Tail end logistics distribution scheduling system and method based on GA and DP

Abstract

The invention discloses a terminal logistics distribution scheduling system and method based on GA and DP, and relates to the technical field of logistics distribution scheduling, the system comprises a terminal logistics sorting center and an information interaction client, the terminal logistics sorting center is connected with the information interaction client, and the terminal logistics sorting center is connected with the information interaction client. The tail end 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. According to the method, the problems that a distribution strategy is difficult to adjust in time and a traditional single-stage scheduling algorithm cannot be applied to a multi-stage complex network in an existing method are solved.

Description

Technical Field

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

[0002] In the current intelligent terminal logistics distribution system, the complexity and diversity of scheduling problems make cost reduction and efficiency improvement a huge challenge. Traditional static scheduling solutions are difficult to cope with the dynamics of customer demand and the variability of delivery routes, and are also difficult to cope with emergencies. In real applications, customers may change the delivery time or address 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 it is difficult to adjust the delivery strategy according to immediate changes. This not only reduces delivery efficiency, but also increases the possibility of delivery errors, which in turn affects customer experience.

[0003] In addition, modern logistics networks usually involve multiple levels of distribution, such as from major distribution centers to regional distribution points and then to end customers. Each level has different optimization goals and constraints, making the scheduling problem more complicated. 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 needs and route optimization. The complexity of this multi-level network makes it difficult for traditional single-level scheduling algorithms to operate effectively in practical applications. Summary of the invention

[0004] In view of the above-mentioned deficiencies in the prior art, the present invention provides a terminal logistics distribution scheduling system and method based on GA and DP, which solves the problem that the existing methods are difficult to adjust the distribution strategy in time, and the traditional single-level scheduling algorithm cannot be applied to multi-level complex networks.

[0005] In order to achieve the above-mentioned invention object, the technical solution adopted by the present invention is: a terminal logistics distribution scheduling system based on GA and DP, including a terminal logistics sorting center and an information interaction client, the terminal logistics sorting center is connected to the information interaction client, and the terminal logistics sorting center includes a sequentially connected unloading area, an express sorting system, a storage area, a loading area, a parking area and a mobile express station;

[0006] The information interaction client is used to obtain the express logistics information of each website platform of the user;

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

[0008] The express sorting system is used to deliver express to corresponding areas for classification;

[0009] The storage area is used to store the sorted express deliveries according to size specifications;

[0010] The loading area is used to transfer the express in the storage area to the mobile express station according to the desired pickup location;

[0011] The parking area is used for parking and dispatching of mobile express delivery stations;

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

[0013] Furthermore, the information interaction client adopts a mini-program mode, and obtains the express logistics information of each online shopping platform of the user after the user registers, logs in and authorizes.

[0014] Furthermore, the express sorting system includes an automatic packing module, an automatic package turning module, a scanning and warehousing module, an express automatic sorting module and an express automatic in and out warehousing module which are connected in sequence;

[0015] The automatic packing module is used to automatically deliver the express to the packing area, and automatically slide it into the sorting conveyor belt through the packing platform;

[0016] The package automatic flip module adopts a flip plate structure to put the express barcode transmitted by the sorting conveyor belt in the position for identification and scanning;

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

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

[0019] The express delivery automatic entry and exit module adopts a stacker to enter the express delivery that has been sorted and classified, input relevant entry information, and determine the express delivery exit time according to the user's expected pickup time.

[0020] Furthermore, the mobile express station adopts 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 cell group and a waste recycling module. The solar cell group is placed on the top of the cargo box. The waste recycling module adopts 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.

[0021] In addition, the present invention also adopts a technical solution: a terminal logistics distribution scheduling method based on GA and DP, comprising the following steps:

[0022] S1: Use the information interaction client to obtain the express logistics information of each website platform of the user;

[0023] S2: Based on the express logistics information, the express sorting system is used to sort and store the express;

[0024] S3: Use the mobile express station to deliver the express after sorting and warehousing to the destination based on the genetic algorithm GA and the dynamic programming algorithm DP, and complete the terminal logistics distribution scheduling based on GA and DP.

