Dispatching method and related equipment for cold chain logistics vehicles

Abstract

The embodiments of the present disclosure provide a method and device for scheduling cold chain logistics vehicles, a computer-readable storage medium, and an electronic device, and belong to the field of computer and communication technology. The method includes: obtaining the total amount of goods to be distributed, the vehicle loading rate, and the maximum loading capacity of the vehicle; obtaining a set of possible vehicle quantities according to the total amount of goods to be distributed, the vehicle loading rate, and the maximum loading capacity of the vehicle; for each possible number of vehicles in the set of possible vehicle quantities, determining the sub-optimal distribution route that minimizes the total distribution timeout under capacity constraints, temperature layer constraints, and time constraints; according to the sub-optimal distribution route and the shortest total distribution timeout corresponding to each possible number of vehicles in the set of possible vehicle quantities, using the elbow method to determine the number of vehicles used for scheduling and the global optimal distribution route. The method of the present disclosure can realize the scheduling of cold chain logistics vehicles.

Description

Technical Field

[0001] The present disclosure relates to the field of computer and communication technology, and in particular, to a scheduling method and device for cold chain logistics vehicles, a computer-readable storage medium, and an electronic device. Background Art

[0002] Cold chain logistics refers to a systematic project that ensures food quality and reduces food loss by keeping the goods in the optimal low temperature environment specified by the product at all stages of the production, warehousing, transportation and sales process until consumption. The optimization of truck cargo collection and distribution scheduling in cold chain logistics has huge market value and research value. Problems such as vehicle-cargo matching and scheduling in cold chain scenarios have certain particularities. For example, the scheduling of cold chains requires a special scheduling center, and the transportation of cold chains also requires special vehicles. At the same time, since cold chain products need to prevent thawing, the requirements for timeliness are relatively high. These particularities determine that scheduling and other issues in cold chain scenarios cannot be directly solved using scheduling solutions in general scenarios.

[0003] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0004] The embodiments of the present disclosure provide a method and apparatus for scheduling cold chain logistics vehicles, a computer-readable storage medium, and an electronic device, which can achieve efficient scheduling of cold chain logistics vehicles.

[0005] Other features and advantages of the present disclosure will become apparent from the following detailed description, or may be learned in part by the practice of the present disclosure.

[0006] According to one aspect of the present disclosure, a method for dispatching cold chain logistics vehicles is provided, comprising:

[0007] The total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle are obtained; a set of possible vehicle numbers is obtained according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; for each possible number of vehicles in the set of possible vehicles, a sub-optimal delivery route with the shortest total delivery timeout is determined under capacity constraints, temperature layer constraints and time constraints; based on the sub-optimal delivery route corresponding to each possible number of vehicles in the set of possible vehicles and the shortest total delivery timeout, the elbow method is used to determine the number of vehicles used for scheduling and the global optimal delivery route.

[0008] In one embodiment, obtaining a possible set of vehicle quantities based on the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle includes: dividing the total amount of goods to be delivered by the product of the first vehicle loading rate and the maximum loading capacity of the vehicle, and rounding up to obtain an upper bound of the possible set of vehicle quantities; dividing the total amount of goods to be delivered by the product of the second vehicle loading rate and the maximum loading capacity of the vehicle, and rounding up to obtain a lower bound of the possible set of vehicle quantities; forming the possible set of vehicle quantities by the number of vehicles between the upper bound of the possible set of vehicle quantities and the lower bound of the possible set of vehicle quantities; wherein the first vehicle loading rate is the vehicle loading rate minus a first ratio, the second vehicle loading rate is the vehicle loading rate plus a second ratio, and the first ratio and the second ratio are both greater than zero and less than 1.

[0009] In one embodiment, the elbow method is used to determine the number of vehicles used for scheduling and the global optimal delivery route based on the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible number of vehicles, including: dividing the set of possible number of vehicles into a first set and a second set; performing linear regression based on each possible number of vehicles and the corresponding shortest total delivery timeout in the first set and the second set to obtain a first prediction model and a second prediction model; and determining the number of vehicles used for scheduling and the global optimal delivery route based on the first prediction model and the second prediction model.

[0010] In one embodiment, determining the number of vehicles used for scheduling and the global optimal delivery route based on the first prediction model and the second prediction model includes: obtaining the minimum root mean square difference of the first set based on the first prediction model; obtaining the minimum root mean square difference of the second set based on the second prediction model; using the possible number of vehicles used for scheduling and the corresponding sub-optimal delivery route that divides the first set and the second set into a combination in which the sum of the minimum root mean square difference of the first set and the minimum root mean square difference of the second set is the smallest among the combinations of the first set and the second set of the possible number of vehicles used.

[0011] In one embodiment, obtaining the minimum root mean square difference of the first set according to the first prediction model includes: obtaining the minimum root mean square difference of the first set according to the shortest total delivery timeout corresponding to each possible number of vehicles in the first set, the predicted value of the first prediction model and the number of elements of the possible number of vehicles in the first set.

[0012] In one embodiment, obtaining the minimum root mean square difference of the second set according to the second prediction model includes: obtaining the minimum root mean square difference of the second set according to the shortest total delivery timeout corresponding to each possible number of vehicles in the second set, the predicted value of the second prediction model and the number of elements of the possible number of vehicles in the second set.

