Method and system for the management and optimization of logistics operations using autonomous transport platforms

Abstract

The invention relates to a computer-implemented method for managing and optimizing logistics operations using autonomous transport platforms, the method comprising the following steps: - constructing a representation of a transport network comprising paths between strategic points, the strategic points including transfer zones, - constructing a representation of the autonomous transport platforms, - iteratively planning to establish, for each logistics operation, a route and the autonomous transport platform that will execute the route, by carrying out the sub-steps: - assigning a platform, - determining possible routes being the paths between an initial position, transfer zones and a final position, by reducing a discrepancy between the schedules of the possible routes and the time constraints of the logistics operation, - determining, for each possible route, the energy consumed.- Select the route from the possible routes. Figure for the abbreviation: Figure 1,

Description

Title of the invention: Method and system for the management and optimization of logistics operations using autonomous transport platforms. TECHNICAL FIELD OF THE INVENTION

[0001] The invention relates to methods and systems for managing and optimizing logistics operations using autonomous transport platforms circulating within a transport network. More particularly, the invention relates to route determination through the transport network and the allocation of autonomous transport platforms to logistics operations.

[0002] It relates to the technical field of logistics using autonomous transport platforms. STATE OF THE ART

[0003] The management of logistics operations using autonomous transport platforms presents many specific challenges that existing logistics operation management methods fail to overcome effectively.

[0004] For example, a method of managing logistics operations may involve defining a route through a transport network to reduce the necessary transit times.

[0005] Another example is to assign logistics transport missions to a fleet of vehicles, also referred to as a platform in this text, by maximizing the use of the vehicles in the fleet.

[0006] Route definition can also be carried out in such a way as to deliver cargo just in time so as to reduce cargo storage times.

[0007] The arrival of new transport technologies, for example the electrification of vehicles, brings new constraints for the management of logistics operations using autonomous transport platforms.

[0008] Current logistics operations management methods are particularly poorly suited to transport platforms with limited range compared to traditional transport platforms such as trains or diesel trucks. They are also poorly suited to transport platforms equipped with electric batteries that supply the energy used for transport, requiring long charging times compared to the time needed to refuel a traditional freight vehicle. Description of the invention

[0009] The present invention aims to remedy all or part of the disadvantages of the prior art mentioned above.

[0010] To this end, according to a first aspect, the invention relates to a computer-implemented method for the management and optimization of logistics operations using autonomous transport platforms circulating in a transport network, each logistics operation comprising a starting point, an arrival point and time constraints, the method comprising the following steps: - to construct a representation of the transport network, the network representation including paths between strategic points of the transport network, the strategic points including transfer zones, each path having indicative data of a travel time and energy consumed to travel the corresponding path, - to construct a representation of autonomous transport platforms, the representation of the platforms including, as a function of time, for each platform, a position in the transport network, - to plan logistics operations in order to establish, for each logistics operation, a route and the autonomous transport platform that will carry out the route, planning being an iterative process comprising the following sub-steps: - assign an autonomous transport platform to each logistics operation using the positions in the transport network of the platform representation and the transfer zone corresponding to the starting point, - determine for each logistics operation possible routes, each possible route being the paths between an initial position in the transport network of the platform assigned to the corresponding logistics operation, the transfer zone corresponding to the starting point, the transfer zone corresponding to the arrival point and a final position in the transport network of the platform assigned to the corresponding logistics operation, using indicative travel time data, so as to reduce a gap between the schedules of the possible routes and the time constraints of the logistics operation, - determine, for each possible route, using indicative data on the energy consumed to travel the paths of the corresponding possible route, the energy consumed to travel the corresponding possible route, - Select the route for each logistics operation from among the possible routes.

[0011] A logistics operation is understood to mean the transport of a cargo, for example a container, from a point of departure to a point of arrival.

[0012] The time constraints of a logistics operation, for example, refer to a desired departure date and time and a desired arrival date and time. A time constraint of a logistics operation could also be, for example, a time window for loading cargo.

[0013] An autonomous transport platform is understood to be a cargo transport vehicle which also carries the energy necessary for its movement and whose piloting is automatic.

[0014] The paths between strategic points are understood to mean, for example, segments of a representation of a star network or a grid network, the strategic points being nodes between the segments.

[0015] The initial position of a platform in the transport network is understood to mean, for example, a storage warehouse for autonomous transport platforms or an energy recharging zone for an autonomous transport platform or an arrival point of a previous logistics operation.