[0025] Furthermore, in S3, the stop site selection is performed based on the genetic algorithm GA, and the formula is:

[0026]

[0027] Among them, Minimize is the minimization 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 location 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 coordinate of user j, D is the service coverage, and Δt is the time error.

[0028] Furthermore, in S3, path planning is performed based on a dynamic programming algorithm DP, wherein the total path cost is:

[0029]

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

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

[0032]

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

[0034] By optimizing vehicle configuration and route planning, the vehicle's cargo load and utilization rate will be significantly improved, reducing empty trips and unit transportation costs. At the same time, real-time route optimization can effectively respond to emergencies such as traffic conditions and order changes, thereby shortening delivery time.

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

[0036] The system's optimized dispatching strategy will reduce unnecessary vehicle travel, reduce fuel consumption and carbon emissions, and have a positive impact on the environment. It can also reduce urban traffic pressure by avoiding peak sections and congested areas.

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

[0038] Figure 1 This is a structural diagram of a terminal logistics distribution scheduling system based on GA and DP.

[0039] Figure 2 This is a flow chart of a terminal logistics distribution scheduling method based on GA and DP. DETAILED DESCRIPTION

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

[0041] Embodiment 1, as Figure 1 As shown, a terminal logistics distribution scheduling system based on GA and DP includes a terminal logistics sorting center and an information interaction client, the terminal logistics sorting center is connected to the information interaction client, and the terminal logistics sorting center includes a cargo unloading area, an express sorting system, a storage area, a cargo loading area, a parking area and a mobile express station connected in sequence;

[0042] The information interaction client is used to obtain the express logistics information of each website platform of the user;

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

[0044] The express sorting system is used to deliver express to corresponding areas for classification;

[0045] The storage area is used to store the sorted express deliveries according to size specifications;

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

[0047] The parking area is used for parking and dispatching of mobile express delivery stations;

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

[0049] The information interaction client adopts a mini-program mode, and obtains the express logistics information of the user's various online shopping platforms after the user registers, logs in and authorizes.

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

[0051] The express sorting system comprises an automatic packing module, an automatic package turning module, a scanning storage module, an express automatic sorting module and an express automatic storage and retrieval module which are connected in sequence;

[0052] The automatic packing module is used to automatically deliver the express to the packing area, and automatically slide it into the sorting conveyor belt through the packing platform;

[0053] The package automatic flip module adopts a flip plate structure to put the express barcode transmitted by the sorting conveyor belt in the position for identification and scanning;

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

[0055] The scanning warehouse module is mainly composed of three parts: electromagnetic sensor, photoelectric sensor and CCD industrial camera. Electromagnetic sensor and photoelectric sensor are mainly used to detect whether the express delivery is in place, whether the cylinder position is working, etc. The detailed functional applications are shown in Table 1:

[0056] Table 1 Sensor types and functions

[0057]

[0058] CCD industrial cameras are used to read the information contained in the barcode. They use optical principles to decode the content of the barcode and transmit it to the host or other devices via data lines or wirelessly.

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

[0060] In this embodiment, PLC is used to realize the automatic control of the express automatic sorting system. The scanning camera sends the express barcode information read to the PLC. The PLC host will control the corresponding pneumatic device and push it to the corresponding area for classification according to the destination, pickup time, weight, size and other information, so as to ensure that different coded expresses are sorted and classified through different containers;

[0061] At the same time, the scanning warehousing module and the express automatic sorting module are both switch quantity controlled. The system uses Siemens S7-200SMART PLC as the core control platform. The IO address allocation and description are shown in Table 2:

[0062] Table 2IO address allocation and description

[0063]

[0064] The express delivery automatic entry and exit module adopts a stacker to enter the express delivery that has been sorted and classified, input relevant entry information, and determine the express delivery exit time according to the user's expected pickup time.

