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Solving the Electric Bus Scheduling Problem by an Integrated Flow and Set Partitioning Approach
(2024)
Attractive and cost-efficient public transport requires solving computationally difficult optimization problems from network design to crew rostering. While great progress has been made in many areas, new requirements to handle increasingly complex constraints are constantly coming up. One such challenge is a new type of resource constraints that are used to deal with the state-of-charge of battery-electric vehicles, which have limited driving ranges and need to be recharged in-service. Resource constrained vehicle scheduling problems can classically be modelled in terms of either a resource constrained (multi-commodity) flow problem or in terms of a path-based set partition problem. We demonstrate how a novel integrated version of both formulations can be leveraged to solve resource constrained vehicle scheduling with replenishment in general and the electric bus scheduling problem in particular by Lagrangian relaxation and the proximal bundle method.
Lagrangean Relaxations and Subgradient Methods for Multiple-Depot Vehicle Scheduling Problems
(1996)
This paper presents two Lagrangean relaxation approaches for the {\em NP}-hard multiple-depot vehicle scheduling problem in public mass transit and reports on computational investigations. Our Lagrangean relaxation approaches can be applied to generate very tight lower bounds and to compute feasible solutions efficiently. A further application is to use the Lagrangean relaxations as new pricing strategies for a delayed column generation of a branch-and-cut approach. The computational investigations are based on real-world test sets from the cities of Berlin and Hamburg having up to 25 thousand timetabled trips and 70 million dead-head trips.
Duty Scheduling Templates
(2012)
We propose duty templates as a novel concept to produce similar duty
schedules for similar days of operation in public transit. Duty templates
can conveniently handle various types of similarity requirements, and
they can be implemented with ease using standard algorithmic techniques.
They have produced good results in practice.
Duty Scheduling Templates
(2012)
We propose duty templates as a novel concept to produce similar duty schedules for similar days of operation in public transit. Duty templates can conveniently handle various types of similarity requirements, and they can be implemented with ease using standard algorithmic techniques. They have produced good results in practice.
Experiments with a Dantzig-Wolfe Decomposition for Multiple-Depot Vehicle Scheduling Problems
(1997)
In this paper, we present a Dantzig-Wolfe decomposition for the $NP$-hard multiple-depot vehicle scheduling problem in public mass transit. It turned out that such a decomposition approach is an unsuitable method to solve such kind of multicommodity flow problems. The major obstacle to solve such problems is that the continuous master problem relaxations become too hard to be solved efficiently. Especially for problems with more than one thousand timetabled trips, the LU factorization in solving a restricted master problem takes far too much time. We will describe our computational experiments in detail and discuss the reasons why the decomposition method fails in this case. Our computational investigations are based on real-world problems from the city of Hamburg with up to 2,283 timetabled trips. Our decomposition implementation is compared with a delayed column generation to solve the linear programming (LP) relaxation directly. This LP method can solve the LP relaxations of the integer linear programming formulation exactly for truly large-scale real-world problems of the cities of Berlin and Hamburg.
This paper presents an integer linear programming approach with delayed column generation for the {\em NP} Multiple-Depot Vehicle Scheduling Problem (MDVSP) in public mass transit. We describe in detail all basic ingredients of our approach that are indispensable to solve truly large-scale real-world instances to optimality, and we report on computational investigations that are based on real-world instances from the city of Berlin, the city of Hamburg, and the region around Hamburg. These real-world instances have up to 25 thousand timetabled trips and 70 million dead-head trips. Computational tests using the data of the Hamburger Hochbahn AG indicate savings of several vehicles and a cost reduction of about 10\% compared with the solution provided by HOT II, the vehicle scheduling tool of the HanseCom GmbH, Hamburg. Parts of our algorithms are already integrated in the BERTA system of the Berliner Verkehrsbetriebe (BVG) and will soon be integrated in the MICROBUS system of the Gesellschaft für Informatik, Verkehrs- und Umweltplanung mbH (IVU), Berlin.
This paper investigates the solution of the linear programming (LP) relaxation of the multicommodity flow formulation of the multiple-depot vehicle scheduling problems arising in public mass transit. We develop a column generation technique that makes it possible to solve the huge linear programs that come up there. The technique, which we call {\em Lagrangean pricing}, is based on two different Lagrangean relaxations. We describe in detail the basic ingredients of our approach and give computational results for large-scale test data (with up to 70 million variables) from three German public transportation companies. Because of these results, we propose Lagrangean pricing as one of the basic ingredients of an effective method to solve multiple-depot vehicle scheduling problems to proven optimality.
This paper presents a large-scale real-world application of the minimum-cost flow problem, describes some details of a new implementation of the network simplex algorithm, and reports on computational comparisions. The real-world test sets include minimum-cost flow problems that are based on single-depot vehicle scheduling problems and on a Lagrangean relaxation of multiple-depot vehicle scheduling problems. Some of the problems are extremely large with up to 42,000 nodes and 20,000,000 arcs. The standard test problems are generated with NETGEN and include parts of the DIMACS standard problems. Our network simplex code is compared with \mbox{RELAX-IV}, Cost Scaling 2 version 3.4, and CPLEX's network solver NETOPT.
This paper addresses the problem of scheduling vehicles in a public mass transportation system. We show how this problem can be modelled as a special multicommodity flow problem and outline the solution methodology we have developed. Based on polyhedral investigations, we have designed and implemented a branch&cut algorithm and various heuristics with which real vehicle scheduling problems of truely large scale can be solved to optimality. We describe some implementation issues and report computational results.
Im Rahmen des EDV-gestützten Systems zur {\bf{}BE}t{\bf{}R}iebseinsa{\bf{}T}zplanung und -{\bf{}A}uswertung (BERTA) der Berliner Verkehrsbetriebe (BVG) wird ein Modul zur Wagenumlaufoptimierung realisiert. In diesem Aufsatz berichten wir über die betrieblichen Anforderungen und Nebenbedingungen an eine mathematische Optimierung und erläutern unsere Konzepte zur Realisierung eines exakten mathematischen Verfahrens.