[0013] In one embodiment, for each possible number of vehicles in the set of possible numbers of vehicles, a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints is determined, including: classifying the goods to be delivered according to the required vehicle temperature layer requirements; allocating each type of classified goods to be delivered to vehicles in the corresponding temperature layer according to the vehicle loading rate and the maximum loading capacity of the vehicle; and obtaining the sub-optimal delivery route that minimizes the total delivery timeout according to the possible delivery routes and the shipping time and delivery time required by the customer.

[0014] In one embodiment, according to possible delivery routes and the shipping time and delivery time required by the customer, a sub-optimal delivery route with the shortest total delivery timeout is obtained, including: when the shortest total delivery timeout with the possible number of vehicles being used is equal to zero, selecting the delivery route with the shortest distance as the sub-optimal delivery route.

[0015] In one embodiment, the vehicle has N temperature layers, where N is an integer greater than or equal to 2.

[0016] In one embodiment, obtaining the total amount of goods to be delivered, the vehicle loading rate and the maximum vehicle loading capacity includes: when the vehicle models are inconsistent, obtaining the total amount of goods to be delivered, the vehicle loading rate and the weighted maximum vehicle loading capacity.

[0017] According to one aspect of the present disclosure, a scheduling device for cold chain logistics vehicles is provided, including: a first acquisition module, configured to obtain the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; a second acquisition module, configured to obtain a set of possible vehicle numbers according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; a first determination module, for each possible number of vehicles in the set of possible vehicles, determining a sub-optimal delivery route with the shortest total delivery timeout under capacity constraints, temperature layer constraints and time constraints; a second determination module, configured to determine the number of vehicles used for scheduling and the global optimal delivery route using the elbow method according to the sub-optimal delivery route corresponding to each possible number of vehicles in the set of possible vehicles and the shortest total delivery timeout.

[0018] According to one aspect of the present disclosure, an electronic device is provided, comprising: one or more processors; and a storage device configured to store one or more programs, so that when the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of the above embodiments.

[0019] According to one aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method described in any one of the above embodiments is implemented.

[0020] The cold chain logistics vehicle scheduling method disclosed in the present invention obtains a set of possible vehicle quantities according to the vehicle loading rate, the maximum loading capacity of the vehicle and the total amount of goods to be delivered, and based on the set of possible vehicle quantities, obtains the number of vehicles used for scheduling and the global optimal delivery route according to capacity constraints, temperature layer constraints and time constraints.

[0021] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The following drawings describe certain illustrative embodiments of the present invention, wherein like reference numerals represent like elements. These described embodiments are intended to be exemplary embodiments of the present disclosure and are not intended to be limiting in any way.

[0023] Figure 1 A schematic diagram showing an exemplary system architecture to which the method for dispatching cold chain logistics vehicles according to an embodiment of the present disclosure can be applied;

[0024] Figure 2 A flowchart schematically shows a method for dispatching cold chain logistics vehicles according to an embodiment of the present disclosure;

[0025] Figure 3 A flowchart of using the elbow method to determine the number of vehicles used in scheduling and the global optimal delivery route according to one embodiment of the present disclosure is shown;

[0026] Figure 4 Another flowchart of using the elbow method to determine the number of vehicles used in scheduling and the global optimal delivery route according to one embodiment of the present disclosure is shown;

[0027] Figure 5 It shows that the regression on both sides of the cut-off point is performed separately, and the cut-off point with the smallest sum of the root mean square difference is selected;

[0028] Figure 6 A block diagram schematically shows a dispatching device for cold chain logistics vehicles according to an embodiment of the present disclosure;

[0029] Figure 7 A block diagram schematically shows a scheduling device for cold chain logistics vehicles according to another embodiment of the present invention;

[0030] Figure 8 A block diagram schematically shows a scheduling device for cold chain logistics vehicles according to another embodiment of the present invention; and

[0031] Fig. 9 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the embodiment of the present disclosure is shown. DETAILED DESCRIPTION

[0032] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in a variety of forms and should not be construed as limited to the examples set forth herein; rather, these embodiments are provided so that this disclosure will be more comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0033] In addition, the described features, structures or characteristics may be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present disclosure. However, those skilled in the art will appreciate that the technical solutions of the present disclosure may be practiced without one or more of the specific details, or other methods, components, devices, steps, etc. may be adopted. In other cases, known methods, devices, implementations or operations are not shown or described in detail to avoid blurring the various aspects of the present disclosure.

[0034] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.

[0035] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.

[0036] First, some terms of the present disclosure are explained below:

[0037] The total amount of goods to be delivered is the total mass or volume of goods to be delivered included in the total waybills to be dispatched within the scheduling period.

[0038] The vehicle loading rate is the ratio of the actual amount of cargo loaded on the vehicle to the theoretical maximum loading amount.

[0039] The maximum cargo capacity of a vehicle is the maximum total mass or volume of cargo that the vehicle can load.

[0040] Capacity constraint means that the actual amount of cargo transferred by the vehicle must be less than or equal to the maximum total mass or volume of cargo that the vehicle can load.

[0041] Temperature layer constraint means that the vehicle's temperature layer (refrigerated, frozen or normal temperature) must match the transportation requirements of the goods.

[0042] Time constraints refer to the shipping time and delivery time required by the customer in the order.

[0043] The total delivery timeout refers to the total timeout time that exceeds the delivery time required by the customer after all vehicles complete the delivery of the order.