[0065] The mobile express station adopts 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 cell group and a waste recycling module. The solar cell group is placed on the top of the cargo box. The waste recycling module adopts 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.

[0066] The mobile express station combines the high storage capacity of the express station with the high convenience of the logistics vehicle. At the same time, it adds energy-saving and emission-reduction design. It consists of a solar cell group and a waste recycling module. The solar cell group is an extended-range design, and the waste recycling module is used to recycle and store recyclable garbage such as express cartons. The chassis design has high reliability and stability. The optimization of the suspension system and power transmission system improves the power performance of the whole vehicle and ensures the stability and reliability of long-term high-load transportation. Secondly, the mobile express station adopts an independent integrated cargo door design, which reduces manual operation time, improves loading and unloading efficiency and transportation safety. The cargo door design occupies a small internal space and can meet the transportation needs of different scales. Finally, in terms of energy conservation, emission reduction and green recycling, the mobile express station uses solar energy for power supply, and places the solar panel on the top of the car. It can be folded and put away during the mobile express station to prevent damage, and can be unfolded to increase the area when docked. The waste recycling system uses an embedded shredder and a retractable waste storage device to effectively improve the green recycling efficiency of waste while ensuring personal privacy and safety.

[0067] Embodiment 2, as Figure 2As shown, a terminal logistics distribution scheduling method based on GA and DP includes the following steps:

[0068] S1: Use the information interaction client to obtain the express logistics information of each website platform of the user;

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

[0070] S3: Use the mobile express station to deliver the express after sorting and warehousing to the destination based on the genetic algorithm GA and the dynamic programming algorithm DP, and complete the terminal logistics distribution scheduling based on GA and DP.

[0071] In S3, the coordinates of the stop sites are obtained based on the genetic algorithm GA. The algorithm fully considers the stop location selection and stay time period of the mobile express station. Based on the coordinate variables and time variables, the genetic algorithm is used to obtain the optimal solution of the variables to improve the user's satisfaction with the pickup.

[0072] In S3, the stop site selection is performed based on the genetic algorithm GA, and the formula is:

[0073]

[0074] Among them, Minimize is the minimization 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 location 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 coordinate of user j, D is the service coverage, and Δt is the time error.

[0075] If the desired location 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 (no service).

[0076] The service coverage constraints are:

[0077]

[0078] This formula ensures that user j is within the service coverage 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 to provide effective service.

[0079] The constraint on the time error is:

[0080] t j -T|≤Δt

[0081] This formula ensures that the user's expected pickup time t jThe error between the actual stop time T at the express station is within the allowed time range Δt, avoiding the user's inability to pick up the package in time due to time mismatch.

[0082] In S3, path planning is performed based on the dynamic programming algorithm DP, wherein the total path cost is:

[0083]

[0084] Among them, Cost(·) is the total path cost, C(·) is the distance cost between two points, p is the total path, and p i is the i-th point after departure, p i+1 is the i+1th point after departure, p n It 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 distance from the express station (X, Y) to each user location (x j ,y j )’s initial transportation costs;

[0088] The state transfer equation is:

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

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

[0091] Since it is a two-layer optimization algorithm, the constraints of the upper and lower layers are the same:

[0092]

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

[0094] The optimal solution L for the path is:

[0095]

[0096] This objective function is used to find the optimal solution for the path and select the user path that minimizes transportation time and transportation cost. j It reflects the demand of each user. The greater the demand, the higher the path priority.

[0097] The dynamic programming algorithm will design routes for mobile express stations based on the docking locations of mobile express stations obtained by the genetic algorithm and the road traffic conditions in the areas under the jurisdiction of the terminal logistics sorting center, thereby minimizing transportation time and costs and improving transportation efficiency.

