The Operational Indicators Board is a top level oriented module for mass transportcompanies. The proposal is to collect and to show the planning and control information tosupport the decision making process. In this extended abstract the authors describe theindicators identification process and discuss the main ideas for the module functionality.The authors acknowledge the financial support given by Agência de Inovação to theEUROBUS project in which the work here described is included

Jorge Freire Sousa

João Pedro Mendes Moreira

The Operational Indicators Board is a top level oriented module for mass transport
companies. The proposal is to collect and to show the planning and control information to
support the decision making process. In this extended abstract the authors describe the
indicators identification process and discuss the main ideas for the module functionality.
The authors acknowledge the financial support given by Agência de Inovação to the
EUROBUS project in which the work here described is included

Repositório Aberto da Universidade do Porto

177.1Operational Indicators for public transport companiesJoão Mendes Moreira, Jorge Freire de SousaINEGI, Rua dos Bragas, 4099 Porto Codex, PortugalTel: +351-2-2073650 Fax: +351-2-2073659 Email: joao@gist.fe.up.ptJfsousa@fe.up.ptKeywords: Transportation, DSS, Data MartSUMMARYThe Operational Indicators Board is a top level oriented module for mass transportcompanies. The proposal is to collect and to show the planning and control information tosupport the decision making process. In this extended abstract the authors describe theindicators identification process and discuss the main ideas for the module functionality.The authors acknowledge the financial support given by Agência de Inovação to theEUROBUS project in which the work here described is included.The GIST systemThe GIST system is a computer application for supporting operational planning in publictransport companies. It was developed as a decision support system which aims to helptransport companies to improve the operation of critical resources, such as vehicles, driversand planning staff. The system is also an important software tool to support tactical andstrategic management studies regarding companies operations. A consortium of 5 leadingPortuguese transport companies (CARRIS, STCP, Horários do Funchal, Empresa Barraqueiroand Vimeca) and 2 R&D institutes (INEGI-FEUP and ICAT-FCUL) is responsible for theGIST system. The companies involved in this consortium operate daily about 6000 vehicles,corresponding broadly to half of the road public transport market in Portugal, includingMadeira and Azores.The GIST system was successfully installed in those companies in 1996. Nowadays, theupgrades of this application are being developed under the name of GIST98/EUROBUS. Thisevolution represents both an improvement to the GIST present functions and an extension ofits functionality.The GIST98/EUROBUS systemThe GIST98/EUROBUS system will contain the following modules:? Network Module, allowing the definition of the transportation network;? Gist-Line Module,  the route information module;? Trip and Vehicle Scheduling Module, allowing the trip timetable definition and thevehicle scheduling information management and optimisation. This module will have 4algorithms: the basic algorithm gives the lower cost solution; the fixed algorithm gives thelower cost solution using a given number of vehicles but guaranteeing that all the trips are177.2affected; the fixed algorithm with penalties does the same as the previous algorithm butdoes not guarantee that all the trips are affected; finally, the multi-depot algorithm doesthe same as the basic one but for more than one depot;? Crew Scheduling Module, the crew scheduling information management and optimisationmodule. This module will have three families of algorithms: genetic algorithms; columnsimplicit generation with Branch and Bound, if the solution is not integer; and feasibleservices generation with lagrangean and linear relaxation;? Rostering Module, in which is defined who will do each duty each day and in whichvarious optimisation algorithms are applied to the rostering rules. The algorithms usecolumns implicit generation with Branch and Bound, when the solution is not integer;? User Information Module, a user-oriented module which provides information to the usersof public transports;? Operational Indicators Board Module, a top level oriented module which gives operationalindicators to support the decision making process;? Hiring Management Module, the module that manages the renting vehicle activity.The first 4 modules will be upgrades of the present GIST modules. The Crew SchedulingModule, the Rostering Module, the User Information Module and the Operational IndicatorsBoard Module belong to the EUROBUS project that has been financially supported by theAgência de Inovação. All these modules together with the Hiring Management one form theGIST98/EUROBUS system.The Operational Indicators Board Module (QIO)The QIO Module filters the information derived from the other modules so that theoperational managers can access, in an easy way, the relevant information for the decisionmaking process. The main problem in structuring a QIO Module is to define those indicators,which are significant to the managers and those that are important to the planners but notnecessarily to the managers. The QIO Module will just contain the information useful to theoperational managers. All the other information is included in the corresponding module. Asthe GIST98/EUROBUS is a decision support system to the operational planning, theinformation we can get from it is essentially planning information.The first questions we tried to answer were: “Which are the big groups of indicators thatoperational managers need?” and “Which indicators must be included in each group?”