[0044] The delivery route refers to the route that the vehicle travels during the delivery process.

[0045] The number of vehicles that may be used refers to the number of vehicles used in the determination of the dispatch and the number of vehicles that may be used for calculation in the global optimal delivery route. The set of the number of vehicles that may be used refers to the set consisting of all the numbers of vehicles that may be used.

[0046] Figure 1 A schematic diagram of an exemplary system architecture 100 is shown to which the method for dispatching cold chain logistics vehicles according to an embodiment of the present disclosure can be applied.

[0047] like Figure 1 As shown, the system architecture 100 may include one or more of terminals 101, 102, 103, a network 104, and a server 105. The network 104 is a medium for providing a communication link between the terminals 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or optical fiber cables, etc.

[0048] It should be understood that Figure 1 The number of terminals, networks and servers in the embodiment is only for illustration. Any number of terminals, networks and servers may be provided as required. For example, the server 105 may be a server cluster composed of multiple servers.

[0049] The staff can use the terminals 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. The terminals 101, 102, 103 can be various electronic devices with display screens, including but not limited to smart phones, tablet computers, portable computers, desktop computers, and the like.

[0050] The server 105 may be a server that provides various services. For example, when a staff member sends a dispatch instruction of a cold chain logistics vehicle to the server 105 through the terminal 103 (or the terminal 101 or 102), the server 105 may obtain the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; obtain a set of possible vehicle quantities according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; for each possible number of vehicles in the set of possible vehicle quantities, determine the sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints and time constraints; according to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible vehicle quantities, use the elbow method to determine the number of vehicles used for dispatch and the global optimal delivery route. The server 105 may display the number of vehicles used for dispatch and the global optimal delivery route on the terminal 103 or other terminals, so that the staff member or other personnel (e.g., user) can view the number of vehicles used for dispatch and the global optimal delivery route based on the content displayed on the terminal.

[0051] When dispatching logistics vehicles, it is necessary to consider both reducing the number of dispatched vehicles and reducing delivery delays. Although there are currently multi-objective optimization technologies that can solve similar problems in some scenarios, they are relatively complex to implement. At the same time, multi-objective optimization generally outputs multiple sets of solutions, but in actual use, only one can be selected from them, and the selection criteria are difficult to define. The present invention fixes the number of available vehicles for each dispatch, and solves a dispatch plan that can minimize the penalties associated with delivery delays. By continuously increasing the number of given dispatched vehicles, different "cost expenditure-optimal benefit" combinations are obtained in each sub-problem, and finally the "elbow method" is used to automatically screen out the optimal dispatch plan among all combinations.

[0052] Figure 2 The flowchart of the method for dispatching cold chain logistics vehicles according to an embodiment of the present disclosure is schematically shown. The method steps of the embodiment of the present disclosure can be executed by a terminal or a server, or by the terminal and the server interacting, or by a server cluster. For example, the method steps can be executed by the above-mentioned Figure 1 The server 105 in the embodiment is executed, but the present disclosure is not limited to this.

[0053] In step S210, the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle are obtained.

[0054] In this step, the terminal or server obtains the vehicle loading rate, the maximum loading capacity of the vehicle and the total amount of goods to be delivered.

[0055] In one embodiment, obtaining the total amount of goods to be delivered, the vehicle loading rate, and the maximum loading capacity of the vehicle includes:

[0056] When the vehicle models are inconsistent, the total amount of goods to be delivered, the vehicle loading rate and the weighted maximum loading capacity of the vehicle are obtained.

[0057] In one embodiment, in actual use, data must first be collected when dispatching vehicles, and the collected data includes but is not limited to: the number of the waybill to be dispatched, the destination of the waybill, the total volume of the goods on the waybill, the total weight of the goods on the waybill, the start delivery time required by the waybill, the latest delivery time required by the waybill, the available vehicle models, etc. By collecting the above information, information such as the total amount of goods to be delivered can be obtained.

[0058] Before actually starting the scheduling, the vehicle scheduling method of the present disclosure also needs to understand the average loading rate level of vehicles in historical scheduling based on business experience, understand the optimization space and set the vehicle loading rate r accordingly.

[0059] When the scheduling plan starts, the total number of waybills to be scheduled in the scheduling cycle will not change, so the total amount of goods (mass or volume) V to be delivered in the cycle is fixed. However, when there is more than one type of truck, that is, different trucks k have different maximum loading capacities v k When the number of vehicles is bounded by V l and the lower bound V of the number of vehicles u The calculation becomes complicated. The present invention calculates the maximum loading capacity of weighted vehicles according to the proportion of various types of vehicles. The "weight" here should be understood as the approximate proportion obtained from historical data. For example, assuming that the average daily dispatch times of two types of vehicles in the past three months were 39 and 11 respectively, and their maximum loading capacities were v1 and v2 respectively, then the weighted average of the two types of vehicles is 4:1, that is, the weighted vehicle maximum loading capacity

[0060] In step S220, a set of possible vehicle quantities is obtained based on the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle.

[0061] In this step, the terminal or the server obtains a set of possible vehicle quantities according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle.

