Method and hybrid system for determining an optimized logistics network

Abstract

The invention discloses a method and a hybrid system for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process of the goods with respect to a vehicle as a means of transport, as well as a planning system.

Description

[0001] The invention discloses a method and a hybrid system for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process of the goods with respect to a vehicle as a means of transport, as well as a planning system.

[0002] Conventional logistics systems have the major disadvantage that many intermediate steps are necessary to transport goods, packages or products from the provider (e.g. manufacturer) to the recipient (e.g. end customer).

[0003] Goods are transported between loading points, transshipment points, and unloading points. These transports have limited capacity, incur costs, and cause delays. For a given good, there are usually multiple transport chains that establish the desired connection between loading and unloading points.

[0004] However, transport companies usually have a large number of vehicles with a large number of goods to be transported.

[0005] From these options, those that meet certain optimality criteria must be filtered out. For example, costs are incurred with each loading and unloading operation, for example, due to time delays and labor costs, so it is desirable to minimize loading and unloading. On the other hand, the transport itself has costs, for example, due to wear and tear on the means of transport and fuel. It is also desirable to optimize not only the route of an individual item, but also the transport network as a whole across all vehicles.

[0006] This was previously not possible, especially with a large number of vehicles, goods and loading and unloading locations.

[0007] It is therefore an object of the invention to provide a global method and hybrid system for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process.

[0008] Furthermore, it is a task to specify an appropriate planning system.

[0009] The object is achieved by a method having the features of claim 1 and a hybrid system having the features of claim 6 as well as a planning system having the features of claim 9.

[0010] Advantageous embodiments emerge from the dependent patent claims, the description and the figures.

[0011] The task is solved by a method for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process of the goods in relation to a vehicle as a means of transport comprising the steps: - Providing a digital computer, - Providing different goods to be transported to different locations, with a number of items of each good to be transported from a first location to a second location, - Provision of various vehicles, whereby the vehicles are provided in digital form, which depict reality, - Determining travel costs from a first location to a second location with respect to a vehicle, - Determining loading and unloading costs of a vehicle to be loaded or unloaded in relation to a given number of items of goods at a location in or from the vehicle, - Generate an energy function based on the loading and unloading costs for all goods and for all vehicles and for all locations with respect to a discretized time as well as the travel costs from a first location to a second location for all locations and for all vehicles using the following binary variables: bv,t,L,k,n={1,if vehicle v is at time t at location L, n pieces of the good k are− / entald0,otherwise - Providing an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer, - Generating an initial state for the adiabatic quantum system based on the energy function and initializing the energy landscape based on the initial state and changing the initial state into an energetic ground state using the energy function in the quantum computer, whereby the energetic ground state corresponds to an energy-minimized energy function that encodes a logistics network with logistically optimized transport routes optimized with regard to loading and unloading costs as well as travel costs and the number of items of a respective good, taking into account the following constraints: ◯ the goods reach a discharge point with a correct total quantity, ◯ the goods are transported from one location in the correct total quantity, ◯ each vehicle may only be in one place at a time ◯ the goods may only be loaded or unloaded in a certain number of pieces at one time and place in one vehicle, - Measuring the basic energetic state by the digital computer after adiabatic change and encoding the measurement by the digital computer and determining and outputting based on the encoding of the logistics network optimized with regard to loading and unloading costs as well as travel costs and the number of items of a respective good with logistically optimized transport routes and using the optimized logistics network to improve the logistics planning with regard to the transport of goods.

[0012] The initial state of the quantum computer typically corresponds to a solvable quantum system in which the qubits are initialized in the ground state.

[0013] According to the invention, the g-different goods k ∈ {1, ..., g} to be transported are first provided. Such goods k can be a single good, such as a single gear or a set of screws as a single good. In this case, g-different goods are present. The goods are provided in digital form, which depicts reality.

[0014] Furthermore, the places L∈L where loading / unloading takes place. Furthermore, different vehicles v ∈ {1, ..., N V}, whereby the vehicles are provided in digital form, which reflects reality; i.e. the transporters actually available in reality, for example, of a transport company. There are N V various vehicles.

[0015] Likewise, the different quantities n(k, L, L') ∈ ℕ 0of a good k ∈ {1, ..., g} that is to be transported from a first location L to a second location L' is known. This applies to all goods k with respect to all locations L∈L

[0016] Also, the number N V of vehicles v ∈ {1, ...,N V} is known, as well as the travel costs d(L, L') from a first location L to a second location L', as well as the loading and unloading costs C (v, k, L, n) to load or unload n ∈ ℤ numbers of the good k ∈ {1, ...,g} into (or from) vehicle v at location L. Here, n is positive when the goods are unloaded and negative when the goods are loaded.