[0016] The final position of a platform in the transport network is understood to mean, for example, a storage warehouse for autonomous transport platforms or an energy recharging zone for an autonomous transport platform or a starting point for a subsequent logistics operation.

[0017] A transfer zone is understood to be a strategic point in the representation of the transport network corresponding to the location of an area suitable for loading or unloading cargo from one of the autonomous transport platforms.

[0018] Indicative data for travel time and energy consumed means a distance and possibly other data such as, for example, an authorized speed, a slope, a radius of curvature and an indication of blocking as well as a waiting time associated with the blocking.

[0019] Such arrangements allow, during the management of logistics operations, the planning and optimization of the execution of logistics operations using autonomous transport platforms where autonomy constraints are critical for logistics operations. Autonomy constraints are understood to mean, for example, a relatively short distance traveled before requiring recharging compared to other transport platforms, or a relatively long recharging time compared to other transport platforms.

[0020] In particular embodiments the invention may further comprise one or more of the following features, taken individually or in all technically possible combinations.

[0021] According to one embodiment, the planning of logistics operations is carried out by optimizing the total energy consumed to perform the logistics operations, the total energy consumed being the sum of the energies consumed to travel the routes.

[0022] Optimizing total energy consumption means, for example, reducing this total energy consumption as much as possible. This can be done by prioritizing this route selection criterion among the different possible routes.

[0023] Optimizing the total energy consumed makes it possible to reduce the amount of energy needed to ensure logistical operations using the process according to the invention.

[0024] According to one embodiment, the planning of logistics operations is carried out by optimizing the duration of the logistics operations so as to complete each route as quickly as possible.

[0025] Optimizing the time required to carry out logistical operations makes it possible to ensure logistical operations in a very responsive manner, which increases the comfort of the beneficiaries of these operations.

[0026] According to one embodiment, the allocation of autonomous transport platforms to each logistics operation is achieved by balancing a distribution of a workload of the autonomous transport platforms.

[0027] Balancing the workload distribution of autonomous transport platforms makes it possible to standardize the wear and fatigue of the platforms and thus increase the lifespan of the platforms, avoid overloading a platform and simplify the organization of maintenance.

[0028] According to one embodiment, the strategic points also include charging zones, in which the representation of the platforms also includes for each platform a level of autonomy as a function of time and in which the planning also includes a sub-step of adaptation, for each logistics operation, of the routes to add a charging phase to a charging zone if the energy consumed to travel the corresponding route exceeds the level of autonomy of the autonomous transport platform assigned to the corresponding logistics operation.

[0029] Charging zones are understood to mean a station enabling one or more autonomous transport platforms to recharge a battery or tank with energy used by the autonomous transport platform to move.

[0030] The energy used may be electrical energy, a traditional fuel, or an alternative fuel such as hydrogen, for example.

[0031] Taking charging zones into account in the representation of the transport network and in the route adaptation sub-step makes it possible to plan autonomous transport platform charging schedules, charging times, and route adjustments. In this way, logistics operations planning will be more robust, meaning less likely to change at the last minute.

[0032] According to one embodiment, each logistics operation also includes a cargo characteristic, wherein the indicative data of energy consumed to travel the paths are a function of the cargo characteristic, and wherein the energy consumed to travel the routes is determined using the cargo characteristic of the corresponding logistics operation.

[0033] A characteristic of a cargo is understood to mean, for example, a cargo weight or an indicator of the additional drag caused by the cargo. This characteristic can influence the energy consumed by the autonomous transport platform carrying the corresponding cargo for certain routes. For example, a route with a significant positive gradient, also called an incline, will require more energy if the cargo is heavy.

[0034] Such provisions allow the method according to the invention to perform logistics operations planning using an estimate of energy consumed that is closer to the actual energy consumed. The logistics operations management proposed by the invention will thus be more robust.

[0035] According to one embodiment, the indicative data for a travel time of the paths are time-dependent so that the indicative data for a travel time of the paths take into account the influence of variations in path congestion.

[0036] According to one embodiment, the representation of the transport network is periodically updated using data representative of the state of the transport network at the time of the update and data representative of the state of the transport network during an estimation period subsequent to the time of the update.

[0037] Periodic updating of the representation of the transport network makes it possible to take into account the latest developments in the represented transport network, for example, the change in the direction of traffic on a path or the congestion or blockage of a path.

[0038] The term "estimation period after the update" refers to a future duration or period during which an estimate or simulation of the evolution of the state of the transport network is carried out. This estimate could be, for example, a forecast of traffic congestion or a forecast of blockages due to planned works.

[0039] Such provisions make it possible to further increase the robustness of the management of logistics operations by refining the planning carried out by the process according to the invention.

