EP2828807A2

EP2828807A2 - Scheduling of maintenance

Info

Publication number: EP2828807A2
Application number: EP13716079.2A
Authority: EP
Inventors: Tony Lindgren; Anna Pernestål
Original assignee: Scania CV AB
Current assignee: Scania CV AB
Priority date: 2012-03-20
Filing date: 2013-03-19
Publication date: 2015-01-28
Also published as: WO2013141797A3; WO2013141797A2

Abstract

A planning entity (100), which schedules a motor vehicle's maintenance visits to a workshop, includes: first and second input interfaces (110, 112), a main processing module (130) and an output interface (114). The first input interface (110) receives utilization data (UD) representing how the motor vehicle has been used during a data collection period. The second input interface (112) receives a client request (CR) containing at least one parameter specifying information relating to a desired maintenance strategy for the motor vehicle, which information is uncorrelated to the utilization data (UD). Based on the utilization data (UD) and the client request (CR), the main processing module (130) generates a maintenance program (MP) that is presented via the output interface (114). The maintenance program (MP) describes recommended visits to a workshop in terms of a series of time instances (T1, T2, …, Ty) to each of which is associated a set of maintenance instances (MI1, MI2, …, MIn) to execute at that time instance (T1, T2, …, Ty).

Description

Scheduling of Maintenance

TH E BACKG ROU N D OF TH E I NVENTION AN D P RIOR ART

The present invention relates generally to scheduli ng of mai ntenance for motor vehicles. More particularly the invention relates to a planning entity according to the preamble of claim 1 . The invention also relates to a method according to the preamble of claim 1 1 , a computer program accordi ng to claim 1 9 and a computer program product accordi ng to claim 20.

Service and mai ntenance of mechanical equipment has been needed as long as such equipment has existed . Moreover, especially for motor vehicles i n commercial use, (e.g . trucks and busses) , it is interesting to minimize the downtime, i.e. when the vehicle cannot be used (for example because it is in a workshop for mai ntenance). Hence, there is a balance between the requi- rement to perform maintenance in order to maxi mize the useful lifespan , and the wish to avoid un necessary workshop visits. Historically, tests and theoretical models have been employed to accomplish relevant maintenance programs for different types of products, such as motor vehicles. Within the vehicle industry, it has been common practice to assign intervals for when mai ntenance shall be performed , and which measu res that shall be taken at each maintenance occasion . For i nstance, a given maintenance occasion may be of smaller type (when only relatively few measures are taken) , or larger type (when comparatively many measu res are taken) . Depending on how much the vehicle is expected to be used and the owner's demand for availability, the dates for the smaller and larger maintenance occasions are assigned. However, such a static maintenance schedule is problematic because the use conditions may change dramatically du ring the lifespan of the vehicle. Consequently, workshop visits may occu r too frequently or too seldom relative to when maintenance is actually needed. Moreover, since it is generally desired to keep the nu mber of workshop visits low, a nu mber of maintenance instances must always be scheduled to a common point in time, which is determined by the shortest maintenance interval for the mai ntenance instances concerned. Hence, in respect of all other maintenance instances except the maintenance instance associated with the shortest interval, the workshop visit actually comes too soon.

US 2003/00655771 reveals a method for creati ng a maintenance algorithm for a fleet of vehicles. Here, mai ntenance information can be collected and displayed in each vehicle of the fleet. I n the fleet vehicles, sensor data that are indicative of a stress on components in the vehicles are used to derive stress variables. The ascertained stress variables are stored in the vehicles for a predetermined period and , if a wear-related event occu rs, wear variables that characterize a wear-related event are additionally ascertained . The stress variables and the wear variables are transmitted to a central location or station , in which the previous maintenance algorithm is checked with the use of the stress and wear variables, and , if applicable, an improved mai ntenance algorithm can be derived . The mai ntenance algorith m serves in establishing and indicating a time when the vehicle should be serviced.