[0017] The initial state of the quantum computer typically corresponds to a solvable quantum system in which the qubits are initialized in the ground state.

[0018] The operating principle of a quantum computer has been known as a theoretical concept for a long time and has also been implemented in practice for some time. While in conventional digital computers, information is represented in bits, which are essentially switches in an on or off position, in quantum computers, the information, which is also essentially binary, is represented by quantum mechanical states. These can typically be the spin of an electron, energy levels of atoms, or the current direction, charge, or magnetic flux in a superconductor. Regardless of the choice of physical implementation, such a quantum mechanical two-state system is called a qubit.

[0019] A quantum annealer, such as a D-Wave machine, can be used as a quantum computer.

[0020] At the beginning of the calculation, the coupling of the quantum annealer qubits is initialized with the initial state and changed to the lowest energy ground state.

[0021] Qubits are a quantum mechanical system with two states (quantum superposition), whereby a qubit can be in a superposition of these two states and can also be entangled with other qubits. Thus, changing the coupling of the qubits simultaneously affects the state of all other entangled qubits.

[0022] Entangled qubits thus behave over any distance as if they were connected to each other. For example, a measurement of the spin of one electron determines the spin state of the other. Thus, if one qubit is changed, the entangled qubit adopts a corresponding change without any time delay and regardless of the distance.

[0023] According to the invention, it was recognized that it has not been possible to date to provide a generally applicable method for optimizing a transport network with regard to loading and unloading costs and transport costs. This applies in particular to a large number of goods and / or loading and unloading locations or vehicles. According to the invention, a digital computer and an adiabatic quantum system (quantum annealer) are provided for this purpose, wherein the quantum annealer is associated with a corresponding energy landscape, and wherein the quantum annealer is coupled to the digital computer.

[0024] Then, an energy function H is calculated based on the loading and unloading costs for all goods k ∈ {1, ..., g} and for all vehicles v ∈ {1, ..., N V} and for all locations (L∈L) with respect to a discretized point in time and the travel costs from a first location L (L∈L) to a second place for all places L′∈L and for all vehicles v ∈ {1, ..., N V} is generated.

[0025] The energy function H for use on a quantum annealer must be generated using the binary variables: bv,t,L,k,n={1,if vehicle v is currently at location L, n pieces of the goods k are loaded / unloaded0,otherwise

[0026] Where n max (k,L) is the maximum transported quantity of a good k ∈ {1, ..., g} to a location L and n min (k, L) is the maximum transported quantity of a good k ∈ {1, ..., g} from a location L and n ∈ (-n min (k, L), ..,n max (k, L)}.

[0027] Using the energy function H, an initial state and thus the energy landscape is initialized. By changing the energy landscape to the base state, after encoding by the digital computer, a transport network optimized with regard to loading and unloading costs and transport costs is specified.

[0028] Furthermore, the method according to the invention is not subject to any restrictions by the number of possible loading and unloading locations, the number of goods, or the number of vehicles. Thus, a large, extensive transport network can be optimized, something that would not be achievable using a conventional computer method (even with a large number of loads, the computation time would be long).

[0029] In further development, the constraint is added as a binary penalty term to the energy function H by the digital computer to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that the good k ∈ {1, ..., g} reaches the unloading location L' with a correct total quantity is given by: ∑v=1NV∑t=1NT∑n=−nmin(k,L′)nmax(k,L′)nbv,t,L′,k,n=∑L∈Ln(k,L,L′)∀k∈{1,…,g},L′∈L where b v,t,L,k,n are binary variables and n describes the number of pieces of a loaded or unloaded good and n(k, L, L') ∈ ℕ 0 is the number of pieces of a respective good k ∈ {1, ..., g} to be transported from a first location L to a second location L', where n max (k, L) is the maximum transported quantity of a good k to a location L and n min (k, L) is the maximum quantity of a good k transported from a location L.

[0030] This means that the good k arrives at the unloading location L' in the correct total quantity.

[0031] In further development, the constraint is added as a binary penalty term to the energy function H by the digital computer to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that the good k is transported from location L in the correct total quantity is given by: −∑v=1NV∑t=1NT∑n=−nmin(k,L)nmax(k,L)nbv,t,L,k,n=∑L′∈Ln(k,L,L′)∀k∈{1,…,g},L∈L where L∈L different locations, with k ∈ {1, ..., g} as different goods to be transported with b v,t,L,k,n as binary variables and n as the number of pieces of a good to be loaded or unloaded and where n(k, L, L') ∈ ℕ 0 is the number of pieces of a respective good k ∈ {1, ..., g} to be transported from a first location L to a second location L', where n max(k, L) is the maximum transported quantity of a good k to a location L and n min (k,L) is the maximum transported quantity of a good k from a location L, and where v ∈ {1, ...,N V} the different vehicles are.