[0062] In one embodiment, the total amount of goods to be delivered is divided by the product of the first vehicle loading rate and the maximum loading capacity of the vehicle, and rounded up to obtain an upper bound of the set of possible vehicle quantities;

[0063] Divide the total amount of goods to be delivered by the product of the second vehicle loading rate and the maximum loading capacity of the vehicle, and round up to obtain the lower bound of the set of possible vehicle quantities;

[0064] The number of available vehicles between the upper bound of the possible number of available vehicles set and the lower bound of the possible number of available vehicles set form the possible number of available vehicles set;

[0065] The first vehicle loading rate is the vehicle loading rate minus a first ratio, the second vehicle loading rate is the vehicle loading rate plus a second ratio, and both the first ratio and the second ratio are greater than zero and less than 1.

[0066] For example, obtaining a set of possible vehicle quantities according to the total amount of goods to be delivered, the vehicle loading rate, and the maximum loading capacity of the vehicle includes:

[0067] The upper limit of the number of vehicles is obtained according to the following formula ( This symbol means rounding x upwards):

[0068]

[0069] The lower bound of the number of vehicles is obtained according to the following formula:

[0070]

[0071] Among them, r is the vehicle loading rate, v is the maximum loading capacity of the vehicle, V is the total amount of goods to be delivered, V l is the upper bound of the number of vehicles, V u is the lower bound of the number of vehicles, V l The first ratio is 0.1, V u The second ratio is also 0.1, but the present disclosure is not limited thereto, and the first ratio and the second ratio may also have different values.

[0072] In one embodiment, the target level of the vehicle loading rate of the present disclosure is set to r. At the same time, the total amount of goods to be delivered on that day is known to be V. Assuming that the maximum loading amount that each vehicle can load is v, the lower limit of the number of vehicles to be used is calculated: The upper limit of the number of vehicles used It represents the number of vehicles required when the average loading rate is the second vehicle loading rate (r+0.1) and the first vehicle loading rate (r-0.1). The set of possible vehicle numbers S = {s|V l ≤s≤V u ,s∈N +}, that is, V l With V u All integers in between are possible numbers of vehicles that can be used.

[0073] In one embodiment, when there are multiple types of vehicle models, for example, m types, the multiple types of vehicle models are sorted from high to low according to the historical dispatch frequency (for example, the average daily dispatch frequency of each type of vehicle in the past three months) and numbered as 1, 2, ..., m, and the historical dispatch frequency ratio of each type of vehicle is set to f1:f2:...:f m ,make For the subproblem set S = {s|V l ≤s≤V u ,s∈N +}, and the vehicle set corresponding to each sub-problem s can be determined as follows: divide s by f to get the quotient quo and remainder rem, then the number of vehicles numbered i is In the above mode of the present disclosure, each vehicle type can be divided into f i quo vehicles, until the number of remaining vehicles rem is less than f, then the vehicles that were more frequently used in the past (i.e. f i larger cars), until all available quotas are used up, so at this stage each car i should be allocated vehicles, and its upper bound is f i The above method can determine the actual vehicle set K corresponding to each sub-problem s. s .

[0074] In step S230, for each possible number of vehicles in the set of possible numbers of vehicles, a sub-optimal delivery route is determined that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints.

[0075] In this step, the terminal or the server determines, for each possible number of vehicles in the set of possible numbers of vehicles, a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints.

[0076] In one embodiment, the shortest total delivery timeout refers to the shortest length of time that exceeds the delivery time required by the customer after a possible number of vehicles are used to complete the delivery. The shortest total delivery timeout is greater than or equal to zero. The shortest total delivery timeout equal to zero indicates that all goods are delivered ahead of schedule or on time.

[0077] For example, in one embodiment, the present disclosure may use each value s in the set S of the number of vehicles to correspond to a sub-problem, that is, given the number of delivery vehicles s, find the sub-optimal vehicle delivery route that meets the capacity and temperature constraints so that the total delivery timeout is the shortest. Specifically, solve the following optimization sub-problem:

[0078]

[0079] st

[0080]

[0081]

[0082]

[0083]

[0084]

[0085]

[0086]

[0087]

[0088]

[0089]

[0090] The variables designed for the above optimization problem include: K is the set of vehicles; N is the set of target delivery locations for all orders; o is the departure point, i.e. the cold storage; d is the destination of the delivery (if it is not necessary to return to the unified destination, the distance from d to all points can be set to 0); e is the indicator variable indicating that truck k passes through path (i, j); i Indicates the agreed delivery start time; i Indicates the agreed delivery end time point; a i represents the time of arrival at delivery point i; w i represents the time from arrival at the delivery point i until unloading can begin, including the waiting time q i , also includes the case where the goods arrive before the agreed time of receipt, i.e. i =max(q i ,e i -a i );s i Indicates the unloading time; t ij The time required to travel the route (i, j); l i represents the order quantity at delivery point i; Q k represents the maximum capacity of truck k; gt i Indicates the temperature layer of the goods in order i; vt k represents the temperature layer of truck k.

[0091] The constraints designed for the above optimization problem include: (1)-(5) are classic constraints for vehicle routing problems, which respectively involve: (1) Each customer has only one vehicle to provide service; (2) For any j∈N, the vehicle k that enters it must leave; (3) Each vehicle k must start from o; (4) Each vehicle k must eventually return to d; (5) Eliminate sub-loops. Constraint (6) is the identity of the arrival time, and (7) is the time window starting point constraint, which includes two situations: one is that if you arrive before the agreed time, you can only wait, and the other is that in actual scenarios, you may need to wait in line to unload; (8) is the truck capacity constraint; (9) is the temperature layer constraint unique to cold chain logistics; (10) is the integer constraint in integer programming.