.The results presented in this text are derived from the study accomplished at the STCPCompany.1. “Which are the big groups of indicators that operational managers need?”Taking attention to the information GIST98 will have and to the big areas thatoperational managers work with, we have defined 3 main groups of operationalindicators: service indicators, crew indicators and vehicle indicators. The first groupgives information about the level of service that the company is offering, which isespecially important to the commercial department; the crew indicators giveinformation relevant to the human resource department and, finally, the vehicleindicators are useful to the logistic department.177.32. “Which indicators must be included in each group?”The answer to this question is not easy. The complexity involved in the definition ofthose indicators creates problems in a systematic approach. The way we solved thisquestion will be explained in the next section.The process of defining the service indicatorsThe first thing to do was to collect information from the companies. It was a long, interactiveprocess between the analysts and the companies’ staff. The service reports produced monthlywere another major source of information.From the information collected it was possible to notice that each potential indicator has nomore than 4 parameters. This means that it is possible to identify a specific indicator byanswering to 4 questions: “Which is the indicator denomination?”, “Which entity does theindicator refers to?”, “Which is the aggregation level used?” and “Which aggregation functiondoes it represent (in other words, is it an average, a maximum or any other kind offunction)?”. As an example: if we want to know the average width of trips per route, theindicator is the width, the entity is the trip, the aggregation level is the route and theaggregation function is the average. Using this methodology it was possible to identify theindicators group, the entities group and the aggregation function group. The aggregation levelwas more complex to define. In fact, using the example above, the trips can be aggregated byroute, by line, by period of the day, by day, by line and by period of the day, at the same time,i.e. they can be aggregated by one entity or by a combination of entities. The first thing weobserved was that when an indicator can be aggregated by one entity it can also be aggregatedby more generic entities. In the example, if routes can aggregate trips, lines can also aggregatetrips (notice that a line is a set of routes). The step forward was to define the differentdimensions to be used. Each dimension refers to a sequence of entities. The order by whichthey appear on the table below is defined by their degree of detail. This means that the firstentity of each dimension is the most specific one and the last one is the most generic. In otherwords, the entity referred at the last column of each dimension is a set of the entities referredat the previous one, and so on.The tables 1 and 2 present part of the results obtained by this methodological approach (theaggregation functions were ignored just for the sake of simplicity).DimensionsSpace Segment Route Line Gist-line Depot NetworkTime Period Day type Week Month Quarter YearTrips Trip Vehicle Depot NetworkRosters Duty-period Duty Duty type Roster Depot NetworkEntitiesTable 1177.4Service IndicatorsSpace Time Trips Rosters Planed vs. RealExtension xNumber of routes xRoutes width xNumber of lines xLines width xNumber of trips x x x x xNumber of vehicles used x x xNumber of vehicles used at the same time x x x xTotal distance x x x x xCommercial distance x x x x xNon commercial distance x x x x xCommercial time x x x x xNon commercial time x x x x xCommercial speed x x xDimensionsTable 2The QIO functionalityThe data for QIO will be stored in an appropriate database. This is an important feature forvarious reasons. Some companies operate in several towns and they may have aGIST98/EUROBUS system working at each town; if the managers want to know indicatorsconcerning all the company, and not just a part of it, the QIO has to work at the same timewith information from several databases in order to provide the right indicators. Moreover,the information that is stored is not necessarily the same that is used to support the operationalplanning. Sometimes, specific statistics based on that information are enough. So, theinformation will be red from different databases with a given periodicity and only the one thatis relevant to the QIO Module will be stored.The QIO Module will have access permission rules. These rules will allow the definition ofpermissions by groups of indicators or for each indicator individually considered. Thevisualisation of the performance indicators and the production of reports according to thecompany needs are the two main user facilities. The production of reports will also includethe possibility of creating documents in a data-sheet format.Main references[1] Bertossi, A.A.; P.Carraresi and G. Gallo, “On some Matching Problems Arising inVehicle Scheduling Models”, Networks 17, 1987, pp. 271-281.[2] Borges, J.L. and Cunha, J.F., “Sistema de apoio à geração de escalas de pessoal noplaneamento operacional de sistemas de transportes colectivos”, In: InvestigaçãoOperacional 15, 1, 1995.[3] Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation,“National Transportation Statistics 1997”, 1997.[4] Chu, P.C and Beasley, J.E., “A Genetic Algorithm for the Set Partitioning Problem”,European Journal of Operation Research 94, 1995, pp. 392-404.[5] Desrochers, M. and Soumis, F., “CREW-OPT: Crew scheduling by column generation”,In: Daduna, J. and Wren, A. (eds.), Computer-Aided Transit Scheduling, North-Holland, Amsterdam, 1995.[6] Desrochers, M. and Soumis, F., “A Column Generation Approach to the Urban TransitCrew Scheduling Problem”, Transportation Science, Vol.23, nº1, February 1989.177.5[7] Mesquita, M. and Paixão, J., “Multiple Depot Vehicle Scheduling Problem: A NewHeuristic Based on Quasi-Assignment Algorithms”, Computer-Aided TransitScheduling,  (M. Desrochers and J.M.Rousseau, eds.), Lecture Notes in Economics andMathematical Systems 386, Springer Verlag, Berlin, Heidelberg, 1992, pp. 167-180.[8] Sousa, J.F., “A Computer Based Interactive Approach to Crew Scheduling”, EuropeanJournal of Operational Research, 55, 1991, pp. 382-393.[9] Sousa, J.P., Sousa, J.F. and Guimarães, R.C., “Un système informatique d’aide à lagénération d’horaires de bus et de chauffeurs”, in: Gestion de l’économie et del’entreprise – l’approche quantitative, Editions CORE, Série Balises, De Boeck,Brussels, 1988, pp. 477-492.[10] Thomsen, E., “OLAP Solutions: Building Multidimensional Information Systems”,John Wiley & Sons, Inc., 1997.

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