[0098] In one embodiment of the present invention, the terminal logistics distribution method of the present invention is adopted in an urban area with a length and width of 500m, and a scheduling system for terminal logistics distribution is configured to carry out terminal logistics distribution. The user learns that the express has arrived at the terminal logistics sorting center through the information interaction client, and enters the expected pickup time and address according to the demand. The express enters the express sorting system from the unloading area, and enters the barcode information through the scanning identification device and the express flip structure. At the same time, according to the user's expected pickup time information, the express is made to correspond to the diversion channel. When the express arrives at the sorting area, the movement direction of the block can be changed to change the express movement path so that the package enters the corresponding diversion channel. Secondly, for the express in and out of the warehouse process, the system will take corresponding measures according to the task information. In terms of the inbound process, the system will query the remaining cargo space and determine the corresponding position, command the stacker to transfer the express to the corresponding position and enter the relevant information into the inbound table; in terms of the outbound process, the system will determine the express outbound demand according to the user's expected pickup time and complete the outbound task according to the time period and enter the relevant information into the outbound table.

[0099] Assume that there are 50 user demands within a certain period of time, and the expected pickup location coordinates, expected pickup time, and demand are shown in Table 3. The maximum service coverage range D i Not greater than 250m.

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

[0101]

[0102] The genetic algorithm fully considers the stop location selection and stay time period of the mobile express station. Based on the coordinate variables and time variables, the location of the mobile express station is calculated to be X=300.43m, Y=258.6m, the median stop time point is t=27.278min, and the total turnover is Z=3524.51.

[0103] The dynamic programming algorithm will design a route for the mobile express station based on the docking location of the mobile express station obtained by the genetic algorithm and the road traffic conditions in the area under the jurisdiction of the terminal logistics sorting center. The specific steps are as follows:

[0104] (1) Variable summary: N = the number of data points; D = the upper limit of coverage, which is half of the number of data points or 24, whichever is smaller. This is to avoid excessive computation and limit the maximum coverage; dp = a dynamic programming array, which is used to record the maximum distance covered. It is a two-dimensional array of size (n, D+1), where dp[i][j] represents the maximum distance when the coverage of the i-th data point is j.

[0105] (2) Dynamic planning process:

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

[0107] b. State transfer: I == each data point; J == traversal coverage; 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] Based on the docking point coordinates provided by the docking point location algorithm and the travel route provided by the path planning algorithm, the mobile express station will stop at the destination for 2 hours within the specified time, unfold the movable structures on both sides of the cargo box and the solar panels on the top of the vehicle, and users can queue up in an orderly manner to pick up the items. At the same time, the additional service strategy at the head of the queue is adopted to provide additional services for users within the N positions of the queue, that is, check and pick up items. When the customer's position is less than or equal to N, the user will receive additional services until the user reaches the pickup scanner, shortening the user's actual waiting time and improving the user's waiting satisfaction. In addition, users can put the discarded packaging into the waste recycling system through the corresponding slot, and the embedded shredder will shred it, and the waste in the form of fragments will be stored in the storage device. After the service time is completed, it returns to the terminal logistics sorting center.

[0110] The present invention proposes a two-level 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 decision points and weight assignments for the resource call intensity and stop site selection problems of the entire logistics network to maximize the system efficiency. This includes maximizing the load utilization of vehicles and optimizing delivery time to achieve cost reduction and efficiency improvement. Global scheduling uses a genetic algorithm (GA), which generates an initial population based on a drop-down front-end form, performs fitness evaluation based on customized multivariate constraints, and performs selection operations, crossover operations, mutation operations, and other steps to continuously iterate and approach the optimal solution.

[0111] At the local level, the main goal is to respond to the actual distribution needs in a specific area or time period, mainly based on path planning, optimize the path according to road congestion and changes in stop sites, and reduce the delivery time by optimizing the path. Local scheduling uses 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 for each stage, and finds the global optimal path through state transfer and path backtracking. The non-aftereffect feature of dynamic programming makes the optimal decision at each stage depend only on the current state, not on the previous specific decision, thereby improving the accuracy and efficiency of decision-making.