US 2006/01 361 05 describes a solution wherein vehicle telematics is employed to improve maintenance scheduling . Specifically, vehicle condition information is collected from diverse sou rces, which include real time data collected from vehicle sensors over an intelligent vehicle controller area network. The network is provided with facilities for generati ng records with stamps allowing their correlation with vehicle inspection results and the generation of trend reports to be used in scheduli ng maintenance. US 2008/0269977 discloses a system, wherein user generated service rules operate upon selected vehicle operati ng variables, vehicle configurations and vehicle vocations to produce estimates of the service life of diverse, routine service items. Items with correspondi ng projected service lives are then g rouped for service to reduce overall service frequency. P ROBLEMS ASSOC IATE D WITH TH E PRIOR ART

Thus various solutions are known for recording utilization data for motor vehicles, and based on the recorded data, adapt a maintenance plan for each vehicle and/or adjust other arrange- ments where use factors are relevant. However, considering the use conditions alone does not allow full flexibility and efficiency in the maintenance planning. I n fact, the known solutions are comparatively inflexible in terms of adaptation to user requirements that are not directly related to the use and wear of the vehicles.

SUMMARY OF TH E I NVENTION

The object of the present invention is therefore to provide a solution, which offers maintenance scheduling with improved flexibility in terms of the users' requirements, for instance regarding vehicle availability and environ mental issues.

According to one aspect of the invention , the object is achieved by the initially described planning entity, wherein the planning entity includes a second input interface configured to receive a client request containing at least one parameter specifying infor- mation relating to a desired maintenance strategy for the motor vehicle, which information is uncorrelated to the utilization data. The main processing module is configured to generate the maintenance program on the fu rther basis of the client request.

This planning entity is advantageous because it renders it pos- sible to weigh into the plan ning for example a user's requirement concerning uptime or to minimize the vehicle's environmental influence. Thus, the at least one parameter of the client request may specify a desired upti me for the motor vehicle between two consecutive visits to the workshop and/or a maximally allowed environ mental influence caused by the motor vehicle between two consecutive visits to the workshop.

According to one embodiment of this aspect of the invention, the main processing module is configured to derive at least one boundary condition from the client request, and implement an iterative optimizing algorith m with respect to a target fu nction. The main processing module is further configured to maxi mize the target function while fulfilling the at least one boundary condition, and thus return a valid solution to an opti mization prob- lem. Then , based on this solution , the main processing module is configu red to generate the maintenance program. Consequently, the maintenance prog ram is produced very efficiently while considering highly diverse criteria.

According to another embodiment of this aspect of the invention, the optimizing algorith m is an Anytime algorithm arranged to retu rn a valid solution at each iteration completed by the optimizing algorith m. Hence, the maintenance prog ram may be i mproved by allowi ng the algorith m to ru n th roug h many iterations. However, an acceptable maintenance prog ram can be produced in each iteration . Preferably, the Anyti me algorith m is arranged to, at each iteration completed by the optimizing algorithm, retu rn the best solution so far.

According to yet another embodiment of this aspect of the invention, the planning entity includes an auxiliary processing mo- dule configured to derive a respective maintenance interval for at least one maintenance instance in respect of the motor vehicle. Namely, the maintenance program describes recom mended visits to a workshop, and is preferably based on the at least one maintenance interval. The utilization data, in tu rn , contains a predefined set of condition factors. Using condition factors is advantageous because these factors render it possible to weigh in various aspects of the use. For example, the auxiliary processing module may be configu red to compute the at least one maintenance interval on the further basis of at least one weight factor associated to each condition factor in the predefined set of condition factors. Each weig ht factor reflects how the condition factor to which it is associated influences the wear of at least one component being the subject of a given maintenance instance. Thereby, very hig h precision can be attained in how the condition factors influence the maintenance intervals for the individual components of the motor vehicle. Fu rthermore, the at least one maintenance interval may be expressed in terms of an operating time for the motor vehicle and/ or a distance d riven by the motor vehicle. I n such a case, the auxiliary processing module is configured to compute the at least one maintenance interval based on the condition factors such that the maintenance interval is either decreased or increased from a nomi nal value depending on whether the data reflected by a particular condition factor is expected to intensify or reduce the wear of a component being the subject of the maintenance instance. As a result, the maintenance prog ram can be made especially relevant for each vehicle.

