GA and DP based terminal logistics distribution scheduling system and method

Abstract

The application discloses a terminal logistics distribution scheduling system and method based on GA and DP, relates to the technical field of logistics distribution scheduling, and comprises a terminal logistics sorting center and an information interaction client, wherein the terminal logistics sorting center is connected with the information interaction client, and the terminal logistics sorting center comprises a goods unloading area, an express sorting system, a storage area, a goods loading area, a parking area and a mobile express station which are connected in sequence. The application solves the problems that the existing method is difficult to timely adjust the distribution strategy and the traditional single-stage scheduling algorithm cannot be applied to a multi-stage complex network.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of logistics distribution scheduling, in particular to a terminal logistics distribution scheduling system and method based on GA and DP. BACKGROUND

[0002] In the current intelligent terminal logistics distribution system, the complexity and diversity of scheduling problems make it a great challenge to reduce costs and increase efficiency. Traditional static scheduling schemes are difficult to cope with the dynamics of customer demand and the variability of distribution routes, and are also difficult to cope with emergencies. In real-world applications, customers may change delivery times or addresses at any time, which requires the scheduling system to be able to adjust the delivery plan in real time. However, most of the existing traditional static scheduling algorithms lack the ability to respond to real-time data and are difficult to adjust the delivery strategy according to the immediate changes. This not only reduces the efficiency of delivery, but also increases the possibility of delivery errors, thereby affecting customer experience.

[0003] In addition, modern logistics networks often involve multiple levels of distribution links, such as from the main distribution center to the regional distribution point, and then to the final customer. Each level has different optimization objectives and constraints, making the scheduling problem more complex. For example, the focus of first-level scheduling may be large-scale route planning and vehicle scheduling, while second-level scheduling needs to consider more detailed customer demand and route optimization. The complexity of this multi-level network makes it difficult for traditional single-level scheduling algorithms to work effectively in practical applications. SUMMARY

[0004] To address the above deficiencies in the prior art, the present application provides a terminal logistics distribution scheduling system and method based on GA and DP, which solves the problem of existing methods that are difficult to adjust delivery strategies in real time, and the problem of traditional single-level scheduling algorithms that cannot be applied to complex multi-level networks.

[0005] To achieve the above application purposes, the technical solution adopted by the present application is as follows: a terminal logistics distribution scheduling system based on GA and DP, comprising a terminal logistics sorting center and an information interaction client, the terminal logistics sorting center is connected with the information interaction client, the terminal logistics sorting center comprises a unloading area, an express sorting system, a storage area, a loading area, a parking area and a mobile express station connected in turn;

[0006] The information interaction client is used to obtain express logistics information of various website platforms of users;

[0007] The unloading area is used to transfer express from a truck to a sorting area, providing transfer space for express sorting;

[0008] The express sorting system is used to send the express to the corresponding area for classification;

[0009] The warehousing area is used for warehousing the sorted express according to size specifications;

[0010] The loading area is used for transferring the express in the warehousing area to the mobile express station according to the desired pick-up location;

[0011] The parking area is used for parking and scheduling of the mobile express station;

[0012] The mobile express station is used for transporting the express to the destination.

[0013] Further, the information interaction client adopts a small program mode, and obtains the express logistics information of each online shopping platform of the user after the user registers and logs in and is authorized.

[0014] Further, the express sorting system comprises an automatic package loading module, a package automatic turnover module, a scanning warehousing module, an express automatic sorting module and an express automatic warehousing and unwarehousing module connected in sequence;

[0015] The automatic package loading module is used for automatically sending the express to the package loading area and sliding into the sorting conveyor belt through the package loading table;

[0016] The package automatic turnover module adopts a turnover plate structure, so that the express barcode transmitted by the sorting conveyor belt is in the orientation of identification scanning;

[0017] The scanning warehousing module is used for reading the information of the express barcode and transmitting the read information to the host;

[0018] The express automatic sorting module adopts PLC automatic control, and sorts and classifies the express through the scanning camera;

[0019] The express automatic warehousing and unwarehousing module adopts a stacker, and performs warehousing, related warehousing information input and express unwarehousing according to the expected pick-up time of the user.