[0040] According to one embodiment, the representation of the autonomous transport platforms is periodically updated using data representative of the state of the platforms at the time of the update.

[0041] Such provisions make it possible to further increase the robustness of the management of logistics operations by refining the planning carried out by the process according to the invention.

[0042] According to one embodiment, the transport network is a railway network.

[0043] According to one embodiment, the location of each transfer zone of the The representation of the transport network is determined based on proximity to a road network.

[0044] Determining the location of transfer zones based on proximity to a road network makes it possible to adapt the process according to the invention particularly to the management of logistical operations involving the loading and unloading of cargo from or to means of road transport.

[0045] According to one embodiment, each autonomous transport platform includes a cargo loading and unloading system.

[0046] An onboard loading and unloading system on each autonomous platform allows for great flexibility in the location of transfer areas. Cargo transfer can be carried out without requiring the transfer area to have an independent loading system such as a mechanical crane, for example.

[0047] According to a second aspect, the invention relates to a system arranged for managing and optimizing logistics operations using autonomous transport platforms circulating in a transport network, each logistics operation comprising a starting point, an arrival point, and time constraints, the system comprising: - means to construct a representation of the transport network, the network representation comprising paths between strategic points of the transport network, the strategic points including transfer zones, each path having indicative data of a travel time and energy consumed to travel the corresponding path, - means to construct a representation of autonomous transport platforms, the representation of the platforms including, as a function of time, for each platform, a position in the transport network, - means to plan logistics operations in order to establish, for each logistics operation, a route and an autonomous transport platform, planning being an iterative process, the planning means being configured to: - assign an autonomous transport platform to each logistics operation using the positions in the transport network of the platform representation and the transfer zone corresponding to the starting point, - determine for each logistics operation possible routes, each possible route being the paths between an initial position in the transport network of the platform assigned to the corresponding logistics operation, the transfer zone corresponding to the starting point, the transfer zone corresponding to the arrival point and a final position in the transport network of the platform assigned to the corresponding logistics operation, using indicative travel time data, so as to reduce a gap between the schedules of the possible routes and the time constraints of the logistics operation, - determine, for each possible route, using indicative data on the energy consumed to travel the paths of the corresponding possible route, the energy consumed to travel the corresponding possible route, - Select the route for each logistics operation from among the possible routes.

[0048] Means for constructing a representation of the transport network and means for constructing a representation of autonomous transport platforms include, for example, a server comprising at least one computing device, which may be a processor, electronic memory and communication links with databases containing information concerning the transport network and a fleet of autonomous transport platforms and possibly with sensors equipping the transport network and the autonomous transport platforms.

[0049] Means for planning logistics operations include, for example, a server comprising at least one computing device, which may be a processor, electronic memory and communication interfaces enabling it to receive the requirements of each logistics operation and enabling it to access representations of the transport network and autonomous transport platforms.

[0050] Such provisions allow the system according to the second aspect of the invention to implement the management and optimization of logistics operations using autonomous transport platforms.

[0051] According to a third aspect, the invention relates to a non-transient, computer-readable medium comprising instructions which, when executed by at least one processor of a computing device, cause the computing device to implement the method according to one of the embodiments of the first aspect of the invention. BRIEF DESCRIPTION OF THE FIGURES

[0052] Other advantages, purposes and particular features of the present invention will become apparent from the following non-limiting description of at least one particular embodiment of the devices and methods of the present invention, with reference to the accompanying drawings, in which: • [Fig.1] is a schematic representation of an example of the process according to the first aspect of the invention; • [Fig.2] is a schematic representation of an example of planning according to the first aspect of the invention; • [Fig.3] is an example of a representation of the transport network; • [Fig.4] is a schematic representation of an example of routes; • Fig. 5 is a schematic representation of an example of the system according to the second aspect of the invention. DETAILED DESCRIPTION OF THE INVENTION

[0053] The present description is given as a non-limiting example of an embodiment.

[0054] Figure 1 is a schematic representation of an example of the method according to the first aspect of the invention. The method is implemented by a computer. It allows for the management and optimization of logistics operations using autonomous transport platforms. The autonomous transport platforms operate within a transport network. The transport network may be a rail, road, waterway, or other type of transportation network. The logistics operations concern freight transport; each operation is initiated by a request from a logistics operator. The request specifies a shipment, its desired departure point, its desired arrival point, and time constraints such as the desired dates and times for departure and, if applicable, arrival.Time constraints could also be a time slot or time window during which the cargo can be loaded onto one of the autonomous transport platforms at the point of departure.