According to still another embodiment of this aspect of the invention, the auxiliary processi ng module is configu red to compute the at least one mai ntenance interval on the further basis of the at least one weight factor, such that the influence of a particular condition factor on a given maintenance instance is either raised or lowered depending on whether the condition factor is deemed to be relatively i mportant or relatively u ni mportant for the wear of the component being the subject of the maintenance instance.

According to a further embodiment of this aspect of the invention, the condition factors express : a weight of the motor vehicle, a d river's d riving behavior, topog raphic data, traffic conditions, road conditions and/or climate conditions in an environ- ment where the motor vehicle has been operated du ri ng the data collection period . As a result, the key aspects for planning the maintenance can be modeled with high accuracy.

According to another aspect of the invention , the object is achieved by the method described initially, wherein a client request is received that contains at least one parameter specifying information relating to a desired maintenance strategy for the motor vehicle, which information is uncorrelated to the utilization data. The maintenance prog ram is then generated on the fu rther basis of the client request. The advantages of this method, as well as the preferred embodiments thereof, are apparent from the discussion above with reference to the proposed plan ning entity. According to a further aspect of the invention the object is achieved by a computer program loadable into the internal memory of a computer, comprising software for controlling the above proposed method when said program is run on a computer. According to another aspect of the invention the object is achieved by a computer program product, having a program recorded thereon, where the program is to make a computer control the above proposed method.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is now to be explained more closely by means of embodiments, which are disclosed as examples, and with reference to the attached drawings.

Figure 1 shows a block diagram over a planning entity according to one embodiment of the invention, Figure 2 exemplifies a maintenance plan according to one embodiment of the invention,

Figure 3 shows a block diagram over an auxiliary processing module according to one embodiment of the invention, and Figure 4 shows a flow diagram illustrating the general method according to the invention.

DESCRIPTION OF EMBODIMENTS OF THE INVENTION

We refer initially to Figure 1 showing a block diagram over a planning entity for scheduling a motor vehicle's maintenance vi- sits to a workshop according to one embodiment of the invention. The planning entity includes first and second input interfaces 110 and 112 respectively, a main processing module 130 and an output interface 114. Preferably, an auxiliary processing module 120 is also included. The first input interface 110 is configured to receive utilization data UD representing how the motor vehicle has been used du- ring a data collection period (representing any defined interval, say 24 hou rs to six months) . Thus, the data collection period may be relatively short or relatively long depending what is deemed to be appropriate with regard to the vehicle and its expec- ted usage.

The second input interface 1 1 2 is configu red to receive a client request C R containing at least one parameter specifying information relating to a desired maintenance strategy for the motor vehicle, which information is uncorrelated to the utilization data U D. Thus, the client request C R may for example contai n parameters specifying a desi red uptime for the motor vehicle between two consecutive visits to the workshop, and/or a maximally allowed envi ronmental influence caused by the motor vehicle between two consecutive visits to the workshop. To some ex- tent, said requests may involve contradictory constraints because prolonging the interval between consecutive workshop visits generally increases the vehicle's environmental influence.

The main processing module 1 30 is configured to generate a maintenance program M P describing recommended visits to a workshop in terms of a series of ti me instances T1 , T2, Ty (see Figu re 2) to each of which is associated a set of maintenance instances M M , M I2, M l n to be executed at that time instance T1 , T2, Ty. The main processing module 1 30 generates the mai ntenance program M P based on the utilization data U D and the client request C R.

According to one embodiment of the invention , the mai n processing module 1 30 is configured to derive at least one boundary condition from the client request C R. Analogously, the main processing module 1 30 is configu red to derive at least one boundary condition from the utilization data U D. Additionally, the main processing module 1 30 is configu red to implement an iterative optimizing algorith m with respect to a target fu nction, which is to be maximized while fulfilling said boundary conditions. As a result, the main processi ng module 1 30 retu rns a va- lid solution to an optimization problem. On the fu rther basis of this solution , the main processing module 1 30 generates the maintenance program M P. P referably, the maintenance prog ram M P has the general structu re illustrated in Figu re 2, i.e. represents a matrix that for each maintenance instance M M , M I2, M l n specifies ti me instances T1 , T2, Ty (say particular weeks) when the maintenance instance shall be handled at a workshop.