[0032] This ensures that all corresponding goods k are transported away from a location L in the correct total quantity.

[0033] In further development, the constraint is added as a binary penalty term to the energy function H by the digital computer to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that each vehicle v at a time t ∈ {1, ..., N T} may only be in one place, given by: ∑L∈L∑k=1g∑n=−nmin(k,L)nmax(k,L)b,v,t,L,k,n=1 ∀t∈{1,…,NT},v∈{1,…,NV} where L∈L different locations, with k ∈ {1, ..., g} as different goods to be transported with b v,t,L,k,nas binary variables and n as the number of pieces of a good to be loaded or unloaded and where n(k, L, L') ∈ ℕ 0 is the number of pieces of a respective good k ∈ {1, ..., g} to be transported from a first location L to a second location L', where n max (k, L) is the maximum transported quantity of a good k to a location L and n min (k,L) is the maximum transported quantity of a good k from a location L, and where v ∈ {1, ...,N V} the different vehicles are.

[0034] This ensures that each vehicle v may only be in one location at a time t.

[0035] In further development, the constraint is added as a binary penalty term to the energy function H by the digital computer to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that the good k ∈ {1, ..., g} may only be loaded or unloaded in a quantity n at a time t, into a vehicle v, and at a location L, is given by: ∑n=−nmin(k,L)nmax(k,L)bv,t,L,k,n=1 ∀t∈{1,…,NT},v∈{1,…,NV},k∈{1,…,g},L∈L where L∈L different locations, with k ∈ {1, ..., g} as different goods to be transported with b v,t,L,k,n as binary variables and n as the number of pieces of a good to be loaded or unloaded and where n(k, L, L') ∈ ℕ 0 the number of pieces of a particular good k ∈ {1, ..., g} to be transported from a first location L (L∈L) to a second place L' (L′∈L) is, where n max(k,L) is the maximum transported quantity of a good k to a location L and n min (k, L) is the maximum transported quantity of a good k from a location L, and where v ∈ {1, ...,N V} the different vehicles are.

[0036] This ensures that the good k may only be loaded or unloaded in a quantity n at a time t, into a vehicle v, and at a location L.

[0037] Furthermore, the task is solved by a hybrid system for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process of the goods with respect to a vehicle as a means of transport, wherein the hybrid system comprises a digital computer for providing various goods to be transported to different locations, with a quantity of each good to be transported from a first location to a second location, and further for providing various vehicles, wherein the vehicles are provided in digital form, which depict reality, and wherein the digital computer is further configured to determine travel costs from a first location to a second location with respect to a vehicle, wherein the digital computer is configured to determine loading costs and unloading costs of a vehicle to be loaded or unloaded with respect to a predetermined quantity of a good at the location in or from the vehicle,and wherein the digital computer is designed to generate an energy function based on the loading costs and unloading costs for all goods and for all vehicles and for all locations with respect to a discretized point in time as well as the travel costs from a first location to a second location for all locations and for all vehicles using the following binary variables: bv,t,L,k,n={1,if vehicle v is currently loading / unloading n pieces of goods k at location L,0,otherwise wherein the hybrid system further comprises an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer, and wherein the digital computer is configured to generate an initial initial state for the adiabatic quantum system based on the energy function and is further designed to initialize the energy landscape based on the initial state, and wherein the adiabatic quantum system is designed to achieve a variation of the initial state into an energetic ground state, wherein the energetic ground state encodes an energy-minimized energy function which encodes a logistics network of logistical transport routes optimized with regard to loading and unloading costs as well as travel costs and the number of items of a respective good, taking into account the following constraints, with: ◯ the goods reach the unloading point with the correct total quantity, ◯ the goods are transported from one location in the correct total quantity, ◯ each vehicle may only be in one place at a time, ◯ the goods may only be loaded or unloaded in a certain number at a time, in a vehicle, and at a place, and wherein the digital computer is designed to measure the basic energetic state after adiabatic variation and encode the measurement of the basic energetic state, and to output the logistics network optimized with regard to loading costs and unloading costs as well as the travel costs and the number of items of a respective good, with logistically optimized transport routes, based on the encoding and using the optimized logistics network in the digital computer to improve the logistics planning with regard to the transport of goods.

[0038] The method according to the invention can be implemented particularly on the hybrid system. In particular, the advantages of the method and its configurations can be transferred to the hybrid system.