[0055] The application may specify cargo characteristics such as mass, container type, and center of gravity. An autonomous transport platform is a cargo transport vehicle that can be driven automatically and whose energy used for movement is carried on board the platform, unlike, for example, a train powered by an overhead line. The autonomous transport platform could be an electric vehicle with a battery or any other vehicle with a reduced range compared to current diesel trucks and trains.

[0056] The method according to the invention comprises a step 101 of constructing a representation of the transport network 1. The representation of the transport network 1 is a digital model containing information about the transport network relevant to the planning of logistics operations using that transport network. The representation of the transport network 1 may be, for example, a directed graph comprising nodes and segments. A node of the representation of the transport network is also called a strategic point in this text. A strategic point may correspond to an intersection of the transport network or to another type of location of particular interest.Locations of particular interest within the transport network, corresponding to strategic points in the transport network representation, may include locations enabling cargo transfer to or from autonomous transport platforms, energy recharging points for autonomous transport platforms, or storage and maintenance points for autonomous transport platforms. A segment of the transport network representation is also referred to as a path in this document. A path corresponds to a transit lane within the transport network between two adjacent strategic points. The transport network model includes, for each path, data relevant to the planning of logistics operations using the transport network.These relevant data are indicative of the time required for one of the autonomous transport platforms to travel the corresponding path and indicative of the energy consumed by one of the autonomous transport platforms to travel the corresponding path. These indicative data include, at a minimum, a distance and may also include a speed, a path gradient, and the radii of curvature of any curves the path may present. The process described includes a step 102 for constructing a digital model of the fleet of autonomous transport platforms 2. The digital model is a representation of the autonomous transport platforms 2; this representation 2 includes, in particular, the position of each platform as a function of time. This representation 2 will allow the simulation of the platforms' movements within the transport network.The representation of autonomous transport platforms 2 may also include information about each platform, such as its status, autonomy level, and mileage. The status of a platform could be, for example, available, on a mission, or requiring maintenance. Once the representations of the transport network and the fleet of transport platforms are complete... autonomously constructed, the planning of the execution of logistical operations is carried out iteratively.

[0057] Upon receipt of each request for a new logistics operation, each logistics operation is replanned, prioritizing the execution of previously received logistics operations. If the planning results in a solution, the last request received is confirmed; otherwise, adjustments to the starting and / or ending points and / or time constraints may be proposed to the logistics operator requesting the new logistics operation. The operator may then accept the adjustments or cancel their request. Requests for new logistics operations are possible up to a certain time, for example, one day, before the start of the logistics operations. The routes and assignments are then finalized to avoid modifying the plan just before or during the execution of the logistics operations.

[0058] Replanning all logistics operations upon receipt of each new logistics operation request, if the new requests are not received late, allows for optimal planning according to selected criteria taking into account all logistics operation needs.

[0059] During planning 110, the starting point and the arrival point of each logistics operation are each associated with a transfer zone in the representation of the transport network. Planning 110 leads to defining a route and assigning an autonomous transport platform for each operation.

[0060] The planning step 110 includes a substep 111 for assigning an autonomous transport platform to each logistics operation. This assignment 111 is carried out using representations of the transport network and the autonomous transport platforms. This assignment can, for example, be performed by sorting the logistics operations chronologically by departure date and time and assigning to each logistics operation the available autonomous transport platform closest to the departure point at the desired departure date and time.

[0061] The following substeps are an example of allocating autonomous transport platforms to logistics operations:

[0062] 1. Sorting and selection of logistics operations: The operations are sorted based on the start time to ensure they are processed in chronological order. This minimizes the risk of an urgent operation being overlooked.

[0063] 2. Filtering of compatible platforms: For each operation, filter the platforms available according to several criteria: • Travel time: The platform must be able to reach the transfer zone corresponding to the loading point within a time window loading, by choosing the fastest route taking into account travel time and waiting times caused for example by blockages or busy roads. • Range: verification that the platform has sufficient range to complete the journey without needing immediate recharging. If necessary, calculation of a stop at a charging station.

[0064] 3. Sorting of platforms by time: Once the compatible platforms have been identified, They are sorted according to total time (travel time + any waiting time due to blocked routes). This approach allows prioritizing the platforms that can complete the operation in the shortest possible time.

[0065] 4. Route optimization using a Dijkstra algorithm to guarantee that the platforms always take the fastest route, which ensures smooth operations management and optimal resource utilization.