[0020] Further, the mobile express station adopts a novel logistics vehicle, the novel logistics vehicle comprises a cargo box, a chassis and an energy-saving and emission-reducing system, the cargo box and the chassis are detachably connected, the energy-saving and emission-reducing system comprises a solar cell group and a waste recycling module, the solar cell group is arranged at the top of the cargo box, the waste recycling module adopts an embedded paper shredder and a pullable waste storage device, the embedded paper shredder is embedded at the tail of the cargo box, and the pullable waste storage device is arranged at the bottom of the embedded paper shredder.

[0021] In addition, the technical solution adopted by the present application is: a terminal logistics distribution scheduling method based on GA and DP, comprising the following steps:

[0022] S1: obtaining the express logistics information of each website platform of the user by using the information interaction client;

[0023] S2: based on express logistics information, using express sorting system to sort the express into warehouse;

[0024] S3: using mobile express station to distribute the sorted express to the destination based on genetic algorithm GA and dynamic programming algorithm DP, completing the end logistics distribution scheduling based on GA and DP.

[0025] Further, the S3 based on genetic algorithm GA for parking site selection, the formula is:

[0026]

[0027] Wherein, Minimize is the minimum operation, Z is the total cost, w j is the demand of user j, t j is the expected pickup time of user j, (X, Y) is the parking position of mobile express station, T is the actual parking time, n is the number of users, (x j , y j ) is the expected pickup address coordinates of user j, D is the service coverage, Δt is the time error.

[0028] Further, the S3 based on dynamic programming algorithm DP for path planning, wherein the total path cost is:

[0029]

[0030] Wherein, Cost(·) is the total path cost, C(·) is the distance cost between two points, p is the total path, p i is the i-th point after departure, p i+1 is the i+1-th point after departure, p n is the last point before returning to the logistics center, depot is the logistics sorting center.

[0031] The optimal solution L of the path is:

[0032]

[0033] The beneficial effects of the present application are:

[0034] By optimizing the configuration and path planning of the vehicle, the carrying rate and utilization rate of the vehicle will be significantly improved, and the empty running and unit carrying cost will be reduced. At the same time, real-time path optimization can effectively deal with sudden events such as traffic conditions and order changes, thereby shortening the distribution time.

[0035] In terms of resource management, dispatchers can make quick decisions and improve work efficiency by relying on the optimization solutions and real-time data provided by the system. This not only reduces the workload of dispatchers but also makes the allocation of human resources more rational, reducing the ineffective waiting time and excessive labor of delivery personnel. Service quality will also be improved, as the system can accurately predict delivery times and provide flexible time and location options, thereby increasing customer satisfaction and experience.

[0036] The system's optimized scheduling strategy will reduce unnecessary vehicle travel, lower fuel consumption and carbon emissions, and have a positive impact on the environment. By avoiding peak-hour routes and congested areas, it can also alleviate urban traffic pressure.

[0037] In terms of economic benefits, the optimized dispatch system will reduce logistics operating costs, including savings in fuel, vehicle maintenance, and personnel costs. This improvement not only enhances the company's operational efficiency but also strengthens its market competitiveness, helping to attract more customers and orders and expand market share. Attached Figure Description

[0038] Figure 1 This is a structural diagram of a last-mile logistics delivery scheduling system based on GA and DP.

[0039] Figure 2 This is a flowchart of a last-mile logistics delivery scheduling method based on GA and DP. Detailed Implementation

[0040] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0041] Example 1, such as Figure 1 As shown, a last-mile logistics delivery scheduling system based on GA and DP includes a last-mile logistics sorting center and an information interaction client. The last-mile logistics sorting center is connected to the information interaction client. The last-mile logistics sorting center includes a loading area, an express sorting system, a storage area, a loading area, a parking area, and a mobile express station connected in sequence.

[0042] The information interaction client is used to obtain express delivery and logistics information from various website platforms of the user;

[0043] The unloading area is used to transfer express packages from the truck to the sorting area, providing transfer space for express package sorting;

[0044] The express sorting system is used to send express packages to the corresponding areas for classification.

[0045] The storage area is used to store sorted express packages according to their size specifications.

[0046] The loading area is used to transfer express packages in the storage area to the mobile express station according to the desired pickup location.