According to one embodiment of the invention, the optimizing algorithm implemented by the main processing module 1 30 is an Anytime algorith m that is arranged to return a valid solution at each iteration completed by the optimizing algorith m. Moreover, this algorith m is preferably arranged to, at each completed iteration ; return the best solution so far. Consequently, a fully acceptable maintenance program M P can normally be obtained very quickly. However, a better maintenance program M P can be expected if the algorith m is allowed to ru n through many iterations.

Turning now to Figure 3, we see a block diagram over the auxiliary processing module 1 20 according to one embodiment of the invention. In this embodiment, it is presumed that the utilization data U D contains a predefined set of condition factors d 1 , d2, dx. For example the condition factors d 1 , d2, dx may here express the actual weig ht of the motor vehicle during the data collection period ; or if the weight has varied , respective actual weights of the motor vehicle du ring each of a number of intervals within this period (preferably factored by the relative du ration of each interval) . Alternatively, or as a complement, the condition factors d 1 , d2, dx may also express a d river's d riving behavior during the data collection period , topog raphic data, traffic condi- tions, road conditions and/or climate conditions reflecting an environment wherein the motor vehicle has been operated during the data collection period. The data collection period may have any extension in time suitable for the i mplementation, for example as indicated above. The auxiliary processing module 1 20 is configured to derive a respective maintenance interval M-INT1, M-INT2, M-INTn for at least one maintenance instance MM, MI2, Mln of the motor vehicle. The maintenance intervals M-INT1, M-INT2, M-INTn may be arranged in a listing 150, which is fed to the main processing module 130. A maintenance interval M-INT for a maintenance instance MM is the time between two consecutive maintenance occasions for a component in the motor vehicle. Thus, if a first maintenance instance MM is represented by the oil filter, a first maintenance interval M-INT1 reflects the in- terval from installing a new oil filter until this filter must be replaced.

A processor 320 of the auxiliary processing module 120 is configured to derive the maintenance intervals M-INT1, M-INT2, M-INTn based on the utilization data UD, preferably by compu- ting the maintenance intervals M-INT1, M-INT2, M-INTn based on the condition factors d1, d2, dx and at least one weight factor w1 , w2, wx associated to each condition factor in the predefined set of condition factors d1, d2, dx. The weight factors w1, w2, wx, in turn, may be stored in a data storage 340 accessible by the processor 320. Each weight factor reflects how the condition factor to which it is associated influences the wear of at least one component being the subject of a given maintenance instance MM, MI2, Mln.

According to embodiments of the invention, the maintenance in- terval M-INT1, M-INT2, M-INTn is expressed in terms of an operating time and/or a distance driven by the motor vehicle. The processor 320 is further preferably configured to compute the maintenance interval M-INT1, M-INT2, M-INTn based on the condition factors d1, d2, dx, such that the maintenance interval M-INT1, M-INT2, M-INTn is either decreased/shortened or increased/prolonged from a nominal value depending on whether the data reflected by a particular condition factor is expected to intensify or reduce the wear of a component being the subject of the maintenance instance MM, MI2, Mln. For example, the respective correlations between the condition factors d1, d2, dx and the maintenance intervals M-INT1, M- INT2, M-INTn are assigned manually by an expert who en- ters his/her knowledge into a computer program comprising software for controlling the processor 320.

It also advantageous if the processor 320 is configured to compute each maintenance interval M-INT1, M-INT2, M-INTn on the further basis of the at least one weight factor w1 , w2, wx, such that the influence of a particular condition factor d1, d2, dx on a given maintenance instance MM, MI2, Mln is either raised or lowered depending on whether the condition factor is deemed to be relatively important or relatively unimportant for the wear of the component being the subject of the maintenance instance MM, MI2, Mln. Analogous to the above, the weight factors w1 , w2, wx may be assigned manually by an expert in the form of parameters in a computer program for controlling the processor 320. Here, the expert assigns the weight factors w1 , w2, wx based on empirical knowledge about relationships between the condition factors d1, d2, dx and the maintenance instances MM, MI2, Mln.