[0039] Furthermore, the object is achieved by a planning system which has a hybrid system as described above and / or a method as described above for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process of the goods with respect to a vehicle as a means of transport, taking into account at least the loading costs and unloading costs for all goods and for all vehicles and for all locations with respect to a discretized point in time as well as the travel costs from a first location to a second location for all locations and for all vehicles and wherein the planning system has a display unit for displaying the optimized logistics network on the display unit.

[0040] Such a planning system can be implemented, for example, on a digital computer. This planning system can generate an optimized logistics network with optimized logistical transport routes for each vehicle and optimized loading and unloading quantities of a good k, regardless of the multitude of variables, such as a large number of loading and unloading locations, vehicles, or goods.

[0041] Furthermore, the planning system can have an interface for the automated transmission of the optimized logistical transport routes to all vehicles.

[0042] Further features and advantages of the present invention will become apparent from the following description with reference to the accompanying figures, which schematically show: Fig. 1: a method according to the invention, Fig. 2: an inventive planning system.

[0043] Fig. 1 shows a method according to the invention for determining an optimized logistics network with logistically optimized transport routes of goods with a loading process and an unloading process of the goods with respect to a vehicle as a means of transport.

[0044] In a first step S1, the various goods k ∈ {1, ...,g} to be transported are digitally provided in a digital computer 2, which are to be sent to different locations L∈L are to be transported, with a number of pieces to be transported n(k, L, L') ∈ ℕ 0 of a respective good k ∈ {1, ..., g}, which are to be transported from a first location L to a second location L'. There are g-different goods. The goods k can, for example, be stored digitally as a real image in a storage unit. Adding or changing a good, a location, or a quantity is thus easily possible.

[0045] Furthermore, different vehicles v ∈ {1, ...,N V}, whereby the vehicles are provided in digital form, which reflects reality; i.e. the transporters actually available in reality, for example, of a transport company. There are N V various vehicles.

[0046] Then the travel costs d(L, L') from a first location L∈L to a second place L′∈L with respect to a vehicle v ∈ {1, ..., N V}. Current fuel prices, for example, can be taken into account.

[0047] Furthermore, in a second step S2, the digital computer 2 is used to determine travel costs d(L, L') from a first location L∈L to a second place L′∈L with respect to a vehicle v ∈ {1, ...,N V} This can include, for example, current fuel prices, the route's length and geography, wear and tear on the means of transport, etc.

[0048] In a third step S3, the digital computer 2 calculates the loading and unloading costs C(v, k, L, n) of a vehicle v ∈ {1, ..., N V} with respect to a given n ∈ ℤ Number of items of a good k at location L in or from the vehicle v ∈ {1, ...,N V} is determined by the digital computer 2. Each loading and unloading operation incurs costs, including, for example, time delays and labor costs. Additional costs may include, for example, the provision of equipment for unloading, etc.

[0049] The loading and unloading costs C(v, k, L, n) indicate the costs to n ∈ ℤ Number of items of good k to be loaded or unloaded into (or from) vehicle v at location L. Loading or unloading determines whether n is positive or negative.

[0050] A transport of a maximum number n of a good k from a location L' to a location L can be specified as: nmax(k,L):=∑L′∈Ln(k,L′,L)

[0051] This is the maximum quantity that can be unloaded at the location L (outward transport), ie n max (k,L) is thus the maximum quantity of a good k transported to a location L.

[0052] A transport of a maximum number n of a good k from a location L to the location L' can be specified as: nmin(k,L):=∑L′∈Ln(k,L,L′)

[0053] This is the maximum quantity (number of pieces) that can be loaded at the location L (transportation), i.e. n min (k, L) is thus the maximum quantity of a good k transported from the location L.

[0054] In a fourth step S4, an adiabatic quantum system is provided, in particular a quantum annealer 3, wherein the quantum annealer 3 is associated with a corresponding energy landscape. The quantum annealer 3 is coupled to the digital computer 2.

[0055] In a fifth step S5, an energy function H is generated based on the loading and unloading costs for all goods k ∈ {1, ..., g} and for all vehicles v ∈ {1, ..., N V} and for all locations L∈L with respect to a discretized time t ∈ {1, ..., N T}, as well as travel costs from a first location L∈L to a second place L′∈L for all locations and for all vehicles v ∈ {1, ..., N V} using the following binary variables: bv,t,L,k,n={1,if vehicle v is currently loading / unloading n pieces of goods k at location L,0,otherwise where L∈L are the different places for loading and unloading, with k ∈ {1, ...,g} as different goods to be transported, where n max (k, L) is the maximum transported quantity of a good k to a location L and n min (k,L) is the maximum transported quantity of a good k from a location L, and v ∈ {1, ..., N V} are the different vehicles and t ∈ {1, ...,N T} the discretized time.

