Method and system for loading a car train with vehicles, as well as a computer program product

Abstract

The invention relates to a method for loading a car-carrying train (14) with vehicles (14). In the method, at least one vehicle (16) is provided from a vehicle pool (18), the positions of which are known. Subsequently, a position for each vehicle (16) from the vehicle pool (18) on the car-carrying train (14) is determined by means of an optimization algorithm based on at least one vehicle parameter and / or at least one car-carrying train parameter. The determined position for the provided vehicle (16) is then displayed by means of a display device (22). Furthermore, the invention relates to a system (10) for loading a car-carrying train (14) with vehicles (16) and to a computer program.

Description

[0001] The present invention relates to a method for loading a car train with vehicles. Furthermore, the invention relates to a system for loading a car train with vehicles and a computer program product.

[0002] New vehicles sometimes travel extremely long distances from the manufacturer to the dealer or customer. Car trains are used for many of these journeys. Car trains have two loading levels of fixed length: an upper loading level and a lower loading level, separated by a vertically adjustable intermediate level. An example of such a car train is known from DE 295 16 152 U1.

[0003] Vehicles are loaded onto the car train one at a time. The vehicle inventory is usually known. Computer systems can also determine the sequence, i.e., the exact position of the vehicles. The decision as to whether a vehicle is loaded on the upper or lower loading level is based on experience and is therefore a manual process.

[0004] For loading, the vehicles to be loaded are arranged in several rows per car train. A marshal then drives up and down the rows of vehicles and decides which row of vehicles will be loaded next. A team of drivers then drives the vehicles onto the car train or the loading levels. In some cases, individual vehicles are parked and loaded later. During loading, the marshal assigns each vehicle to the upper or lower level by hand signal. Once the driver has parked the vehicle on one of the loading levels, the driver secures the vehicle and leaves the train at the side. A shuttle takes them back to the rows of vehicles to drive the next vehicle onto the train.

[0005] The dispatcher attempts to make the best possible use of capacity, but has only a limited overview of the total number and types of available vehicles. He relies on his experience. A disadvantage of the familiar loading process is that the combination of vehicles is suboptimal.

[0006] The present invention is based on the object of creating a method, a system and a computer program product that enable capacity optimization and automation of vehicle allocation to the loading levels of a car train.

[0007] To solve the problem, a method having the features of claim 1, a system having the features of claim 9 and a computer program product having the features of claim 10 are proposed.

[0008] Advantageous embodiments of the method are the subject of the dependent claims.

[0009] According to a first aspect of the invention, a method for loading a car train with vehicles is proposed. In the method, at least one vehicle is provided from a vehicle fleet, the positions of the vehicles within the vehicle fleet being known. Subsequently, a position for each vehicle from the vehicle fleet on the car train is determined based on at least one vehicle parameter and / or at least one car train parameter using an optimization algorithm. The determined position for the provided vehicle is then displayed using a display device.

[0010] The method according to the invention makes it possible to determine the positions of the vehicles on the car train in a capacity-optimized manner before the vehicles are manually loaded. Furthermore, the method according to the invention enables automated assignment of the vehicles to the loading levels of a car train and, at the same time, determines the loading sequence. To determine the positions, the problem is formulated as an assignment problem. Based on known vehicle parameters or vehicle data, taking into account the vehicle mix and the position of the vehicles in the vehicle fleet or the loading blocks, the problem is solved with the aid of a mathematical model, in particular a mathematical solver such as Gurobi, in order to distribute the vehicles as optimally as possible across the two loading levels of the car train even before loading.The process enables a significant improvement in capacity utilization and a reduction in personnel costs, as manual training is no longer required. This allows for a reduction in transport costs. Furthermore, the process serves as a preparatory step for the automation of the loading of a car train.

[0011] The advantage of the automated driving rollout at the factory is that the exact positions of the vehicles are relatively easy to determine. In order to drive a vehicle automatically to a specific location, this location must be known or specified in advance.

