighting numbers on
bus dwell times and patron transfer delays at the rail terminal; and (iii)
examining the advantages of asymmetric coordination between trunk and feeder
schedules in both service directions. To tackle the increased modeling
complexity, we introduce a semi-analytical method that combines analytically
derived properties of the optimal solution with an iterative search algorithm.
Local transit agencies can readily utilize this approach to design a real
fixed-route feeder system. This paper reveals many findings and insights not
previously reported. For instance, integrating the heterogeneous stop spacing
optimization further reduces the system cost (by 4% under specific operating
conditions). The cost savings increase with demand heterogeneity but decrease
with the demand rate and service region size. Choosing the layout of feeder
lines where buses pick up and drop off passengers along the service region's
shorter side also significantly lowers the system cost (by 6% when the service
region's aspect ratio is 1 to 2). Furthermore, coordinating trunk and feeder
schedules in both service directions yields an additional cost saving of up to
20%.Comment: 30 pages, 9 Figures, 8 Table
Ant Colony Optimisation for Dynamic and Dynamic Multi-objective Railway Rescheduling Problems
Recovering the timetable after a delay is essential to the smooth and efficient operation
of the railways for both passengers and railway operators. Most current
railway rescheduling research concentrates on static problems where all delays are
known about in advance. However, due to the unpredictable nature of the railway
system, it is possible that further unforeseen incidents could occur while the trains
are running to the new rescheduled timetable. This will change the problem, making
it a dynamic problem that changes over time. The aim of this work is to investigate
the application of ant colony optimisation (ACO) to dynamic and dynamic multiobjective
railway rescheduling problems. ACO is a promising approach for dynamic
combinatorial optimisation problems as its inbuilt mechanisms allow it to adapt to
the new environment while retaining potentially useful information from the previous
environment. In addition, ACO is able to handle multi-objective problems by
the addition of multiple colonies and/or multiple pheromone and heuristic matrices.
The contributions of this work are the development of a junction simulator to
model unique dynamic and multi-objective railway rescheduling problems and an
investigation into the application of ACO algorithms to solve those problems. A
further contribution is the development of a unique two-colony ACO framework to
solve the separate problems of platform reallocation and train resequencing at a UK
railway station in dynamic delay scenarios.
Results showed that ACO can be e
ectively applied to the rescheduling of trains
in both dynamic and dynamic multi-objective rescheduling problems. In the dynamic
junction rescheduling problem ACO outperformed First Come First Served
(FCFS), while in the dynamic multi-objective rescheduling problem ACO outperformed
FCFS and Non-dominated Sorting Genetic Algorithm II (NSGA-II), a stateof-
the-art multi-objective algorithm. When considering platform reallocation and
rescheduling in dynamic environments, ACO outperformed Variable Neighbourhood
Search (VNS), Tabu Search (TS) and running with no rescheduling algorithm. These
results suggest that ACO shows promise for the rescheduling of trains in both dynamic
and dynamic multi-objective environments.Engineering and Physical Sciences Research Council (EPSRC
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