[0047] The parking area is used for parking and scheduling of the mobile express station;

[0048] The mobile express station is used for transporting express to the destination.

[0049] The information interaction client adopts a small program mode, and obtains the express logistics information of each online shopping platform of the user after the user registers and logs in and is authorized.

[0050] In addition, the user can preset the expected express pickup time and location in single or batch, the expected express pickup time is a specific time in the specific date selected by the user, for example, 15:00-17:00 on August 15, and the expected express pickup location is the location coordinates selected by the user, which belongs to the responsible area of the terminal logistics sorting center under the user's pickup address.

[0051] The express sorting system comprises an automatic package feeding module, a package automatic turnover module, a scanning and warehousing module, an express automatic sorting module and an express automatic warehousing and delivery module connected in sequence;

[0052] The automatic package feeding module is used for automatically feeding the express into the package feeding area and sliding into the sorting conveyor belt through the package feeding table;

[0053] The package automatic turnover module adopts a turnover plate structure, so that the express barcode transmitted by the sorting conveyor belt is in the orientation of identification scanning;

[0054] The scanning and warehousing module is used for reading the information of the express barcode and transmitting the read information to the host;

[0055] The scanning and warehousing module mainly comprises three parts of an electromagnetic sensor, a photoelectric sensor and a CCD industrial camera. The electromagnetic sensor and the photoelectric sensor are mainly used for detecting whether the express is in place, whether the cylinder position is working, etc., and the detailed function applications are shown in Table 1:

[0056] Table 1 Type and function of sensor

[0057]

[0058] The CCD industrial camera is used for reading the information contained in the barcode, and uses optical principle to decode the content of the barcode and transmit it to the host or other equipment through data line or wireless mode;

[0059] The express automatic sorting module adopts PLC automatic control, and sorts and classifies the express through the scanning camera;

[0060] The automatic control of the express automatic sorting system is realized by using PLC in the embodiment. The scanning camera sends the read express barcode information to the PLC. The PLC host computer controls the corresponding pneumatic device according to the information such as destination, pickup time, weight and size, and pushes it to the corresponding area for classification, so as to ensure that different express codes are sorted and classified through different containers.

[0061] Meanwhile, the scanning warehouse module and the express automatic sorting module are both on-off control. The system uses Siemens S7-200 SMART type PLC as the core control platform. The IO address allocation and explanation are shown in Table 2.

[0062] Table 2 IO address allocation and explanation

[0063]

[0064] The express automatic warehouse module uses a stacker to store the sorted and classified express, enter the relevant storage information, and determine the express delivery time according to the user's expected pickup time.

[0065] The mobile express station uses a new type of logistics vehicle. The new type of logistics vehicle includes a cargo box, a chassis and an energy-saving and emission-reducing system. The cargo box and the chassis are detachably connected. The energy-saving and emission-reducing system includes a solar cell group and a waste recycling module. The solar cell group is arranged at the top of the cargo box. The waste recycling module uses an embedded paper shredder and a pullable waste storage device. The embedded paper shredder is embedded at the tail of the cargo box. The pullable waste storage device is arranged at the bottom of the embedded paper shredder.

[0066] The mobile express station combines the high storage capacity of the express station and the high convenience of the logistics vehicle. Meanwhile, the energy-saving and emission-reducing design is added, which is composed of a solar cell group and a waste recycling module. The solar cell group is designed as a range extender. The waste recycling module is used for recycling and storing recyclable garbage such as express cartons. The chassis design has high reliability and stability. The optimization of the suspension system and the power transmission system improves the power performance of the whole vehicle, ensuring the stability and reliability of long-time high-load transportation. Secondly, the mobile express station uses an independent and integrated cargo box door design, which reduces the manual operation time, improves the loading and unloading efficiency and transportation safety, and the cargo box door design occupies less internal space, which can meet the transportation needs of different scales. Finally, in terms of energy saving and emission reduction and green recycling, the mobile express station uses solar energy to supply power. The solar panels are arranged at the top of the vehicle compartment. They can be folded and stored to prevent damage during the movement of the mobile express station, and can be unfolded to increase the area when parked. The waste recycling system uses an embedded paper shredder and a pullable waste storage device, which effectively improves the efficiency of green waste recycling while ensuring personal privacy and safety.