[0012] In an advantageous embodiment, the vehicles are arranged in rows before being lined up. Advantageously, the data on the vehicle sequences is known.

[0013] In an advantageous embodiment, the vehicles are deployed in the rows one after the other during deployment. This means that all vehicles in a first row are first deployed one after the other. Then, the vehicles in the next row—which can be, for example, the row immediately adjacent to it or any other row—are deployed one after the other, and so on.

[0014] In an advantageous embodiment, the first vehicle of each of the vehicle rows is provided during provision.

[0015] In an advantageous embodiment, the vehicles are provided at random from the vehicle rows during provision.

[0016] In an advantageous embodiment, a loading level of the car train is determined as the position. The loading level can be indicated by a signal lamp.

[0017] In an advantageous embodiment, the at least one vehicle parameter comprises a length of the vehicle, a height of the vehicle, and / or a weight of the vehicle. A vehicle parameter of a vehicle can be based on the vehicle type.

[0018] In an advantageous embodiment, the at least one car train parameter comprises an available length of the loading level, a height of the loading level, a car train type and / or an available load capacity of the loading level.

[0019] According to a further aspect of the invention, a system for loading a car train with vehicles for carrying out a method according to the invention is proposed. The system comprises an evaluation device configured to determine, based on known positions of the vehicles within a vehicle fleet, a position for each vehicle from the vehicle fleet on the car train using an optimization algorithm based on at least one vehicle parameter and / or at least one car train parameter, and at least one display device configured to display the determined position.

[0020] The evaluation device can be a data processing device. The evaluation device can have a memory device and a computing unit. The optimization algorithm and a database can be stored in the memory device. The known positions of the vehicles, the at least one vehicle parameter and / or the at least one car train parameter can be stored in the database. The evaluation device is advantageously configured to carry out step b) of the method according to the invention and to send a signal to the display device on the basis of which signal the display device displays the determined position. To carry out step b) of the method according to the invention, the known positions of the vehicles, the optimization algorithm, the at least one vehicle parameter and / or the at least one car train parameter stored in the memory device are first loaded.The known positions of the vehicles can be transmitted to the evaluation device via an interface, such as a data line, e.g., a data cable, or a wireless connection, e.g., Wi-Fi, and then stored in the database. The vehicle parameter can be based on a vehicle type, and the car-train parameter can be based on a car-train type. The vehicle type and / or the car-train type can be entered using an input interface of the evaluation device, such as a keyboard.

[0021] The indicator device can be designed as signal lamps that indicate which loading level the vehicle should drive to. The indicator device can be connected to the evaluation device via an interface, such as a data cable or radio.

[0022] According to a further aspect of the invention, a computer program product is proposed which comprises instructions which cause the system according to the invention to carry out the method according to the invention.

[0023] A system and method for loading a car train with vehicles, as well as further features and advantages, are described in more detail below using an exemplary embodiment schematically illustrated in the figures. Herein: Fig. 1 a schematic representation of a system for loading a car train with vehicles; Fig. 2 cross-sections of a car train and its parameters; Fig. 3 is a schematic representation of a method for loading a car train according to a first embodiment; Fig. 4 is a schematic representation of the method for loading a car train according to a second embodiment; and Fig.5 a schematic representation of the method for loading a car train according to a third embodiment.

[0024] In Fig. 1 shows a system 10 for loading an upper loading level 12a and a lower loading level 12b of a car train 14 with vehicles 16 from a vehicle fleet 18.

[0025] The system 10 has an evaluation device 20 which is configured to determine, based on known positions of the vehicles 16 within the vehicle fleet 18, a position for each vehicle 16 from the vehicle fleet 18 on the car train 14 on the basis of at least one vehicle parameter and / or at least one car train parameter by means of an optimization algorithm, and at least one display device 22 which is configured to display the determined position.