Preferably, each maintenance interval M-INT1, M-INT2, M- INTn is associated with a minimal value and a maximal value. The minimal value represents a value below the nominal value for the maintenance interval M-INT1, M-INT2, M-INTn, and correspondingly, the maximal value represents a value above the nominal value. The minimal and maximal values express a shortest and a longest recommended interval respectively bet- ween two consecutive visits to the workshop in the maintenance program MP. I.e. the minimal and maximal values may be expressed in terms of driving distance and/or operating time for the motor vehicle.

In practice, the minimal and maximal values may be used as fol- lows. If, based on the condition factors d1, d2, dx and weight factors w1, w2, wx, a given maintenance interval M-INT1, M- INT2, M-INTn is computed to a value below the minimal value, the processor 320 is configured to assign the maintenance interval M-INT1, M-INT2, M-INTn to the minimal value. Ana- logously, if, based on the condition factors d1, d2, dx and weight factors w1, w2, wx, a given maintenance interval M- INT1, M-INT2, M-INTn is computed to a value above the maximal value, the processor 320 is configured to assign the maintenance interval M-INT1, M-INT2, M-INTn to the maximal value. Thus, also under extreme use conditions, the mainte- nance program MP will contain reasonable maintenance intervals.

According to one embodiment of the invention, the first input interface 110 is connected to at least one communication network, which, in turn, has at least one wireless interface towards the motor vehicle. The input interface 110 is further configured to collect the utilization data UD via the at least one wireless interface. Consequently, utilization data UD in respect to a large number of vehicles may be fed into the auxiliary processing module 120 in very convenient manner. Analogously, it is preferable that the second input interface 112 is connected to at least one communication network, which, in turn, has at least one interface, preferably wireless, towards the motor vehicle, such that the second input interface 112 can collect the client requests CR via the at least one interface. The above procedure implemented by the planning entity 100 is preferably controlled by a computer program loaded into a memory 160 of the planning entity 100, or an external memory unit accessible by the planning entity 100 (not shown). The computer program, in turn, contains software for controlling the steps of the procedure when the program is run on the processing modules 120 and 130 of the planning entity 100.

In order to sum up, the general method of scheduling a motor vehicle's visits to a workshop according to the invention will be described below with reference to the flow diagram in figure 4. In a first step 410, it is checked whether utilization data have been received, which utilization data represent how the motor vehicle has been used during a data collection period; and if so, a step 420 follows. Otherwise, the procedure loops back and stays in step 410. In step 420, it is checked whether a client request has been received , which client request contains at least one parameter specifying information relati ng to a desired maintenance strategy for the motor vehicle, which information is u ncorrelated to the utilization data; and if so, a step 430 follows. Otherwise, the procedure loops back and stays in step 420.

In step 430 a mai ntenance prog ram is generated, which describes recommended visits to a workshop in terms of a series of ti me instances to each of which is associated a set of mai nte- nance instances to execute at the respective time i nstance. The maintenance program is generated based on the utilization data and the client request. Subsequently, the procedu re ends.

The process steps, as well as any sub-sequence of steps, described with reference to the figure 4 above may be controlled by means of a programmed computer apparatus. Moreover, although the embodiments of the invention described above with reference to the drawings comprise computer apparatus and processes performed in computer apparatus, the invention thus also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of source code ; object code, a code intermediate source and object code such as in partially compiled form, or in any other form suitable for use in the implementation of the process according to the invention. The carrier may be any entity or device capable of carrying the program. For example, the carrier may comprise a storage medium, such as a Flash memory, a ROM (Read Only Memory), for example a C D (Compact Disc) or a semiconductor ROM, an EP ROM (Erasable Programmable Read-Only Memory) , an E EPROM (Electrically Erasable Programmable Read-Only Memory), or a magnetic recording medium, for example a floppy disc or hard disc. Further, the carrier may be a transmissible carrier such as an electrical or optical signal, which may be conveyed via electrical or optical cable or by radio or by other means. When the program is embodied in a signal, which may be conveyed, directly by a cable or other device or means, the carrier may be constituted by such cable or device or means. Alternatively, the carrier may be an integrated circuit in which the program is embedded, the integrated circuit being adapted for performing, or for use in the performance of, the relevant processes.