[0056] A number of pieces n ∈ {-n min (k, L), ...,n max (k, L)} is allowed.

[0057] For example, n min (k,L) = 10 and n max(k,L)=100, so that n ∈ {-10, ...,100}. The index n describes the number of items being loaded or unloaded, so n = 8 describes that 8 items of the good k are unloaded; thus, 8 more items of the good k ∈ {1, ..., g} are now present at location L, and n = -8 describes that 8 items of the good k are loaded; thus, 8 fewer items are present at location L.

[0058] The energy function H is given by: H=∑L,L′∈L∑v=1NV∑t=1NTd(L,L′)δ(v,t,L)δ(v,t+1,L′) +∑L∈L∑v=1NV∑t=1NT∑k=1g∑n=−nmin(k,L)nmax(k,L)C(v,k,L,n)bv,t,L,k,n with v ∈ {1, ...,N V} as vehicles, L∈L different locations, k ∈ {1, ...,g} g-different goods, d(L, L') the travel costs from a first location L to a second location L', and C (v, k, L, n) the loading and unloading costs to load or unload n ∈ ℤ numbers of the good k at location L into (or from) vehicle v, where n max(k, L) is the maximum transported quantity of a good k to a location L and n min (k,L) is the maximum transported quantity of a good k from a location L, and where t ∈ {1, ...,N T} is the discretized time, and where δ(v,t,L):={1,if vehicle v at time t at location L,loads / unloads a good 0,otherwise

[0059] The condition that a vehicle v loads or unloads a good at time t at location L can be given by: ∑k=1g∑n=−nmin(k,L)nmax(k,L)bv,t,L,n≥1 with b u,t,L,n equal to the binary variables.

[0060] Thus, the first term of the energy function H takes into account the travel costs and the second term takes into account the loading and unloading costs, for all vehicles, all times, all goods and all quantities.

[0061] Furthermore, the constraints are added as binary penalty terms to the energy function H to generate a QUBO (Quadratic Unconstrained Binary Optimization) model.

[0062] The constraint that the good k reaches a discharge location L' with a correct total quantity is given by: ∑v=1NV∑t=1NT∑n=−nmin(k,L′)nmax(k,L′)nbv,t,L′,k,n=∑L∈Ln(k,L,L′) ∀k∈{1,…,g},L′∈L

[0063] The constraint that the good k is transported from location L in the correct total quantity is given by: −∑v=1NV∑t=1NT∑n=−nmin(k,L)nmax(k,L)nbv,t,L,k,n=∑L′∈Ln(k,L,L′) ∀k∈{1,…,g},L∈L

[0064] The constraint is that each vehicle v at a time t ∈ {1, ...,N T} may only be at one location L, given by: ∑L∈L∑k=1g∑n=−nmin(k,L)nmax(k,L)bv,t,L,k,n=1 ∀t∈{1,…,NT},v∈{1,…,NV}

[0065] The constraint that the good k may only be loaded or unloaded in a quantity n at a time t, into a vehicle v, and at a location L, is given by: ∑n=−nmin(k,L)nmax(k,L)bv,t,L,k,n=1 ∀t∈{1,…,NT},v∈{1,…,g},L∈L

[0066] Such an energy function H together with the constraints can be converted into binary variables, which can be represented as a maximal quadratic problem and thus can be solved by the quantum annealer 3, whereby the energy-minimized energy function H encodes a logistics network with logistically optimized transport routes that is optimized with regard to the loading and unloading costs as well as the travel costs and the number of items of a respective good.

[0067] Using the energy function H, an initial state and thus the energy landscape is initialized.

[0068] In a sixth step S6, the energy landscape of quantum annealer 3 is initialized using an initial state. The initial state of quantum annealer 3 typically corresponds to a solvable quantum system in which the qubits are initialized in the ground state.

[0069] In the adiabatic quantum computer, the energy function H is translated into a coupling strength between the qubits.

[0070] In a seventh step S7, the initial state is changed to an energetic base state using the energy function H. By changing the energy landscape to the base state, a logistics network with logistically optimized transport routes is specified, after encoding by the digital computer 2, which is optimized with regard to loading and unloading costs, as well as travel costs and the number of items of a particular good.

[0071] In an eighth step S8, the achieved energetic baseline after adiabatic change is measured by the digital computer 2, and the measurement is encoded. Based on the encoding, an optimized logistics network can now be displayed on the digital computer 2 and used to improve the logistics of, for example, a transport company / company.