[0026] The evaluation device 20 has a computing unit 24, for example a CPU, and a storage device 26 for storing the optimization algorithm and a database.

[0027] The database contains 16 vehicle parameters and car train parameters for each vehicle in the 18 vehicle fleet.

[0028] The vehicle parameters include the following parameters: l v Length of the vehicle v h v Height of the vehicle v. The height of the vehicle changes according to hr v , if it is on the back slope, and after hf v when it is on the front slope of a wagon (see Fig. 2) w u Weight of the vehicle v b v Width of the vehicle v. The vehicles are grouped into three categories. For each category, there is a height requirement value γ v, which defines the additional height requirement for the vehicle v on the upper level.

[0029] As in Fig. As can be seen in Figure 2, the car train parameters include the following parameters: L max Maximum available length on the upper and lower levels of a wagon LF max Flat area of ​​the lower level of a wagon L sl Length of a slope on the lower level of a wagon W0 max Maximum permissible weight on the lower level of a wagon W1 max Maximum permissible weight on the upper level of a wagon H max Maximum overall height of the train (excluding the train chassis) δ Height of the vertically adjustable floor. For some strategies, this is a parameter; for others, it is a continuous decision variable.

[0030] According to Fig.1, the display device 22 has two arrow-shaped signal lamps 28, which indicate the loading level 12a, 12b to which the vehicle 16 is to be driven for loading. In this case, the display device 22 is connected to the evaluation device 20 via a data line 30, such as a data cable. Instead of a data line, the display device 22 can be connected to the evaluation device 20 via a radio connection, such as WLAN.

[0031] Depending on which loading level 12a, 12b the evaluation device 20 determines for the vehicle 16 provided from the vehicle fleet 18, the evaluation device 20 controls the upper signal lamp 32a for the upper loading level 12a via the data line 30, and for the lower loading level 12b the evaluation device 20 controls the lower signal lamp 32b via the data line 30.

[0032] The following procedure can be carried out to load the car train 14 with the vehicles 16 from the vehicle fleet 18 using the system 10.

[0033] In a first step, the vehicles 16 are arranged in rows as shown in Fig. 3. The positions of the vehicles 16 from the vehicle fleet 18 within the rows 34 are known. For example, the exact positions of the vehicles 16 can be determined during the rollout of automated driving at the factory and transmitted to the evaluation device 20 via an interface (not shown), such as a data cable or WLAN, and stored in the storage device 26.

[0034] In a second step, the positions of the vehicles 16 are calculated using the optimization algorithm stored in the storage device 26.

[0035] The optimization algorithm is based on an assignment problem that is solved with the following mathematical function: max∑v∈V∑p∈Pxvp (1) Maximize number of vehicles on train

[0036] As in Fig. As can be seen in Figure 3, to determine the positions, the vehicles 16 are provided in the vehicle rows 34 one after the other, with the position on the car train 14 or the upper loading level 12a and the lower loading level 12b being calculated for each vehicle 16 from the vehicle fleet 18. For this purpose, the optimization algorithm uses the following conditions: Allocation, length and weight conditions ∑v∈Vxvp≤1∀p∈P (2) One vehicle per position ∑p∈Pxvp≤1∀v∈V (3) Each vehicle only in one position ∑v∈V∑p∈SIvxvp≤Lmax∀S∈PWL (4) Maximum available length limited in each wagon for both levels ∑v∈V∑p∈Swvxvp≤Wlmax∀S∈PWL (5) Maximum available length limited in each wagon for both levels Altitude conditions ∑p∈SLhvxvp≤Hmax−δh−γv∀SL∈P1,v∈V (6) Height limitation upper level taking into account the profile shape ∑p∈S / {(0),S(−1)}hvxvp+hrvxvS(−1)≤δh∀S∈PW0,v∈V (7) Height limit lower level depending on the position of the vehicle in the wagon 2jw+(1−jw)iw≤∑v∈VxvPw0(0)+∑v∈VxvPw0(−1)∀w∈W (8) Decision about how many vehicles must be in a wagon on a slope ∑v∈V∑p∈Pw0lvxvp≥LFmax+θ−M(1−iw)∀w∈W (9) If i = 1 and j = 0: One vehicle (sum of the vehicles shorter than the horizontal part of the wagon) ∑v∈V∑p∈Pw0lvxvp≤LFmax+Miw∀w∈W (10) ∑v∈V∑p∈Pw0lvxvp≥LFmax+Lsl+θ−M(1−jw)∀w∈W (11) If j = 2: Two vehicles (sum of the vehicles longer than the horizontal part of the wagon and than the length of a slope) ∑v∈V∑p∈Pw0lvxvp≤LFmax+Lsl+Mjw∀w∈W (12)