The invention is not restricted to the described embodiments in the figures, but may be varied freely within the scope of the claims.

Claims

1. A planning entity (100) for scheduling a motor vehicle's maintenance visits to a workshop, the planning entity comprising:

a first input interface (110) configured to receive utilization data (UD) representing how the motor vehicle has been used during a data collection period,

a main processing module (130) configured to, based on the utilization data (UD), generate a maintenance program (MP) describing recommended visits to a workshop in terms of a series of time instances (T1, T2, Ty) to each of which is associated a set of maintenance instances (MM, MI2, Mln) to execute at that time instance (T1, T2, Ty), and

an output interface (114) configured to present the mainte- nance program (MP),

characterized in that

the planning entity (100) comprises a second input interface (112) configured to receive a client request (CR) containing at least one parameter specifying information relating to a desi- red maintenance strategy for the motor vehicle, which information is uncorrelated to the utilization data (UD), and

the main processing module (130) is configured to generate the maintenance program (MP) on the further basis of the client request (CR).

2. The planning entity (100) according to claim 1, wherein the at least one parameter specifies one of:

a desired uptime for the motor vehicle between two consecutive visits to the workshop, and

a maximally allowed environmental influence caused by the motor vehicle between two consecutive visits to the workshop.

3. The planning entity (100) according to any one of claims 1 or 2, wherein the main processing module (130) is configured to: derive at least one boundary condition from the client re- quest (CR),

implement an iterative optimizing algorithm with respect to a target function, which is to be maximized while fulfilling said at least one boundary condition, thus returning a valid solution to an optimization problem; and

generate the maintenance program (MP) based on said so- lution.

4. The planning entity (100) according to claim 3, wherein the optimizing algorithm is an Anytime algorithm arranged to return a valid solution at each iteration completed by the optimizing algorithm.

5. The planning entity (100) according to claim 4, wherein the Anytime algorithm is arranged to, at each iteration completed by the optimizing algorithm, return the best solution so far.

6. The planning entity (100) according to any one of the preceding claims, comprising an auxiliary processing module (120) configured to derive a respective maintenance interval (M-INT1, M-INT2, M-INTn) for at least one maintenance instance (MM, MI2, Mln) in respect of the motor vehicle, the maintenance program (MP) describing recommended visits to a workshop, which maintenance program (MP) is based on said at least one maintenance interval (M-INT1, M-INT2, M-INTn), and the utilization data (UD) comprises a predefined set of condition factors (d1 , d2, dx).

7. The planning entity (100) according to claim 6, wherein the auxiliary processing module (120) is configured to compute the at least one maintenance interval (M-INT1, M-INT2, M-INTn) on the further basis of at least one weight factor (w1, w2, wx) associated to each condition factor in the predefined set of condition factors (d1, d2, dx), the weight factor reflecting how the condition factor to which it is associated influences the wear of at least one component being the subject of a given maintenance instance (MM, MI2, Mln).

8. The planning entity (100) according to claim 7, wherein the at least one maintenance interval (M-INT1, M-INT2, M-INTn) is expressed in terms of at least one of an operating time for the motor vehicle and a distance driven by the motor vehicle, and the auxiliary processing module (130) is configured to compute the at least one maintenance interval (M-INT1, M-INT2, M- INTn) based on the condition factors (d1, d2, dx) such that the maintenance interval (M-INT1, M-INT2, M-INTn) is either decreased or increased from a nominal value depending on whether the data reflected by a particular condition factor is expected to intensify or reduce the wear of a component being the subject of the maintenance instance (MM, MI2, Mln).

9. The planning entity (100) according to claim 8, wherein the auxiliary processing module (130) is configured to compute the at least one maintenance interval (M-INT1, M-INT2, M-INTn) on the further basis of the at least one weight factor (w1 , w2, wx) such that the influence of a particular condition factor on a given maintenance instance (MM, MI2, Mln) is either raised or lowered depending on whether the condition factor is deemed to be relatively important or relatively unimportant for the wear of the component being the subject of the maintenance instance (MM, MI2, Mln).