[0092] The above optimization subproblems can be solved by various free or commercial optimization solvers, or you can write your own program and use various meta-heuristic methods to obtain high-quality solutions. Therefore, for each subproblem corresponding to s, an approximate shortest total delivery timeout t(s) can be obtained.

[0093] In one embodiment, for each possible number of vehicles in the set of possible numbers of vehicles, a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints is determined, including: classifying the goods to be delivered according to the required vehicle temperature layer requirements; allocating each type of classified goods to be delivered to vehicles in the corresponding temperature layer according to the vehicle loading rate and the maximum loading capacity of the vehicle; and obtaining the sub-optimal delivery route that minimizes the total delivery timeout according to the possible delivery routes and the shipping time and delivery time required by the customer.

[0094] In one embodiment, when the total timeout of a delivery with the shortest possible number of vehicles is equal to zero (ie, delivered ahead of schedule or on time), the delivery route with the shortest delivery distance is selected as the sub-optimal delivery route.

[0095] In one embodiment, w i Defined as max(q i ,e i -a i ), where for the queue time q i The modeling relies on the mining of past historical data. For example, each order delivery location can be divided into different categories according to the busyness. Each type of delivery location can be modeled using classical queuing theory, assuming the arrival rate and service rate, and taking the expected waiting time as q i ; Unloading time s i Based on the experience of business personnel, we can assume an average unloading speed v d , calculate s i = l i / v d To obtain; the driving time t between two delivery destinations i and j ijWe can also calculate based on historical experience, assuming the average speed of trucks u, To obtain, where d ij It represents the distance between i and j. It can be calculated in different ways in different logistics scenarios. For example, in inter-city transportation, especially in inter-provincial transportation, d ij The great sphere distance can be directly taken; in the city distribution (same-city delivery) scenario, the Manhattan distance or other more accurate routing distances can be used.

[0096] In one embodiment, the vehicle may have N temperature layers, where N is an integer greater than or equal to 2. In the present disclosure, it is assumed that each vehicle k has only one certain temperature layer vt k However, in reality, a vehicle can achieve multiple temperature levels by using multiple refrigerators, cargo boxes and partitions. This is especially true for large vehicles that deliver goods over long distances. Customers are usually far away from each other, and customers may order different goods from multiple temperature levels at the same time. In this case, the use of a vehicle with multiple temperature levels can greatly reduce transportation costs. For example, a vehicle must deliver goods from both the "refrigerated" temperature level and the "frozen" temperature level. In this case, the constraints (9) of the optimization subproblem can be adjusted accordingly, and the mathematical expression is: Vehicle k has two transportable temperature levels. and Then constraint (9) is changed to

[0097] In step S240, the number of vehicles used for scheduling and the global optimal delivery route are determined using the elbow method according to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible numbers of vehicles.

[0098] In this step, the terminal or server uses the elbow method to determine the number of vehicles used for scheduling and the global optimal delivery route according to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible number of vehicles.

[0099] The cold chain logistics vehicle scheduling method disclosed in the present invention obtains a set of possible vehicle quantities according to the vehicle loading rate, the maximum loading capacity of the vehicle and the total amount of goods to be delivered, and based on the set of possible vehicle quantities, obtains the number of vehicles used for scheduling and the global optimal delivery route according to the vehicle capacity constraints, temperature layer constraints and time constraints.

[0100] Figure 3 A flowchart of using the elbow method to determine the number of vehicles used in scheduling and the global optimal delivery route according to an embodiment of the present disclosure is shown.

[0101] refer to Figure 3, using the elbow method to determine the number of vehicles used in scheduling and the global optimal delivery route includes:

[0102] In step S241, the set of possible number of vehicles to be used is divided into a first set and a second set;

[0103] In step S242, linear regression is performed according to each possible number of vehicles in the first set and the second set and the corresponding shortest total delivery timeout to obtain a first prediction model and a second prediction model;

[0104] In step S243, the number of vehicles used for scheduling and the global optimal delivery route are determined according to the first prediction model and the second prediction model.

[0105] Figure 3 In an embodiment of the present invention, the set of possible numbers of vehicles to be used is divided into a first set and a second set, and linear regression is performed based on each possible number of vehicles in the first set and the second set and the corresponding shortest total delivery timeout to obtain a first prediction model and a second prediction model, and then the number of vehicles used for scheduling and the global optimal delivery route are determined based on the first prediction model and the second prediction model, so that the scheduling of cold chain logistics vehicles can be realized.

[0106] Figure 4 Another flowchart of using the elbow method to determine the number of vehicles used in scheduling and the global optimal delivery route according to an embodiment of the present disclosure is shown.

[0107] refer to Figure 4 , determining the number of vehicles used for scheduling and the global optimal delivery route according to the first prediction model and the second prediction model, including:

[0108] In step S2431, obtaining the minimum root mean square error of the first set according to the first prediction model;

[0109] In one embodiment, obtaining the minimum root mean square difference of the first set according to the first prediction model includes:

[0110] The minimum root mean square difference of the first set is obtained according to the shortest total delivery timeout corresponding to each possible number of vehicles in the first set, the predicted value of the first prediction model and the number of elements of the possible number of vehicles in the first set.