[0067] In embodiment 2, as Figure 2As shown, a GA and DP based end logistics distribution scheduling method includes the following steps:

[0068] S1: Obtain the express logistics information of each website platform of the user by using the information interaction client;

[0069] S2: Based on the express logistics information, use the express sorting system to sort and store the express;

[0070] S3: Use the mobile express station to distribute the sorted and stored express to the destination based on the genetic algorithm GA and dynamic programming algorithm DP, and complete the GA and DP based end logistics distribution scheduling.

[0071] In S3, the genetic algorithm GA is used to obtain the coordinates of the stop station. This algorithm fully considers the selection of the stop position and the stay time period of the mobile express station, and based on the coordinate variable and the time variable, the genetic algorithm is used to obtain the optimal solution of the variable, thereby improving the user's pickup satisfaction.

[0072] In S3, the genetic algorithm GA is used to select the stop station, and the formula is:

[0073]

[0074] Wherein, Minimize is the minimum operation, Z is the total cost, w j is the demand of user j, t j is the expected pickup time of user j, (X, Y) is the stop position of the mobile express station, T is the actual stop time, n is the number of users, (x j , y j ) is the expected pickup address coordinates of user j, D is the service coverage range, and Δt is the time error.

[0075] If the expected position and time of user j meet the range D and time error Δt of the service station, the service cost is 1, otherwise it is infinite (unable to serve).

[0076] Wherein, the constraint of the service coverage range is:

[0077]

[0078] This formula ensures that user j is within the service coverage range D of the express mobile service station. That is, the distance between the user and the service station must be within the maximum coverage range, so as to provide effective service.

[0079] The constraint of the time error is:

[0080] t j -T|≤Δt

[0081] This formula ensures that the expected pickup time t jThe error between the actual parking time T of the express station and the time T is within the allowed time range Δt, so that the user cannot pick up the package in time due to time mismatch.

[0082] The path planning in S3 is based on a dynamic programming algorithm DP, wherein the total path cost is:

[0083]

[0084] wherein Cost(·) is the total path cost, C(·) is the distance cost between two points, p is the total path, p i is the i-th point after departure, p i+1 is the i+1-th point after departure, p n is the last point before returning to the logistics center, and depot is the logistics sorting center.

[0085] The first algorithm after entering the actual operation is responsible for initializing the dynamic programming:

[0086] dp[0][j]=C(X,x i ,y j )

[0087] This formula is used to initialize the first row of the dynamic programming array, which represents the initial transportation cost from the express station (X, Y) to each user location (x j ,y j );

[0088] The state transition equation is:

[0089] dp[i][j]=min(dp[i-1][k]+C(x k ,x j ))

[0090] This state transition equation is used in the dynamic programming process, i.e., the minimum transportation cost from the i-1-th user location (x k ,y k ) to the i-th user location;

[0091] Since it is a double-layer optimization algorithm, the same constraint conditions for the upper and lower layers are:

[0092]

[0093] This formula ensures that all user locations in the path planning are within the service coverage of the express mobile service station, i.e., the distance between the user and the service station cannot exceed D;

[0094] The optimal path solution L is:

[0095]

[0096] The objective function is used to solve the optimal solution of the path, and the user path that can minimize the transportation time and transportation cost is selected. j Reflects the demand of each user, the larger the demand, the higher the path priority.

[0097] The dynamic programming algorithm will design the route for the mobile express station according to the parking position of the mobile express station obtained by the genetic algorithm and the road traffic condition of the area under the jurisdiction of the terminal logistics sorting center, so as to minimize the transportation time and cost and improve the transportation efficiency.