[0037] In addition, the optimization algorithm uses the following individual conditions: ∑p∈SLpxvp≤∑p∈SLpxv*p+(1−∑p∈SLxv*p)|V|∀SL∈PL,v∈V,v*∈{v+1,…,V} (13) All vehicles v* behind v must be on a later position or the other level ∑p∈Px(v+1)p≤∑p∈Pxvp∀v∈{0,…,V−1} (14) If the vehicle is loaded directly behind v (i.e. v+1), then v must also be loaded

[0038] Using the optimization algorithm and the mathematical conditions mentioned above, the complete car train 14 can be loaded and precalculated.

[0039] After the positions have been precalculated, in a third step, the position of the vehicle 16 provided from the vehicle rows 34 is displayed. For this purpose, the evaluation device 20 controls the display device 22 via the data line 30 such that the signal lamp 32a, 32b lights up that corresponds to the previously determined position of the vehicle 16 to be loaded from the vehicle row 34. In this case, the upper signal lamp 32a is controlled so that the provided vehicle is loaded onto the upper loading level 12a, as shown in Fig. 1 is evident.

[0040] Further embodiments of the previously described method for determining the position of the vehicles 16 on the car train 14 are explained below, wherein the same reference numerals are used for identical or functionally equivalent parts or steps.

[0041] In the method according to the second embodiment, in the first step, the respective first vehicle 16 of the rows 34 is provided, as in Fig. 4. In the second step S2, the following individual mathematical conditions are applied to the optimization algorithm: ∑v∈Vzvn≤1∀n∈N (15) Always choose only one vehicle ∑n∈Nzvn≤1∀v∈V (16) Select each vehicle only once ∑n∈N(|N|−n)zvn≤∑n∈N(|N|−n)zun∀(v,u)∈A (17) Vehicle u is always chosen before its successor v ∑n∈Nnzvn−∑n∈Nnzv*n≤|N|(2−xvp*−xv*p)∀v∈V,v*∈V / {v},p∈P,p*∈SPp (18) If two vehicles v* ≠ v are loaded onto the same level and v should be in a position that is in front of the position of v*, then v must also be chosen before v* ∑p∈Pxvp≤∑p∈Pxup∀(v,u)∈A (19) When a vehicle is charged, its predecessor must also be charged

[0042] With the new decision variable and quantity: SP p SP p ⊆ P(P) contains a subset of positions for each p ∈ P (|SP p | = |P|). Each of these subsets contains all positions preceding the position p ∈ P (e.g., for P = {0, 1, 2, 3}, SP p = {{}, {0}, {0, 1}, {0, 1, 2}}). The sets in SP p are ordered as the set P is ordered. Therefore, the set SP p be indexed with any p ∈ P (e.g. in the previous example: if p = 2, then SP2 = {0, 1}). znv={1,if the vehicle v∈V is the n-th loaded vehicle0,all other cases

[0043] The rolling horizon heuristic is used to solve the algorithm: δh is fixed ywl=∑v∈V∑p∈PwllvMxvp∀w∈W,l∈L (20) Space utilization (based on vehicle length) in the individual wagons on both levels max∑w∈W∑l∈Lywl (21) Space utilization as a secondary objective to avoid uneven distribution of large and small vehicles

[0044] In the method according to the third embodiment, in the first step, the vehicles 16 are randomly provided from the rows 34, as shown in Fig. 5. In the second step, the following individual mathematical conditions are applied to the optimization algorithm: ∑p∈Px(v+1)p≤∑p∈Pxvp∀v∈{0,…,V−1} Vehicles have a certain priority. We need to ensure that when one vehicle is loaded, all vehicles with a higher priority are also loaded.