[0111] In step S2432, obtaining the minimum root mean square error of the second set according to the second prediction model;

[0112] In one embodiment, obtaining the minimum root mean square difference of the second set according to the second prediction model includes:

[0113] The minimum root mean square difference of the second set is obtained according to the shortest total delivery timeout corresponding to each possible number of vehicles in the second set, the predicted value of the second prediction model and the number of elements of the possible number of vehicles in the second set.

[0114] In step S2433, the possible number of vehicles used to divide the first set and the second set of the possible vehicle number sets and the corresponding sub-optimal delivery route are used as the number of vehicles used for scheduling and the global optimal delivery route.

[0115] Figure 4 In an embodiment of the present invention, the minimum root mean square difference of the first set is obtained according to the first prediction model, and the minimum root mean square difference of the second set is obtained according to the second prediction model. Then, the first set and the second set of possible vehicle quantity sets are divided into a combination in which the sum of the minimum root mean square difference of the first set and the minimum root mean square difference of the second set is the smallest, and the possible number of vehicles used for scheduling and the corresponding sub-optimal delivery route are used as the number of vehicles used and the global optimal delivery route, thereby realizing the scheduling of cold chain logistics vehicles.

[0116] For example, in one embodiment, after the shortest total delivery timeout t(s) corresponding to each possible number of vehicles is obtained, the possible number of vehicles set S is decomposed into a first set S1 = {V l ,V l +1,V l +2,...,V m}(where V m It is advisable to l +1,V l +2,...,V u -1}) and the second set S2 = {V m +1,V m +2,...,V u -1,V u}, so we can know that S=S1∪S2.

[0117] Perform linear regression on the point set {(s, t(s))|s∈S1} corresponding to the first set S1 to obtain the first prediction model p1(s) ​​and find the minimum root mean square error Where p1(s) ​​is the predicted value of the first prediction model fitted by the above linear regression, and |S1| is the number of elements in S1;

[0118] Similarly, find the point set {(s, t(s))|s∈S2} corresponding to the second set S2 for linear regression, obtain the first prediction model p2(s), and find the minimum root mean square error Where p2(s) is the predicted value of the second prediction model fitted by the above linear regression, and |S2| is the number of elements in S2.

[0119] Figure 5 It shows that the regression on both sides of the cut-off point is performed separately, and the cut-off point with the smallest sum of the root mean square difference is selected.

[0120] refer to Figure 5 , for each V m , we can find the sum of the root mean square error of the two parts RMSE (V m )=RMSE1(V m )+RMSE2(V m ), traverse V m All values ​​{V l +1,V l +2,...,V u -2}, find the RMSE(V m )The minimum value RMSE * The corresponding This is used as the number of dispatched vehicles, and the path planning solution solved by its corresponding sub-problem is used as the global optimal delivery route.

[0121] This disclosure proposes a vehicle dispatching method for logistics distribution scenarios, which is also applicable to collection scenarios. The disclosed method balances the needs of service quality and cost savings in terms of optimization objectives, and in terms of constraints, it not only considers the traditional truck capacity constraints, but also the temperature layer constraints unique to the cold chain scenario.

[0122] Figure 6 The block diagram of the cold chain logistics vehicle dispatching device according to one embodiment of the present disclosure is schematically shown. The cold chain logistics vehicle dispatching device 600 provided in the embodiment of the present disclosure can be set on the server side, or partly set on the terminal and partly set on the server side. For example, it can be set on Figure 1 The server 105 in the embodiment of the present invention is described below, but the present disclosure is not limited to this.

[0123] The scheduling device 600 for cold chain logistics vehicles provided in the embodiment of the present disclosure may include a first acquisition module 610 , a second acquisition module 620 , a first determination module 630 , and a second determination module 640 .

[0124] The first acquisition module 610 is configured to acquire the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle;

[0125] The second acquisition module 620 is configured to acquire a set of possible vehicle quantities according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle;

[0126] The first determination module 630 is configured to determine, for each possible number of vehicles in the set of possible number of vehicles, a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints;

[0127] The second determination module 640 is configured to use the elbow method to determine the number of vehicles used for scheduling and the global optimal delivery route according to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible number of vehicles.

[0128] According to an embodiment of the present disclosure, the above-mentioned cold chain logistics vehicle scheduling device 600 obtains a set of possible vehicle quantities according to the vehicle loading rate, the maximum loading capacity of the vehicle and the total amount of goods to be delivered. Based on the set of possible vehicle quantities, according to capacity constraints, temperature layer constraints and time constraints, the number of vehicles used for scheduling and the global optimal delivery route can be obtained, thereby realizing the scheduling of cold chain logistics vehicles.

[0129] According to the embodiments of the present disclosure, the above-mentioned cold chain logistics vehicle dispatching device 600 can be used to implement Figures 2 to 4 An implementation method for describing a cold chain logistics vehicle scheduling method.

[0130] Figure 7 A block diagram of a scheduling device 700 for cold chain logistics vehicles according to another embodiment of the present invention is schematically shown.

[0131] like Figure 7 As shown, except Figure 7 In addition to the first acquisition module 610 , the second acquisition module 620 , the first determination module 630 , and the second determination module 640 described in the implementation manner, the scheduling device 700 for cold chain logistics vehicles also includes a display module 710 .