[0098] In an embodiment of the present application, the terminal logistics distribution method is used in a city area with a length and width of 500m in a region, and a set of scheduling system for terminal logistics distribution is configured to perform terminal logistics distribution. Users obtain the arrival of express at the terminal logistics sorting center through the information interaction client, and input the expected pickup time and address according to the demand. The express enters the express sorting system from the goods unloading area, and the barcode information is input through the scanning identification device and the express turnover structure. At the same time, according to the user's expected pickup time information, the express is matched with the guide channel, and when the express arrives at the sorting area, the moving direction of the change block is changed to change the moving path of the express, so that the package enters the corresponding guide channel. Secondly, according to the express in and out warehouse process, the system will take corresponding measures according to the task information. In the aspect of warehouse entry process, the system will query the remaining storage space and determine the corresponding position, and command the stacker to transfer the express to the corresponding position and input the related information into the warehouse entry table; in the aspect of warehouse exit process, the system will determine the express exit demand according to the user's expected pickup time, and complete the warehouse exit task according to the time period and input the related information into the warehouse exit table.

[0099] Suppose that there are 50 user demands in a certain time period, and the expected pickup position coordinates, expected pickup time and demand are shown in Table 3, and the maximum service coverage D i Not more than 250m.

[0100] Table 3 User expected coordinates and related demand (part)

[0101]

[0102] The genetic algorithm fully considers the selection of the parking position of the mobile express station and the stay time period, and based on the coordinate variable and the time variable, the site selection position of the mobile express station is X=300.43m, Y=258.6m, the median of the parking time point is t=27.278min, and the total turnover is Z=3524.51.

[0103] The dynamic programming algorithm will design the route for the mobile express station according to the parking position of the mobile express station obtained by the genetic algorithm and the road traffic condition of the area under the jurisdiction of the terminal logistics sorting center, so as to minimize the transportation time and cost and improve the transportation efficiency.

[0104] (1) Variable aggregation: N == number of data points; D == upper limit of coverage, taking half of the number of data points or the smaller value of 24. This is to avoid excessive calculation, limiting the maximum coverage; dp == dynamic programming array, used to record the maximum distance covered. It is a two-dimensional array with a size of (n, D+1), where dp[i][j] represents the maximum distance of the ith data point with a coverage of j.

[0105] (2) Dynamic programming process:

[0106] a. Initialization: The distance from the first data point to the last data point is taken as the initial value of the first row, representing the case when there is only one data point.

[0107] b. State transition: I == each data point; J == coverage iteration; update the value of dp[i][j].

[0108] c. Solve the maximum coverage: After the dynamic programming process is completed, the maximum coverage is the maximum value in dp[n-1][j], where j ranges from 0 to D.

[0109] The mobile express station based on the stop point location algorithm and the travel route provided by the path planning algorithm, goes to the destination stop for 2 hours within a specified time, expands the activity structure on both sides of the box and the solar panels above the vehicle, and the user can orderly queue to pick up the package. At the same time, the queue head extra service strategy is adopted to provide additional services to users within N positions in the queue, i.e. checking and picking up the package. When the customer's position is less than or equal to N, the user will receive additional services until the user reaches the pick-up code scanner, which shortens the actual waiting time of the user and improves the waiting satisfaction of the user. In addition, the user can put the waste packaging into the waste recycling system through the corresponding slot, and the embedded paper shredder is used for shredding processing, and the shredded waste is stored in the storage device. After the service time is completed, return to the end of the logistics sorting center.

[0110] The present application proposes a double-layer scheduling algorithm. The algorithm optimizes the resource allocation of the logistics system and improves the scheduling efficiency by dividing the scheduling problem into two levels: global scheduling and local scheduling. At the global level, the main goal is to derive the resource call intensity and decision points and weight assignment of the stop site location problem for the entire logistics network to maximize system efficiency. This includes maximizing the load utilization rate of the vehicle and optimizing the delivery time to achieve cost reduction and efficiency improvement. Global scheduling uses genetic algorithm (Genetic Algorithm, GA), which generates an initial population according to the pull front-end form, evaluates the fitness according to the customized multi-constraint conditions, selects, crosses, and mutates, etc. steps, and iteratively approaches the optimal solution.

[0111] At the local level, the main goal is to deal with the actual delivery demand in a specific area or time period, which is mainly path planning. The path is optimized in the case of road congestion and the change of stop site, and the delivery time is reduced through path optimization. Local scheduling adopts dynamic programming (DP), which is suitable for solving multi-stage decision problems. This method defines the state space and decision variables, calculates the optimal solution of each stage, and finds the global optimal path through state transition and path backtracking. The non-causal characteristics of dynamic programming make the optimal decision of each stage only depend on the current state, not on the specific decision before, so as to improve the accuracy and efficiency of the decision.