[0072] Fig. Figure 2 shows a planning system 4 with a hybrid system 1 according to the invention. The hybrid system 1 comprises the digital computer 2 and the quantum annealer 3, which is associated with a corresponding energy landscape. The quantum annealer 3 is coupled to the digital computer 2.

[0073] Furthermore, the digital computer 2 is configured as energetically set qubits for generating the energy function H for input to the quantum annealer 3. The constraints are added to the energy function H as penalty terms, so that a maximally quadratic structure is maintained.

[0074] Furthermore, after setting the qubits in the quantum annealer 3 to an initial state, the quantum annealer 3 varies the initial state into an energetic ground state, where the energetic ground state encodes an energy-minimized energy function H, where the energy-minimized energy function encodes a logistics network with logistically optimized transport routes, optimized with regard to loading and unloading costs, as well as travel costs and the number of items of a given good. Subsequently, the digital computer 2 measures the energetic ground state after adiabatic variation and encodes the measurement.

[0075] This allows for logistically optimized transport routes and quantities of all goods. The planning system is universally applicable, even for large fleets, a large number of goods, and multiple loading and unloading stations. List of reference symbols 1 hybrid system 2 digital computers 3 quantum annealers 4 Planning system

Claims

[1] Method for determining an optimized logistics network with logistically optimized transport routes of goods (k ∈ {1,.., g}) with a loading process and an unloading process of the goods (k ∈ {1, . ., g}) with respect to a vehicle (v ∈ {1, ..., N V}) as a means of transport, comprising the steps: - Providing a digital computer (2), - Providing different goods to be transported (k ∈ {1,.., g}), which are to be delivered to different locations (L∈L) are to be transported, with a number of pieces to be transported (n(k, L, L') ∈ ℕ 0 ) of a respective good (k ∈ {1, . ., g}) from a first place (L∈L) to a second place (L′∈L), - Provision of different vehicles (v ∈ {1, ...,N V}), whereby the vehicles are provided in digital form, which depicts reality - Determine travel costs d(L, L') from a first location (L∈L) to a second place (L′∈L) with respect to a vehicle (v ∈ {1, ...,N V}), - Determination of loading and unloading costs of a vehicle to be loaded or unloaded (v ∈ {1, ...,N V}) with respect to a given number (n ∈ ℤ) of a good (k ∈ {1,.., g}) at the location (L∈L) in or from the vehicle (v ∈ {1, ...,N V}), - Generate an energy function (H) based on the loading and unloading costs for all goods (k ∈ {1, . ., g}) and for all vehicles (v ∈ {1, ..., N V}) and for all locations (L∈L) with respect to a discretized time (t ∈ {1, ..., N T}) and travel costs from a first location (L∈L) to a second place (L'∈L) for all locations and for all vehicles (v ∈ {1, ..., N V}) using the following binary variables: bv,t,L,k,n={1, if vehicle v at time t at location L, n pieces of goods k loaded / unloaded 0 otherwise - providing an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer (2), - Generating an initial state for the adiabatic quantum system based on the energy function (H) and initializing the energy landscape based on the initial state and changing the initial state using the energy function (H) in the quantum computer into an energetic ground state, whereby the energetic ground state corresponds to an energy-minimized energy function (H) that encodes a logistics network with logistically optimized transport routes optimized with regard to loading and unloading costs as well as travel costs and the number of items of a respective good, taking into account the following constraints: ◯ the good (k ∈ {1,.., g}) reaches a discharge location L' with a correct total quantity, ◯ the good (k ∈ {1,.., g}) is transported from the location (L∈L) transported in the correct total quantity, ◯ each vehicle (v ∈ {1, ..., N V}) may at a time (t ∈ {1, ..., N T}) only be in one place, ◯ the good (k ∈ {1,..,g}) may only be produced in a quantity n at a time (t ∈ {1, ..., N T}) , into a vehicle (v ∈ {1, ..., N V}), and in one place (L∈L) be loaded or unloaded, - Measuring the basic energetic state by the digital computer (2) after adiabatic change and encoding the measurement by the digital computer (2) and determining and outputting based on the encoding of the logistics network optimized with regard to loading and unloading costs as well as the travel costs and the number of pieces of a respective good (k ∈ {1,.., g}) with logistically optimized transport routes