[0066] According to one embodiment, the allocation of one of the autonomous transport platforms to each logistics operation is carried out taking into account the workload of each platform. This workload may be associated, for example, with the mileage of each platform. Taking into account the workload of each platform during allocation makes it possible to distribute this workload across the entire fleet and thus reduce and spread maintenance requirements.

[0067] The planning 110 also includes a step 112 for determining the possible routes for each logistics operation. The possible routes are determined using the representation of the transport network in such a way as to establish the sequence of paths and strategic points to be followed by the autonomous transport platform assigned to the corresponding logistics operation. A pathfinding algorithm such as Dijkstra's algorithm may be used for this determination 112. The possible routes are determined so as to connect an initial location and a final location of the assigned platform by passing through the strategic point corresponding to the starting point and then through the strategic point corresponding to the arrival point. A completion time for each possible route is determined using indicative travel time data for the paths.This data is present in the representation of the transport network. Indicative travel time data may include distances, permitted speeds, and potential waiting times due to blockages or busy routes.

[0068] According to one embodiment, the indicative travel time data for the paths vary over time in order to represent variations in the congestion of the corresponding paths. A path can be congested and requiring a speed reduction or being blocked for a certain time and requiring a waiting period.

[0069] The completion time for each route is determined by taking into account loading and unloading times at the transfer zones. Determining the completion time leads to establishing route schedules, including the date and time for loading the cargo for the logistics operation and the date and time for unloading the cargo. The possible routes are calculated in such a way as to limit the discrepancy between the route schedules and the time constraints of the corresponding logistics operation. In practice, it is very rare to ensure a match between the time constraints received when the logistics operation is requested and the schedules of the possible routes. To overcome this problem, the time constraints are expressed as time ranges to allow for flexibility within these time ranges for planning logistics operations.The precise final times within these time slots can then be fixed for a predefined duration, for example 24 hours or another duration, before the start of the planned logistical operations.

[0070] The planning 110 then includes, for each possible route, a determination 113 of the energy consumed to travel the corresponding possible route. The energy consumed to travel the corresponding possible route makes it possible to anticipate the capacity of an autonomous transport platform to complete the route. The calculation of the energy consumed 113 to travel one of the routes may be the sum of the energies consumed to travel the paths included in the route. The energy consumed to travel one of the paths is determined using the indicative energy consumption data contained in the representation of the transport network. This indicative data includes at a minimum a distance and may also include a path gradient and a path radius of curvature.A large radius of curvature can cause autonomous transport platforms traveling along the path to slow down and then accelerate, which impacts energy consumption compared to maintaining a constant speed.

[0071] Considering an autonomous ground transport platform equipped with an electric battery, the energy consumed to travel a path can be determined by taking into account the propulsion or traction phase and the regenerative braking phase. The energy consumed to travel a path can, for example, be calculated using the following equations:

[0072] [Math.l] p ^batt ways— J

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[0089] With Econs in Watt hour per meter (Wh / m), Ebatt in Watt Hour (Wh) and d in meter (m). [Math.2] Ebatt = battet) ^t T being the time in hours (h) to travel a finite element of the path and Pbatt a power in Watts (W). With [Math.3] Ebatt Pma under traction, P in under braking Pout can be estimated as follows: [Math.4] P jj __ 1 wheei * out “ n n being the efficiency of a propulsion system for autonomous transport platforms and Pwheel being the power developed by wheels determined as follows: [Math.5] Pwheel - P whed — Prr + P ad + Pc g + Paccel Frr = Cf-m gcos (a) Frg-m gsin(a) F g dv accel &dt With - v: the speed of the platform on the finite element considered - Cr: a coefficient of rolling resistance - m: the mass of the platform and its possible load - g: gravitational acceleration - a: the slope of the finite element under consideration - pa: the density of the air - Cd: the aerodynamic drag coefficient - Af: the frontal surface of the platform - ô: a constant of inertia of the platform Pin can be estimated as follows: [Math.6] Pin = kPrege„-0 <k<l

[0090] With k an energy recovery coefficient and Pregen a braking power.

[0091] These equations allow for a precise estimation of the energy consumed by a platform to travel a path. They can also be used with simplifications if less information is available to perform the calculation. For example, an average energy per meter (Econs) can be considered to quickly and easily determine an estimate of the energy consumed to travel a route, requiring only the distance to be covered to perform the estimation.

[0092] According to one embodiment, a characteristic of the cargo is indicated by the logistics operator when requesting the handling of a new logistics operation. This characteristic could, for example, be a weight of the cargo which will allow the calculation of the energy consumed to be refined by contributing to the mass parameters of the platform and its possible load and to the inertia constant of the platform present in equation [Math. 5].