[0132] Specifically, after the second determination module 640 obtains the number of vehicles used in the dispatch and the global optimal delivery route, the display module 710 displays the number of vehicles used in the dispatch and the global optimal delivery route to the customer or the staff.

[0133] In the cold chain logistics vehicle dispatching device 700, the number of vehicles used in the dispatching and the global optimal delivery route can be displayed through the display module 710.

[0134] Figure 8 A block diagram of a scheduling device 800 for cold chain logistics vehicles according to another embodiment of the present invention is schematically shown.

[0135] like Figure 8 As shown, except Figure 6 In addition to the first acquisition module 610 , the second acquisition module 620 , the first determination module 630 , and the second determination module 640 described in the implementation manner, the scheduling device 800 for cold chain logistics vehicles also includes a storage module 810 .

[0136] Specifically, the storage module 810 is used to store data in the scheduling process of cold chain logistics vehicles to facilitate subsequent calls and references.

[0137] It is understood that the first acquisition module 610, the second acquisition module 620, the first determination module 630 and the second determination module 640, the display module 710 and the storage module 810 can be combined in one module, or any one of the modules can be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules can be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present invention, at least one of the first acquisition module 610, the second acquisition module 620, the first determination module 630 and the second determination module 640, the display module 710 and the storage module 810 can be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on a chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or can be implemented in hardware or firmware such as any other reasonable way of integrating or packaging the circuit, or in a suitable combination of software, hardware and firmware. Alternatively, at least one of the first acquisition module 610, the second acquisition module 620, the first determination module 630 and the second determination module 640, the display module 710 and the storage module 810 can be at least partially implemented as a computer program module, and when the program is run by a computer, the function of the corresponding module can be executed.

[0138] Since each module of the dispatching device of the cold chain logistics vehicle of the exemplary embodiment of the present invention can be used to implement the above Figures 2 to 4 The steps of an example implementation of the method for scheduling cold chain logistics vehicles are described, so for details not disclosed in the implementation of the device of the present invention, please refer to the implementation of the method for scheduling cold chain logistics vehicles of the present invention described above.

[0139] The specific implementation of each module, unit and sub-unit in the scheduling device for cold chain logistics vehicles provided in the embodiment of the present disclosure can refer to the contents of the above-mentioned scheduling method for cold chain logistics vehicles, and will not be repeated here.

[0140] Fig. 9 A schematic diagram of the structure of a computer system suitable for implementing the electronic device of the embodiment of the present disclosure is shown.

[0141] It should be noted that Fig. 9 The computer system 900 of the electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0142] like Fig. 9 As shown, the computer system 900 includes a central processing unit (CPU) 901, which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM) 902 or the program loaded from the storage part 908 to the random access memory (RAM) 903. Various programs and data required for system operation are also stored in the RAM 903. The CPU 901, ROM 902 and RAM 903 are connected to each other through a bus 904. An input / output (I / O) interface 905 is also connected to the bus 904.

[0143] The following components are connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc., and a speaker; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN (Local Area Network) card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed so that a computer program read therefrom is installed into the storage section 908 as needed.

[0144] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable storage medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication part 909, and / or installed from a removable medium 911. When the computer program is executed by a central processing unit (CPU) 901, various functions defined in the method and / or device of the present application are executed.

[0145] It should be noted that the computer-readable storage medium shown in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM (Erasable Programmable Read Only Memory, Erasable Programmable Read Only Memory) or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable storage medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical cable, RF (Radio Frequency), etc., or any suitable combination of the above.

[0146] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods, devices and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment, or a part of a code, and the above-mentioned module, program segment, or a part of the code contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions selected in the box can also occur in a different order from the order selected in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram or flowchart, and the combination of boxes in the block diagram or flowchart, can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0147] The modules and / or units and / or sub-units involved in the embodiments described in the present disclosure may be implemented by software or hardware, and the modules and / or units and / or sub-units described may also be set in a processor. The names of these modules and / or units and / or sub-units do not, in some cases, constitute limitations on the modules and / or units and / or sub-units themselves.

[0148] As another aspect, the present application also provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiment; or may exist independently without being installed in the electronic device. The above computer-readable storage medium carries one or more programs, and when the above one or more programs are executed by an electronic device, the electronic device implements the method described in the following embodiment. For example, the electronic device may implement the following Figures 2 to 4 each step.

[0149] In related technologies, for example, machine learning methods, deep learning methods, etc. can be used to schedule cold chain logistics vehicles, and different methods have different applicable scopes.

[0150] It should be noted that, although several modules, units and sub-units of the device for action execution are mentioned in the above detailed description, this division is not mandatory. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules, units and sub-units described above can be concretized in one module, unit and sub-unit. Conversely, the features and functions of one module, unit and sub-unit described above can be further divided into being concretized by multiple modules, units and sub-units.

[0151] Through the description of the above implementation, it is easy for those skilled in the art to understand that the example implementation described here can be implemented by software, or by software combined with necessary hardware. Therefore, the technical solution according to the implementation of the present disclosure can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a USB flash drive, a mobile hard disk, etc.) or on a network, including several instructions to enable a computing device (which can be a personal computer, a server, a touch terminal, or a network device, etc.) to execute the method according to the implementation of the present disclosure.

[0152] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the present disclosure that follow the general principles of the present disclosure and include common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and embodiments are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the following claims.

