Bi-objective Evolutionary Heuristics for Bus Drivers

The Bus Driver Rostering Problem refers to the assignment of drivers to the daily schedules of the company's buses, during a planning period of a given duration. The drivers' schedules must comply with legal and institutional rules, namely the Labour Law, labour agreements and the company's specific regulations. This paper presents a bi-objective model for the problem and two evolutionary heuristics differing as to the strategies adopted to approach the Pareto frontier. The first one, the utopian strategy, extends elitism to include an unfeasible solution in the population, and the second one is an adapted version of the well known SPEA2 (Strength Pareto Evolutionary Algorithm). The heuristics' empirical performance is studied with computational tests on a set of instances generated from vehicle and crew schedules. This research shows that both methodologies are adequate to tackle the instances of the Bus Driver Rostering Problem. In fact, in short computing times, they provide the planning department, with several feasible solutions, rosters that are very difficult to obtain manually and, in addition, identify among them the efficient solutions of the bi-objective model

A Memetic Algorithm for a Bi-objective Bus Driver Rostering Problem

The Bus Driver Rostering Problem (DRP) consists of assigning bus drivers to daily duties during a planning period. The problem considers hard constraints imposed by institutional and legal requirements. Solutions should as much as possible satisfy soft constraints that qualify rosters according to either the company's or the drivers' interests. A bi-objective version of the DRP is considered and two models are presented. Due to the high computational complexity of DRP, this paper proposes the Strength Pareto Utopic Memetic Algorithm (SPUMA) a new heuristic algorithm specially devised to tackle the problem. SPUMA genetic component combines utopic elitism with a strength Pareto fitness evaluation and includes an improvement procedure. Computational results show that SPUMA outperforms an adaptation of one of the state-of-the-art most competitive multi-objective evolutionary algorithms, SPEA2

Exploring Priorities in Transit Scheduling Between Small and Large Bus Companies

This research paper explores the relative importance of strategic and tactical objectives during the crew scheduling and rostering process between smaller and larger bus companies via a survey of bus companies in Victoria, Australia. Results indicate that larger bus companies emphasise reduced labour cost through maximising their operational efficiency. They acknowledge the importance of keeping their customers satisfied and to a lesser extent their drivers, however, doing so is not as important as it is to smaller organisations. Whilst reducing labour cost plays an important role to small bus companies their main focus is maximising customer service levels. To a lesser extent keeping their drivers satisfied is also important, however, in both cases smaller companies are willing to sacrifice labour cost and operational efficiencies to ensure these two objectives are met.A modelling exercise was undertaken to determine the impacts of applying large company priorities to small bus operators. The key strategic objective applied during this process was reducing labour cost whilst at an operational level maximising the use of on-road meal break locations and reducing meal breaks during peak periods were the focus. As anticipated in both cases the labour costs were reduced (by between 1.5% and 13.3%). Dead running distances increased in both cases, however, these would have a minor impact on overall operating costs savings.The results of both this survey and subsequent modelling have implications for both research and practice. Research in the area of crew scheduling and rostering for smaller companies has been quite limited. Most literature has focussed primarily on the objectives of larger companies. Implications for future research and practice are identified.1. Introductio

The crew-scheduling module in the GIST system

The public transportation is gaining importance every year basically due the population growth, environmental policies and, route and street congestion. Too able an efficient management of all the resources related to public transportation, several techniques from different areas are being applied and several projects in Transportation Planning Systems, in different countries, are being developed. In this work, we present the GIST Planning Transportation Systems, a Portuguese project involving two universities and six public transportation companies. We describe in detail one of the most relevant modules of this project, the crew-scheduling module. The crew-scheduling module is based on the application of meta-heuristics, in particular GRASP, tabu search and genetic algorithm to solve the bus-driver-scheduling problem. The metaheuristics have been successfully incorporated in the GIST Planning Transportation Systems and are actually used by several companies.Integrated transportation systems, crew scheduling, metaheuristics

Solving Public Transit Scheduling Problems

Operational planning within public transit companies has been extensively tackled but still remains a challenging area for operations research models and techniques. This phase of the planning process comprises vehicle scheduling, crew scheduling and rostering problems. In this paper, a new integer mathematical formulation to describe the integrated vehicle-crew-rostering problem is presented. The method proposed to solve this multi-objective problem is a sequential algorithm considered within a preemptive goal programming framework that starts from the solution of an integrated vehicle and crew scheduling problem and ends with the solution of a driver rostering problem. Feasible solutions for the vehicle and crew scheduling problem are obtained by combining a column generation scheme with a branch-and-bound method. These solutions are the input of the rostering problem, which is tackled through a mixed binary linear programming approach. An application to real data of a Portuguese bus company is reported and shows the importance of integrating the three scheduling problems

IVIsion and IVInet – Tool Chain for the Electrification of City Bus Routes

AbstractThe Fraunhofer Institute for Transportation and Infrastructure (IVI) developed a method and matching tools (IVIsion and IVInet) that analyzes and evaluates individual bus routes or entire bus route networks regarding their suitability for electric buses.IVInet analyses the vehicle rostering plan and the route network. The software is designed especially for the development of suitable solutions in the transfer from diesel-powered bus networks to electric buses. Based on generalized values for traction energy demands and simplified assumptions regarding the energy demand of auxiliaries, heating, and cooling, the state of charge of the energy storage is analysed for the vehicle circulation under consideration of a boost charging strategy. With an optimization method the optimal number of charging infrastructure will be find under given conditions.IVIsion is an in-house development that comprises several program modules for data processing, for the calculation of driving systems, and for evaluating the calculation results. At least it contains more than 200 preconfigured drive trains for conventional, parallel, and serial hybrid as well as purely electrical driving systems. IVIsion offers opportunity for detailed calculations that take into account models for auxiliary units, the wiring system, drive train cooling, and passenger compartment air conditioning. All powertrain components, auxiliary components and their respective intelligent control strategies are part of the tool. The usage of the tool chain is explained in an application example

Integrated Driver Rostering Problem in Public Bus Transit

AbstractThe driver rostering problem (DRP), arising in public bus transport companies, generates for each group of drivers a cyclic roster while management considerations, labor laws, and the preferences of drivers have to be satisfied. Optimal rosters are characterized by maximal satisfaction of drivers, minimal difference of overtime among all drivers, and minimal number of unassigned duties. The DRP is mostly solved sequentially due to its high complexity, namely firstly the rota scheduling problem, and secondly the duty sequencing problem. However, this method may generate sub-optimal rosters. In order to avoid a sub-optimal solution, the paper discusses an integrated DRP, which is solved for real-world instances and compared with the results of the sequential approach

Operations research in passenger railway transportation

In this paper, we give an overview of state-of-the-art OperationsResearch models and techniques used in passenger railwaytransportation. For each planning phase (strategic, tactical andoperational), we describe the planning problems arising there anddiscuss some models and algorithms to solve them. We do not onlyconsider classical, well-known topics such as timetabling, rollingstock scheduling and crew scheduling, but we also discuss somerecently developed topics as shunting and reliability oftimetables.Finally, we focus on several practical aspects for each of theseproblems at the largest Dutch railway operator, NS Reizigers.passenger railway transportation;operation research;planning problems