[0093] According to one embodiment, the representation of the transport network also includes strategic points corresponding to areas allowing the recharging of autonomous transport platforms. If the energy required to complete a possible route is greater than the autonomy of the platform assigned to the logistics operation for which the possible route is determined, a step is taken to adapt the possible route in order to add a passage through a recharging zone, while taking into account the time required to recharge the platform in the route schedule.

[0094] Once the energy required to carry out the possible routes has been determined, the route adopted for the logistics operation being planned is selected. Several criteria may be used to select the route from among the possible routes, for example, the route requiring the least energy, the route whose schedules are closest to the time constraints of the logistics operation, the route allowing the cargo to arrive at the destination point the fastest, or a combination of these criteria.

[0095] According to one embodiment, the representation of the transport network is periodically updated from, for example, a database or sensors that reflect the state of the transport network as it is known or measured at the time of the update. In this way, planning can be carried out taking into account possible changes in the state of the transport network, such as, for example, roadworks, congestion, and accidents. The update of the representation of the transport network may also include data from a simulation of a future predicted state of the transport network. The simulation of a predicted state The future development of the transport network may include consideration of the use of the network by vehicles other than autonomous transport platforms.

[0096] According to one embodiment, the representation of the autonomous transport platforms is periodically updated to take into account possible changes in the platform fleet such as the failure of one of the platforms or the addition of a new platform to the fleet.

[0097] Iterative planning of logistics operations makes it possible to determine different possible routes and allocations, to compare the different solutions and their energy consumption, and to adopt, for each logistics operation, the allocation and route that optimizes the management of logistics operations, particularly with regard to energy consumption. According to one embodiment, the solution selected is the one with the lowest total energy consumption. The total energy consumption is the sum of the projected energy consumption required to carry out all the logistics operations.

[0098] According to one embodiment, the solution chosen is the one offering the best responsiveness. That is to say, the solution enabling each of the logistical operations to be carried out as quickly as possible.

[0099] According to one embodiment, the chosen solution is one that allows a workload, for example equivalent to the number of kilometers to be traveled, to be distributed as fairly as possible among the autonomous transport platforms constituting the fleet. In this way, the risks of breakdowns are limited and fleet maintenance can be more easily ensured.

[0100] A combination of these different approaches to selecting a planning solution may also be adopted, in particular by prioritizing or weighting these approaches relative to each other.

[0101] Figure 2 is a schematic representation of a planning example according to the first aspect of the invention. In this example of implementing planning step 110, two types of adjustments are possible and can be proposed to the logistics operator submitting a request to handle a new logistics operation. Planning 110 may include a step for adjusting the transfer zone corresponding to the starting point of the logistics operation. Planning 110 may also include a step for adjusting a time range within the time constraints of the logistics operation request being processed. A step for returning to a platform storage warehouse or returning to a reloading zone is represented in this planning example 110 according to the first aspect of the invention.

[0102] Figure 3 is an example of a representation of the transport network 1. Several strategic points 12 are represented; these strategic points may be Transfer zones, charging zones, and warehouses for storing or maintaining autonomous transport platforms are all strategic points connected by paths. These paths can be modeled numerically as a succession of discrete points, each with characteristics such as latitude, longitude, distance from the previous point on the path, gradient, and permitted speed. The concatenation of the characteristics of each discrete point on a path can provide indicative data for travel time and energy consumption. The transport network shown in [Fig. 3] is a rail transport network.The transfer zones were determined based on proximity to a road network in order to allow for easy transfer of cargo between a truck using the road network and one of the autonomous transport platforms using the rail network.

[0103] A railway network, also called a rail network, is a system structured around several tracks that can be represented numerically by paths and numerous nodes. Some nodes correspond to intersections or bifurcations, which allow tracks to cross or separate. Thanks to this node structure, it is possible to accurately estimate the distances between each pair of nodes while respecting the direction of travel.

[0104] A directed graph modeling this railway network may contain thousands of nodes, some of which have been selected as strategic points because they play an essential role in the planning of logistical operations.

[0105] The representation of the railway network may include blocks, and the determination of possible routes may take into account the blocks and their occupancy to estimate the timetables for each possible route. Blocks are understood to be a system for ensuring a certain safety distance between different vehicles, which may be autonomous transport platforms, operating within a railway network.

[0106] According to one embodiment, each autonomous transport platform includes a cargo loading and unloading system. In this way, the transfer zones can correspond to locations in the transport network where no specific cargo transfer equipment is installed. This provides greater flexibility in carrying out logistics operations using the autonomous transport platforms.