[0153] It should be understood that the present disclosure is not limited to the exact structures that have been described above and shown in the drawings, and that various modifications and changes may be made without departing from the scope thereof. The scope of the present disclosure is limited only by the appended claims.

Claims

1. A method for dispatching cold chain logistics vehicles, characterized in that: include: Obtain the total amount of goods to be delivered, vehicle loading rate and vehicle maximum loading capacity; Obtaining a set of possible vehicle quantities according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; For each possible number of vehicles in the set of possible numbers of vehicles, determining a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints; According to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible numbers of vehicles, the number of vehicles used for scheduling and the global optimal delivery route are determined using the elbow method.

2. The method according to claim 1, characterized in that: Acquiring a possible number of vehicles according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle includes: Divide the total amount of goods to be delivered by the product of the first vehicle loading rate and the maximum loading capacity of the vehicle, and round up to obtain the upper bound of the set of possible vehicle quantities; Divide the total amount of goods to be delivered by the product of the second vehicle loading rate and the maximum loading capacity of the vehicle, and round up to obtain the lower bound of the set of possible vehicle quantities; The number of available vehicles between the upper bound of the possible number of available vehicles set and the lower bound of the possible number of available vehicles set form the possible number of available vehicles set; The first vehicle loading rate is the vehicle loading rate minus a first ratio, the second vehicle loading rate is the vehicle loading rate plus a second ratio, and both the first ratio and the second ratio are greater than zero and less than 1.

3. The method according to claim 1, characterized in that According to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible number of vehicles, the number of vehicles used for scheduling and the global optimal delivery route are determined using the elbow method, including: Dividing the set of possible numbers of vehicles into a first set and a second set; Performing linear regression according to each possible number of vehicles in the first set and the second set and the corresponding shortest total delivery timeout to obtain a first prediction model and a second prediction model; The number of vehicles used for scheduling and the global optimal delivery route are determined based on the first prediction model and the second prediction model.

4. The method according to claim 3, characterized in that: Determining the number of vehicles used for scheduling and the global optimal delivery route according to the first prediction model and the second prediction model includes: Obtaining a minimum root mean square error of the first set according to the first prediction model; Obtaining the minimum root mean square error of the second set according to the second prediction model; Among the combinations of the first set and the second set of the possible vehicle numbers, the possible vehicle numbers and the corresponding sub-optimal delivery routes that divide the first set and the second set, which have the smallest sum of the minimum root mean square difference of the first set and the minimum root mean square difference of the second set, are used as the vehicle numbers and the global optimal delivery routes used for scheduling.

5. The method according to claim 4, characterized in that Obtaining the minimum root mean square error of the first set according to the first prediction model includes: The minimum root mean square difference of the first set is obtained according to the shortest total delivery timeout corresponding to each possible number of vehicles in the first set, the predicted value of the first prediction model and the number of elements of the possible number of vehicles in the first set.

6. The method according to claim 5, characterized in that Obtaining the minimum root mean square error of the second set according to the second prediction model includes: The minimum root mean square difference of the second set is obtained according to the shortest total delivery timeout corresponding to each possible number of vehicles in the second set, the predicted value of the second prediction model and the number of elements of the possible number of vehicles in the second set.

7. The method according to claim 1, characterized in that For each possible number of vehicles in the set of possible number of vehicles, determining a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints, including: Classify the goods to be delivered according to the required vehicle temperature layer requirements; Allocate each type of cargo to be delivered after classification to vehicles in the corresponding temperature layer according to the vehicle loading rate and the vehicle's maximum loading capacity; According to the possible delivery routes and the delivery time and arrival time required by the customer, the sub-optimal delivery route with the shortest total delivery timeout is obtained.

8. The method according to claim 7, characterized in that According to the possible delivery routes and the delivery time and arrival time required by the customer, obtain the sub-optimal delivery route that minimizes the total delivery timeout, including: When the total timeout of the shortest delivery using the possible number of vehicles is equal to zero, the delivery route with the shortest distance is selected as the sub-optimal delivery route.

9. The method according to claim 1, characterized in that: The vehicle has N temperature layers, where N is an integer greater than or equal to 2.

10. The method according to claim 1, characterized in that Obtain the total amount of goods to be delivered, vehicle loading rate and vehicle maximum loading capacity, including: When the vehicle models are inconsistent, the total amount of goods to be delivered, the vehicle loading rate and the weighted maximum loading capacity of the vehicle are obtained.

11. A dispatching device for cold chain logistics vehicles, characterized in that: include: A first acquisition module is configured to acquire the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; A second acquisition module is configured to acquire a set of possible vehicle quantities according to the total amount of goods to be delivered, the vehicle loading rate and the maximum loading capacity of the vehicle; A first determination module determines, for each possible number of vehicles in the set of possible number of vehicles, a sub-optimal delivery route that minimizes the total delivery timeout under capacity constraints, temperature layer constraints, and time constraints; The second determination module is configured to use the elbow method to determine the number of vehicles used for scheduling and the global optimal delivery route according to the sub-optimal delivery route and the shortest total delivery timeout corresponding to each possible number of vehicles in the set of possible number of vehicles.

12. An electronic device, characterized in that: include: one or more processors; A storage device configured to store one or more programs, which, when executed by the one or more processors, enable the one or more processors to implement the method according to any one of claims 1 to 10.

13. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 10 is implemented.

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