and using the optimized logistics network to improve the logistics planning with regard to the transport of goods (k ∈ {1,.., g}). [2] Method according to claim 1, characterized bythat the constraint is added as a binary penalty term to the energy function (H) by the digital computer (2) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that the good (k ∈ {1,..,g}) has a discharge location (L'∈L) with a correct total amount achieved by: ∑v=1NV∑t=1NT∑n=−nmin(k,L')nmax(k,L')nbv,t,L',k,n=∑L∈Ln(k,L,L')∀k∈{1,…,g},L'∈L where (b v,t,L,k,n ) are the binary variables and (n) is the number of pieces of a loaded or unloaded good (k ∈ {1,.., g}) and (n(k, L, L') ∈ ℕ 0 ) the number of pieces of a particular good (k ∈ {1, . ., g}) to be transported from a first location (L∈L) to a second place (L'∈L) is, where (n max (k, L)) the maximum transported quantity of a good (k ∈ {1,.., g}) to a location (L∈L) is and (n min(k, L)) the maximum transported quantity of a good (k ∈ {1,.., g}) from a location (L∈L) is. [3] Method according to claim 1 or 2, characterized by that the constraint is added as a binary penalty term to the energy function (H) by the digital computer (2) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that the good (k ∈ {1,..,g}) from the location (L∈L) is transported away in the correct total amount, given by: −∑v=1NV∑t=1NT∑n=−nmin(k,L)nmax(k,L)nbv,t,L,k,n=∑L∈Ln(k,L,L')∀k∈{1,…,g},L∈L where (L∈L) different locations, with (k ∈ {1,.., g}) as different goods to be transported with (b v,t,L,k,n ) as binary variables and (n) as the number of pieces of a good to be loaded or unloaded (k ∈ {1,..,g}) and where (n(k, L, L') ∈ ℕ 0) the number of pieces of a particular good (k ∈ {1, .., g}) to be transported from a first location (L∈L) to a second place (L'∈L) is, where (n max (k, L)) the maximum transported quantity of a good (k ∈ {1,.., g}) to a location (L∈L) is and (n min (k, L)) the maximum transported quantity of a good (k E {1, .., g}) from a location (L∈L) is, and where (v ∈ {1, ...,N V}) the different vehicles (v ∈ {1, ...,N V}) are. [4] Method according to one of the preceding claims, characterized by that the constraint is added as a binary penalty term to the energy function (H) by the digital computer (2) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that each vehicle (v ∈ {1, ...,N V}) at a time (t ∈ {1, ..., N T}) may only be in one place, is given by: ∑L∈L∑k=1g∑n=−nmin(k,L)nmax(k,L)bv,t,L,k,n=1 ∀t∈{1,…,NT},v∈{1,…,NV} where (L∈L) different locations, with (k ∈ {1,.., g}) as different goods to be transported with (b v,t,L,k,n ) as binary variables and (n) as the number of pieces of a good to be loaded or unloaded and where (n(k, L, L') ∈ ℕ 0 ) the number of pieces of a particular good (k ∈ {1, . ., g}) to be transported from a first location (L∈L) to a second place (L'∈L) is, where (n max (k, L)) the maximum transported quantity of a good (k E {1, . ., g}) to a location (L∈L) is and n min (k, L) the maximum transported quantity of a good (k ∈ {1,.., g}) from a location (L∈L) is, and where (v ∈ {1, ..., N V}) the different vehicles are. [5] Method according to one of the preceding claims, characterized by that the constraint is added as a binary penalty term to the energy function (H) by the digital computer (2) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model, where the constraint that the good (k ∈ {1,.., g}) is only available in one quantity (n) at a time (t ∈ {1, ..., N T}), into a vehicle (v ∈ {1, ...,N V}) , and in one place (L∈L) may be loaded or unloaded, is given by: ∑n=−nmin(k,L)nmax(k,L)bv,t,L,k,n=1 ∀t∈{1,…,NT},v∈{1,…,NV},k∈{1,…,g},L∈L where (L∈L) different places, with(k ∈ {1,.., g}) as different goods to be transported with (b v,t,L,k,n ) as binary variables and (n) as the number of pieces of a good to be loaded or unloaded (k ∈ {1,.., g}) and where (n max(k, L)) the maximum transported quantity of a good (k ∈ {1, .., g}) to a location (L∈L) is and (n min (k, L)) the maximum transported quantity of a good (k ∈ {1,.., g}) from a location (L∈L) is, and where (v ∈ {1, ...,N V}) the different vehicles are. [6] Hybrid system (1) for determining an optimized logistics network with logistically optimized transport routes of goods (k ∈ {1,.., g}) with a loading process and an unloading process of the goods (k ∈ {1,.., g}) with respect to a vehicle (v ∈ {1, ...,N V}) as a means of transport characterized by , that the hybrid system (1) comprises a digital computer (2) for providing different goods to be transported (k ∈ {1, .., g}), which are to be sent to different locations (L∈L) are to be transported, with a number of pieces to be transported n(k, L, L') ∈ ℕ 0of a respective good (k ∈ {1,.., g}) from a first location (L∈L) to a second place (L'∈L), and also for providing various vehicles (v ∈ {1, ...,N V}) , where the vehicles (v ∈ {1, ...,N V}) are provided in digital form, which depict reality, and wherein the digital computer (2) is used to determine travel costs (d(L,L')) from a first location (L∈L) to a second place (L'∈L) with respect to a vehicle (v ∈ {1, ...,N V}), wherein the digital computer (2) is designed to determine loading costs and unloading costs of a vehicle to be loaded or unloaded (v ∈ {1, ..., N V}) with respect to a given number (n ∈ Z) of a good (k ∈ {1,.., g}) at the location (L∈L) in or from the vehicle (v ∈ {1, ...,N V}), and wherein the digital computer (2) is designed to generate an energy function (H) on the basis of the loading costs and unloading costs for all goods (k ∈ {1, . ., g}) and for all vehicles (v ∈ {1, ..., N V}) and for all locations (L∈L) with respect to a discretized time (t ∈ {1, ..., N T}) and travel costs from a first location (L∈L) to a second place (L'∈L) for all locations and for all vehicles (v ∈ {1, ..., N y}) using the following binary variables: bv,t,L,k,n={1, if vehicle v at time t at location L,n is loading / unloading n pieces of goods k, otherwise wherein the hybrid system (1) further comprises an adiabatic quantum system, wherein the adiabatic quantum system is associated with a corresponding energy landscape, wherein the adiabatic quantum system is coupled to the digital computer (2), and wherein the digital computer (2) is designed to generate an initial initial state for the adiabatic quantum system based on the energy function (H) and is further designed to initialize the energy landscape based on the initial state, and wherein the adiabatic quantum system is designed to effect a variation of the initial state into an energetic ground state, wherein the energetic ground state encodes an energy-minimized energy function (H), which encodes a logistics network of logistical transport routes optimized with regard to loading and unloading costs as well as travel costs and the number of items of a respective good (k ∈ {1, .., g}), taking into account the following constraints, with: ◯ the goods (k ∈ {1,..,g}) reach the unloading point (L∈L) with a correct total amount, ◯ the good (k ∈ {1, . ., g}) is transported from the place (L∈L) transported in the correct total quantity, ◯ each vehicle (v ∈ {1, ..., N V}) may at a time (t ∈ {1, ..., N T}) only be in one place, ◯ the good (k ∈ {1, .., g}) may only be produced in a quantity (n) at a time (t ∈ {1, ..., N T}) into a vehicle (v ∈ {1, ..., N V}) and in one place (L∈L) be loaded or unloaded, and wherein the digital computer (2) is designed to measure the energetic basic state after adiabatic variation and encode the measurement of the energetic basic state, and to output the logistics network optimized with regard to loading costs and unloading costs as well as the travel costs and the number of pieces of a respective good (k ∈ {1,.., g}) with logistically optimized transport routes on the basis of the encoding and use of the logistical transport routes by the digital computer (2). [7] Hybrid system (1) according to claim 6, characterized bythat a display unit is provided to display the logistical transport routes. [8] Hybrid system (1) according to claim 6 or 7, characterized by that the constraints are added as binary penalty terms to the energy function (H) by the digital computer (2) to generate a QUBO (Quadratic Unconstrained Binary Optimization) model. [9] Planning system (4) which comprises a hybrid system (1) according to one of the preceding claims 6 to 8 and / or a method according to one of the preceding claims 1 to 5 for determining an optimized logistics network with logistically optimized transport routes of goods (k ∈ {1,..,g}) with a loading process and an unloading process of the goods (k ∈ {1, ..,g}) with respect to a vehicle (v ∈ {1, ...,N V}) as a means of transport, taking into account at least the loading and unloading costs for all goods (k ∈ {1, .., g}) and for all vehicles (v ∈ {1, ..., N V}) and for all locations (L∈L) with respect to a discretized time (t ∈ {1, ...,N T}) and travel costs from a first location (L∈L) to a second place (L'∈L) for all locations and for all vehicles (v ∈ {1, ..., N V}) and wherein the planning system (4) has a display unit for displaying the optimized logistics network on the display unit. [10] Planning system (4) according to claim 9, characterized by that the planning system (4) has an interface for the automated transmission of the optimized logistics network as well as the optimized logistics transport routes to all vehicles (v ∈ {1, ..., N y}).

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