[0107] Figure 4 is a schematic representation of an example of routes 5 resulting from the process 100 according to the invention. These routes 5 make it possible to carry out four logistical operations using two autonomous transport platforms called "Steffi" in Figure 4. For each of the routes, the schedules and a level of autonomy are indicated. Loading and unloading times are planned at transfer zones. Recharging times are also planned at charging zones. In this example route, initial and final positions for the autonomous transport platforms are planned in maintenance warehouses. The number of operating hours for each autonomous transport platform can also be determined and used as an approximation of each platform's workload.

[0108] According to examples of embodiments of process 100, the adaptation 114 of the possible routes 112 may include the following sub-steps:

[0109] Range Prevention: A range prevention constraint may be implemented to prevent one of the autonomous transport platforms from exceeding its range limit and to ensure that it can complete its assigned logistics operation without risk of failure. This constraint may also ensure that, after reaching the transfer zone corresponding to the arrival point of the logistics operation, the platform still has sufficient range to reach a charging zone. If it is determined that the remaining range will not allow the platform to reach a charging zone after reaching the transfer zone corresponding to the arrival point of the logistics operation, a stop at a charging zone before the transfer zone is added to the route.

[0110] Selection of charging zones: A selection of charging zones may be made to minimize the impact of adding a stop at a charging zone in the possible route being adapted. Several parameters may be taken into account for this selection, such as the position over time of the platform assigned to the logistics operation for which the possible route has been determined, the platform's range over time, the location of strategic points in the possible route being adapted, the capacity of each charging zone, and the charging speed of each charging zone. One of the objectives is to limit the waiting time for a platform to recharge the energy used for its movement. If the charging zone whose location is most suitable for adding to the possible route being adapted is unavailable, two options may be considered.This will involve either adding a waiting time for the charging zone to become available or selecting a charging zone in a less suitable location. The option with the least impact on the route schedule will be adopted.

[0111] According to examples of embodiments of the process 100, the determination 112 of possible routes may include a substep of selecting a storage warehouse for autonomous transport platforms as the final location of each possible route. The selection of the storage warehouse, also called a depot, may take into account taking into account the capacity of each depot. If the capacity of the depot whose location is most suitable for the possible route is reached, another depot is chosen for the final position of the corresponding possible route.

[0112] Figure 5 is a schematic representation of an example of the system according to the second aspect of the invention. The system comprises means 20, 21, and 22 for constructing a representation of the transport network, a representation of the fleet of autonomous transport platforms, and for carrying out the planning 110 of the process according to the first aspect of the invention. The means 20, 21, and 22 may be separate or a single, shared means for carrying out different steps. The means 21, 22, and 23 are, for example, one or more computer servers comprising at least one computing device such as a processor and at least one persistent electronic memory. The electronic memory may store the representations of the transport network and the autonomous transport platforms in the form of dynamic digital models.A dynamic digital model is defined as a digital model whose internal parameters or variables can vary during simulations or forecasts. Means 20 and 21 may include digital connections with one or more databases and, potentially, with sensors equipping autonomous transport platforms and the transport network. These digital connections enable the collection of information necessary for constructing representations of the transport network and autonomous transport platforms and, in certain embodiments, for performing periodic updates to these representations. Means 22 may also include digital connections with computing devices such as, for example, a personal computer or a mobile phone available to a logistics operator.Through these digital connections, the means 22 collect requests for handling logistics operations from logistics operators. These requests include a starting point, an ending point, and time constraints for the corresponding logistics operation. The means 20, 21, and 22 are configured to implement the process 100 according to the first aspect of the invention.

[0113] Digital connections may be established through an internet-type communication network or another particular type.

[0114] Such arrangements enable the system according to the second aspect of the invention to carry out the management and optimization of logistical operations using autonomous transport platforms.

[0115] The invention according to a third aspect relates to a non-transient, computer-readable medium comprising instructions which, when executed by at least one processor of a computing device, for example a processor of the system according to The second aspect of the invention involves the computing device implementing the method according to one of the embodiments of the method described in the first aspect of the invention. A non-transient, computer-readable medium is understood to mean, for example, electronic memory such as one or more hard drives or SSDs, or another type of persistent electronic memory.