[0112] Those skilled in the art will appreciate that the embodiments described herein are intended to help readers understand the principles of the present invention, and should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific variations and combinations that do not deviate from the essence of the present invention based on the technical revelations disclosed by the present invention, and these variations and combinations are still within the protection scope of the invention.

Claims

1. A terminal logistics distribution scheduling system based on GA and DP, characterized in that: It includes a terminal logistics sorting center and an information interaction client, the terminal logistics sorting center is connected to the information interaction client, and the terminal logistics sorting center includes a cargo unloading area, an express sorting system, a storage area, a cargo loading area, a parking area and a mobile express station connected in sequence; The information interaction client is used to obtain the express logistics information of each website platform of the user; The unloading area is used to transfer express from the truck to the sorting area, providing transfer space for express sorting; The express sorting system is used to deliver express to corresponding areas for classification; The storage area is used to store the sorted express deliveries according to size specifications; The loading area is used to transfer the express in the storage area to the mobile express station according to the desired pickup location; The parking area is used for parking and dispatching of mobile express delivery stations; The mobile express station is used to transport the express to the destination.

2. The terminal logistics distribution scheduling system based on GA and DP according to claim 1 is characterized in that: The information interaction client adopts a mini-program mode, and obtains the express logistics information of the user's various online shopping platforms after the user registers, logs in and authorizes.

3. The terminal logistics distribution scheduling system based on GA and DP according to claim 1 is characterized in that: The express sorting system comprises an automatic packing module, an automatic package turning module, a scanning storage module, an express automatic sorting module and an express automatic storage and retrieval module which are connected in sequence; The automatic packing module is used to automatically deliver the express to the packing area, and automatically slide it into the sorting conveyor belt through the packing platform; The package automatic flip module adopts a flip plate structure to put the express barcode transmitted by the sorting conveyor belt in the position for identification and scanning; The scanning and warehousing module is used to read the information of the express barcode and transmit the read information to the host; The express automatic sorting module adopts PLC automatic control, and sorts and classifies the express by scanning the information read by the camera; The express delivery automatic entry and exit module adopts a stacker to enter the express delivery that has been sorted and classified, input relevant entry information, and determine the express delivery exit time according to the user's expected pickup time.

4. The terminal logistics distribution scheduling system based on GA and DP according to claim 1 is characterized in that: The mobile express station adopts 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 cell group and a waste recycling module. The solar cell group is placed on the top of the cargo box. The waste recycling module adopts 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.

5. A terminal logistics distribution scheduling method based on GA and DP, characterized in that: The following steps are involved: S1: Use the information interaction client to obtain the express logistics information of each website platform of the user; S2: Based on the express logistics information, the express sorting system is used to sort and store the express; S3: Use the mobile express station to deliver the express after sorting and warehousing to the destination based on the genetic algorithm GA and the dynamic programming algorithm DP, and complete the terminal logistics distribution scheduling based on GA and DP.

6. The terminal logistics distribution scheduling method based on GA and DP according to claim 5 is characterized in that: In S3, the stop site selection is performed based on the genetic algorithm GA, and the formula is: Among them, Minimize is the minimization 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 location 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 coordinate of user j, D is the service coverage, and Δt is the time error.

7. The terminal logistics distribution scheduling method based on GA and DP according to claim 6 is characterized in that: In S3, path planning is performed based on the dynamic programming algorithm DP, wherein the total path cost is: Among them, Cost(·) is the total path cost, C(·) is the distance cost between two points, p is the total path, and p i is the i-th point after departure, p i+1 is the i+1th point after starting, p n It is the last point before returning to the logistics center, and depot is the logistics sorting center; The optimal solution L for the path is:

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