[0045] To solve the algorithm, the rolling horizon heuristic is then also applied as in the method according to the second embodiment.

[0046] With the system 10 and the method, it is possible to determine the positions of the vehicles 16 on the car train 14 in a capacity-optimized manner before the manual loading of the vehicles 16 and also to enable an automated assignment of the vehicles 16 to the loading levels 12a, 12b as well as a determination of the loading sequence. To determine the positions, the problem is formulated as an assignment problem. Based on known vehicle parameters or vehicle data, taking into account the vehicle mix and the position of the vehicles in the vehicle fleet or the loading blocks, the problem is solved with the aid of a mathematical model, in particular a mathematical solver such as Gurobi, in order to distribute the vehicles 16 as best as possible between the two loading levels 12a, 12b of the car train 14 even before loading. List of reference symbols 10 systems 12a upper loading level 12b lower loading level 14 Car train 16 vehicles 18 Vehicle inventory 20 Evaluation device 22 Display device 24 computing unit 26 Storage device 28 Signal lamp 30 data lines 32a upper signal lamp 32b lower signal lamp 34 row QUOTES CONTAINED IN THE DESCRIPTION

[0000] This list of documents submitted by the applicant was generated automatically and is included solely for the convenience of the reader. This list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions. Cited patent literature

[0000] DE 295 16 152 U1

[0002]

Claims

[1] Method for loading a car train (14) with vehicles (16), comprising the following steps: a. Providing at least one vehicle (16) from a vehicle fleet (18), wherein the positions of the vehicles (16) within the vehicle fleet (18) are known; b. Determining a position for each vehicle (16) from the vehicle fleet (18) on the car train (14) based on at least one vehicle parameter and / or at least one car train parameter by means of an optimization algorithm; and c. Displaying the determined position for the provided vehicle (16) by means of a display device (22). [2] Method according to claim 1, characterized by that the vehicles (16) are arranged in rows of vehicles (34) before being made available. [3] Method according to claim 2, characterized by that in step a) the vehicles (16) the vehicle rows (34) are provided one after the other. [4] Method according to claim 2, characterized by that in step a) the first vehicle (16) of each of the vehicle rows (34) is provided. [5] Method according to claim 2, characterized by that in step a) the vehicles (16) are provided at random from the rows (34). [6] Method according to one of the preceding claims, characterized by that in step b) a loading level of the car train is determined as the position. [7] Method according to one of the preceding claims., characterized by that the at least one vehicle parameter comprises a length of the vehicle (16), a height of the vehicle (16) and / or a weight of the vehicle (16). [8] Method according to one of the preceding claims, characterized by in that the at least one car train parameter comprises an available length of the loading level (12a; 12b), a height of the loading level (12a; 12b), a car train type and / or an available load capacity of the loading level (12a; 12b). [9] System (10) for loading a car train (14) with vehicles (16) for carrying out a method according to one of claims 1 to 8, comprising an evaluation device (20) which is set up to determine, based on known positions of the vehicles (16) within a vehicle fleet (18), a position for each vehicle (16) from the vehicle fleet (18) on the car train (14) on the basis of at least one vehicle parameter and / or at least one car train parameter by means of an optimization algorithm, and at least one display device (22) which is set up to display the determined position. [10] A computer program product comprising instructions causing the system (10) of claim 9 to carry out the method of any one of claims 1 to 8.

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