10. The planning entity according to any one of the claims 4 to 9, wherein the condition factors (d1, d2, dx) express at least one of: a weight of the motor vehicle, a driver's driving behavior, topographic data, traffic conditions, road conditions and climate conditions in an environment where the motor vehicle has been operated during the data collection period.

11. A method of scheduling a motor vehicle's maintenance visits to a workshop, the method comprising:

receiving utilization data (UD) representing how the motor vehicle has been used during a data collection period,

generating, based on the utilization data (UD), a maintenance program (MP) describing recommended visits to a workshop in terms of a series of time instances (T1, T2, Ty) to each of which is associated a set of maintenance instances (MM, MI2, Mln) to execute at that time instance (T1, T2, Ty), and

presenting the maintenance program (MP),

characterized by

receiving a client request (CR) that contains at least one parameter specifying information relating to a desired maintenance strategy for the motor vehicle, which information is uncorrected to the utilization data (UD), and

generating the maintenance program (MP) on the further basis of the client request (CR).

12. The method according to claim 11, wherein the at least one parameter specifies one of:

13. The method according to any one of claims 11 or 12, comprising:

deriving at least one boundary condition from the client re- quest (CR),

executing an iterative optimizing algorithm with respect to a target function, which is to be maximized while fulfilling said at least one boundary condition, thus returning a valid solution to an optimization problem; and

generating the maintenance program (MP) based on said solution.

14. The method according to claim 13, wherein a valid solution is returned at each iteration completed by the optimizing algorithm.

15. The method according to any one of claims 11 to 14, comprising:

deriving a respective maintenance interval (M-INT1, M- INT2, M-INTn) for at least one maintenance instance (MM, MI2, Mln) in respect of the motor vehicle, the maintenance program (MP) describing recommended visits to a workshop, which maintenance program (MP) is based on said at least one maintenance interval (M-INT1, M-INT2, M-INTn), and the utilization data (UD) comprises a predefined set of condition fac- tors (d1 , d2, dx).

16. The method according to claim 15, comprising computing the at least one maintenance interval (M-INT1, M-INT2, M- INTn) on the further basis of at least one weight factor (w1, w2, wx) associated to each condition factor in the predefined set of condition factors (d1, d2, dx), the weight factor reflecting how the condition factor to which it is associated influences the wear of at least one component being the subject of a given maintenance instance (MM, MI2, Mln).

17. The method according to claim 16, wherein the at least one maintenance interval (M-INT1, M-INT2, M-INTn) is expressed in terms of at least one of an operating time for the motor vehicle and a distance driven by the motor vehicle, and the method comprising computing the at least one maintenance interval (M-INT1, M-INT2, M-INTn) based on the condition fac- tors (d1, d2, dx) such that the maintenance interval (M-INT1, M-INT2, M-INTn) is either decreased or increased from a nominal value depending on whether the data reflected by a particular condition factor is expected to intensify or reduce the wear of a component being the subject of the maintenance ins- tance (MM, MI2, Mln).

18. The method according to claim 17, comprising computing the at least one maintenance interval (M-INT1, M-INT2, M- INTn) on the further basis of the at least one weight factor (w1, w2, wx) such that the influence of a particular condition fac- tor on a given maintenance instance (MM, MI2, Mln) is either raised or lowered depending on whether the condition factor is deemed to be relatively important or relatively unimportant for the wear of the component being the subject of the maintenance instance (MM , MI2, Mln).

19. The method according to any one of the claims 15 to 18, wherein the condition factors (d1, d2, dx) express at least one of: a weight of the motor vehicle, a driver's driving behavior, topographic data, traffic conditions, road conditions and climate conditions in an environment where the motor vehicle has been operated during the data collection period.

20. A computer program loadable into the internal memory of a computer, comprising software for controlling the steps of any of the claims 11 to 19 when said program is run on the computer.

21. A computer program product (160), having a program recorded thereon, where the program is to make a computer control the steps of any of the claims 11 to 19.