Claims

1. Demands A computer-implemented method for managing and optimizing logistics operations using autonomous transport platforms circulating within a transport network, each logistics operation comprising a starting point, an arrival point, and time constraints, the method comprising the following steps: - constructing a representation of the transport network, the representation of the transport network comprising paths between strategic points of the transport network, the strategic points including transfer zones, each path having indicative data of a travel time and energy consumed to travel the corresponding path, - to construct a representation of autonomous transport platforms, the representation of the platforms including, as a function of time, for each platform, a position in the transport network, - to plan logistics operations in order to establish, for each logistics operation, a route and the autonomous transport platform that will carry out the route, planning being an iterative process comprising the following sub-steps: - assign an autonomous transport platform to each logistics operation using the positions in the transport network of the platform representation and the transfer zone corresponding to the starting point, - determine for each logistics operation possible routes, each possible route being the paths between an initial position in the transport network of the platform assigned to the corresponding logistics operation, the transfer zone corresponding to the starting point, the transfer zone corresponding to the arrival point and a final position in the transport network of the platform assigned to the corresponding logistics operation, using indicative travel time data, so as to reduce a gap between the schedules of the possible routes and the time constraints of the logistics operation, - determine, for each possible route, using indicative data on the energy consumed to travel the paths of the corresponding possible route, an energy consumed to travel the corresponding possible route, - select the route for each logistics operation from among the possible routes.

2. A method according to claim 1 in which the planning of logistics operations is carried out by optimizing the total energy consumed to perform the logistics operations, the total energy consumed being the sum of the energies consumed to travel the routes.

3. A method according to claim 1 wherein the planning of logistics operations is carried out by optimizing the duration of logistics operations so as to complete each route as quickly as possible.

4. A method according to any one of the preceding claims in which the allocation of autonomous transport platforms to each logistics operation is achieved by balancing a workload distribution of the autonomous transport platforms.

5. A method according to any one of the preceding claims, wherein the strategic points also include charging zones, wherein the representation of the platforms also includes for each platform a level of autonomy as a function of time, and wherein the planning also includes a substep of adaptation, for each logistics operation, of the possible routes to add a charging phase to a charging zone if the energy consumed to travel the corresponding possible route exceeds the level of autonomy of the autonomous transport platform assigned to the corresponding logistics operation.

6. A method according to any one of the preceding claims wherein each logistics operation also includes a cargo characteristic, wherein the indicative data of energy consumed to travel the paths are a function of the cargo characteristic, and wherein the energy consumed to travel the routes is determined using the cargo characteristic of the corresponding logistics operation.

7. A method according to any one of the preceding claims, wherein the indicative data for a travel time of the paths are a function of time such that the indicative data for a travel time of The route planning takes into account the influence of variations in path congestion.

8. A method according to claim 7 wherein the representation of the transport network is periodically updated using data representative of the state of the transport network at the time of the update and data representative of the state of the transport network during an estimation period subsequent to the time of the update.

9. A method according to any one of the preceding claims wherein the representation of autonomous transport platforms is periodically updated using data representative of the state of the platforms at the time of the update.

10. A method according to any one of the preceding claims wherein the transport network is a railway network.

11. A method according to claim 10 wherein the location of each transfer zone of the representation of the transport network is determined based on proximity to a road network.

12. A method according to any one of the preceding claims in which each autonomous transport platform includes a cargo loading and unloading system.

13. A system arranged to manage and optimize logistics operations using autonomous transport platforms circulating in a transport network, each logistics operation comprising a starting point, an arrival point, and time constraints, the system comprising: - means for constructing a representation of the transport network, the network representation comprising paths between strategic points of the transport network, the strategic points comprising transfer zones, each path having indicative data of a travel time and energy consumed to travel the corresponding path, - means for constructing a representation of the autonomous transport platforms, the representation of the platforms comprising, as a function of time, for each platform, a position in the transport network.- means to plan logistical operations in order to establish, for each logistical operation, a route and a platform, autonomous transport, planning being an iterative process, the means for planning being configured for: - assign an autonomous transport platform to each logistics operation using the positions in the transport network of the platform representation and the transfer point corresponding to the starting point, - determine possible routes for each logistics operation, each possible route being the paths between the transfer zone corresponding to the starting point and the transfer zone corresponding to the arrival point, using indicative travel time data, in order to reduce the gap between the schedules of the possible routes and the time constraints of the logistics operation, - determine, for each possible route, using indicative data on energy consumption, the energy consumed to travel the corresponding possible route, - Select the route for each logistics operation from among the possible routes.

14. System according to claim 13, also comprising means for carrying out the method according to any one of claims 2 to 12.

15. Non-transient computer-readable medium comprising instructions which, when executed by at least one processor of a computing device, cause the computing device to implement the method according to any one of claims 1 to 12.

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