[0112] Those skilled in the art will appreciate that the embodiments described herein are presented for the purpose of understanding the principles of the present application and should be understood as not limiting the scope of protection of the present application to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations according to the technical inspiration disclosed in the present application without departing from the essence of the present application, and these modifications and combinations are still within the scope of protection of the present application.

Claims

1. A last-mile logistics delivery scheduling method based on GA and DP, characterized in that, Includes the following steps: S1: Use the information interaction client to obtain the user's express delivery and logistics information from various website platforms; S2: Based on express logistics information, use the express sorting system to sort and store express packages; S3: Utilize mobile express stations to deliver sorted and warehoused express packages to their destinations using genetic algorithm (GA) and dynamic programming algorithm (DP), thus completing the last-mile logistics delivery scheduling based on GA and DP. In S3, the stop site selection is based on the Genetic Algorithm (GA), and the formula is as follows: in, For minimization operation, For total cost, For users The demand, For users Expected pickup time, This is a designated location for mobile courier station stops. This refers to the actual stop time. For the number of users, For users The expected pickup address coordinates, To cover service areas, This is due to time error; In step S3, path planning is performed based on the dynamic programming algorithm (DP), where the total path cost is: in, The total cost of the path. Cost is the distance between two points. For the total path, For the first day after departure One point, For the first day after departure One point, The last point before returning to the logistics center, For logistics sorting center; Optimal path solution for: 。 2. A system for a last-mile logistics delivery scheduling method based on GA and DP as described in claim 1, characterized in that, It includes a last-mile logistics sorting center and an information interaction client. The last-mile logistics sorting center is connected to the information interaction client. The last-mile logistics sorting center includes a loading area, an express sorting system, a storage area, a loading area, a parking area, and a mobile express station, which are connected in sequence. The information interaction client is used to obtain express delivery and logistics information from various website platforms of the user; The unloading area is used to transfer express packages from the truck to the sorting area, providing transfer space for express package sorting; The express sorting system is used to send express packages to the corresponding areas for classification. The storage area is used to store sorted express packages according to their size specifications. The loading area is used to transfer express packages in the storage area to the mobile express station according to the desired pickup location. The parking area is used for the parking and dispatching of mobile express stations; The mobile courier station is used to transport packages to their destination.

3. The system of the last-mile logistics delivery scheduling method based on GA and DP according to claim 2, characterized in that, The information interaction client adopts a mini-program model. After a user registers, logs in, and authorizes, it obtains the user's express delivery and logistics information from various online shopping platforms.

4. The system of the last-mile logistics delivery scheduling method based on GA and DP according to claim 2, characterized in that, The express sorting system includes an automatic packing module, an automatic package flipping module, a scanning and warehousing module, an automatic express sorting module, and an automatic express inbound and outbound module connected in sequence. The automatic packing module is used to automatically send express packages into the packing area and automatically slide them into the sorting conveyor belt via the packing platform. The automatic package flipping module adopts a flipping plate structure, which positions the express barcodes transported by the sorting conveyor belt for identification and scanning. The scanning and warehousing module is used to read the information from the express delivery barcode and transmit the read information to the host. The automatic parcel sorting module is automatically controlled by a PLC and sorts and classifies parcels by scanning information read by a camera. The automated parcel warehousing module uses a stacker crane to store sorted and categorized parcels, input relevant warehousing information, and determine the parcel's outbound time based on the user's desired pickup time.

5. The system of the last-mile logistics delivery scheduling method based on GA and DP according to claim 2, characterized in that, The mobile express station uses a new type of logistics vehicle, which includes a cargo box, a chassis, and an energy-saving and emission-reduction system. The cargo box and the chassis are detachably connected. The energy-saving and emission-reduction system includes a solar panel and a waste recycling module. The solar panel is placed on top of the cargo box. The waste recycling module uses an embedded paper shredder and a pull-out waste storage device. The embedded paper shredder is embedded in the rear of the cargo box, and the pull-out waste storage device is placed at the bottom of the embedded paper shredder.

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