Tobacco logistics punctual control method

Abstract

The invention discloses a tobacco logistics punctual control method, which comprises the following steps: S1, customer time selection and information acquisition: an online tobacco distribution reservation platform is built, and the platform provides a calendar type time selection interface and place and name information needing to be input by a customer, and selects expected delivery time; s2, data integration and demand analysis: summarizing and integrating customer information input on the platform, and considering customer storage capability according to customers; and S3, vehicle scheduling and path planning: calculating the distribution priority and the distribution time according to the receiving time and the delivery place between different users. According to the invention, the online tobacco distribution reservation platform is established, so that the customer can select the expected receiving time independently, the customer can reasonably determine the receiving time of the goods according to the actual conditions of the customer, such as shop staff arrangement and sales rhythm, and many problems caused by delivery of the goods in the rest or busy period of a merchant are avoided.

Description

Technical Field

[0001] The invention relates to the technical field of tobacco logistics, and in particular to a tobacco logistics punctual control method. Background Art

[0002] With the development of science and technology, tobacco has become a common necessity in people's daily life. In the process of tobacco supply, the factory usually transports the tobacco after production and transmits the tobacco to designated merchants to achieve the purpose of supply and sale.

[0003] In the process of supplying goods and transporting goods for merchants, manufacturers usually need to take into account the locations of different merchants and then plan the logistics routes to ensure that each merchant receives the tobacco goods accurately and achieve the effect of logistics transmission.

[0004] However, with the changes in the market environment, its limitations have gradually emerged. On the one hand, the pace of modern business activities has accelerated, and the business models of merchants have become more diversified, with different business hours and operating rules. Some merchants may be on break during certain time periods, such as at night or during lunch breaks. At this time, delivery may result in the goods not being processed in time, increasing the risk of goods being stranded outside the store, which not only occupies public space but also may face safety hazards. On the other hand, when delivering goods during busy business hours, the merchants may have no time to take care of the receipt and sorting of the goods, resulting in the goods piling up at the entrance or passage of the store, affecting the normal business order, causing inconvenience to the merchants, and even causing damage or loss of the goods. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a tobacco logistics punctual control method, which solves the problem that the traditional tobacco logistics transportation mode fails to take into account the diversified business models, different business hours and operating rules of merchants, and the goods cannot be processed in time and there is a risk of detention when delivering goods during the merchants' rest or busy periods.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: A tobacco logistics on-time control method, comprising the following steps:

[0007] S1. Customer time selection and information collection: Build an online tobacco delivery reservation platform. The platform also provides a calendar-style time selection interface. Customers need to enter the location and name information and select the desired delivery time.

[0008] S2. Data integration and demand analysis: Summarize and integrate customer information entered on the platform, and consider customer storage capacity based on the customer;

[0009] S3. Vehicle scheduling and route planning: Calculate the delivery priority and delivery time according to the delivery time and delivery location between different users. The specific calculation process includes: using an adaptive large-area search algorithm;

[0010] S4. Real-time monitoring and adjustment: GPS positioning systems and IoT sensors are installed on delivery vehicles to provide real-time feedback on location, speed, and driving direction to the logistics management system. During transportation, when users change the delivery time due to travel, an adaptive large-area search algorithm is used to adjust the delivery plan.

[0011] S5. Delivery completed: After the vehicle arrives at the customer's point, the customer confirms receipt through the electronic signature device, and the system records the actual delivery time.

[0012] Preferably, the use of the adaptive large-area search algorithm includes: 1. Constructing an initial solution: randomly assigning vehicles to customer points to form an initial delivery route set s0, assuming that the vehicle set is V = {v1, v2, ..., v m}, the customer set is Ci={c1,c2,…,c n}, and the initial solution s0 is expressed as S0 = {r1, r2, …, r m}, where r i represents the delivery route of vehicle Ui, r i =(c i1 ,c i2 ,…,c ik ), c i1 Represents the jth customer point on the delivery route of vehicle Ui, and k is the number of customer points on the route.

[0013] Preferably, the use of the adaptive large domain search algorithm further includes: 2. Selecting an operator: Let D = {d1, d2, ..., d p} is the set of destruction operators, R = {r1, r2, …, r q} is a set of repair operators. The initial weight of each operator is equal, set to w0, and the destruction operator d is selected. i Probability Select the repair operator r i Probability During the iteration process, the weights are updated according to the operator performance.

[0014] Preferably, the use of the adaptive large-area search algorithm further includes: 3. Destruction operation: calculating the conflict degree CD (c i ), let the delivery time window of vehicle U be [ET v ,LT v ], the expected delivery time window of the customer's click Ci is [eT i,lT i ], the vehicle goes from the warehouse to the customer point c i The estimated travel time is t 0i , from customer point c i The estimated travel time to the next customer point is t i(i+1) .

[0015] Preferably, the use of the adaptive large domain search algorithm further includes: 4. Updating operator weights: assuming the current optimal solution is S best , after destruction and repair operations, a new solution is obtained new , calculate the difference between the new solution and the optimal solution Δf=f(S new )-f(S best ), where the objective function f(S) is the total delivery time of the vehicle. When Δf<0, it means that the new solution is better, then the weights of the selected destruction operator d and repair operator r are increased, and the update formula is w(d)=w(d)+α,w(r)=w(r)+α. When Δf≥0, the weights are reduced, and the update formula is w(d)=w(d)-β,w(r)=w(r)-β, while ensuring that the weights are within a reasonable range.

[0016] Preferably, the use of the adaptive large-area search algorithm further includes: 5. Repair operation: for the destroyed solution S d For each removed customer point Ci in , calculate the increase in the total vehicle delivery time ΔT when inserting it into different positions of each feasible route ij , assuming that the vehicle before insertion v j The delivery route is r j =(c j1 ,c j2 ,…,c jk ), the route after inserting the customer point Ci to the position l (1≤l≤k+1) is r j ′ =(c j1 ,…,c j(l-1) ,c i ,c jl ,…,c jk ) Calculate the total delivery time T(r) of the inserted vehicle j ′ ), then ΔT ij (l) = T(r j ′ )-T(r j ), select ΔT ij Insert the customer point Ci at the minimum position to obtain the repaired solution Sr.

[0017] Preferably, the use of the adaptive large-area search algorithm further includes: 6. Deciding whether to accept a new solution: assuming that the current temperature is T, the new solution S newWith the current solution S cur The difference in the objective function value is Δf, and the probability of accepting the new solution is P accept The calculation formula is: Generate a random number r (0≤r≤1), when r <P accept , then accept the new solution S new As the current solution S for the next iteration cur , otherwise, keep the current solution unchanged.

[0018] Preferably, the use of the adaptive large domain search algorithm further includes: 7. Determining the end condition: when the maximum number of iterations N is reached max , or the optimal solution is in N consecutive stop , when there is no improvement in the first iteration, the algorithm ends and the current optimal solution S is output best As the final delivery solution.

[0019] Preferably, in the destruction operation, the specific conflict degree CD(c i ) is calculated as:

[0020] Preferably, an application of a tobacco logistics on-time control method, such as the tobacco logistics on-time control method described in claims 1-9, is applied in the tobacco field, especially in the tobacco logistics field.

[0021] The present invention provides a tobacco logistics punctuality control method, which has the following beneficial effects:

[0022] 1. The present invention builds an online tobacco delivery reservation platform to allow customers to independently choose the desired delivery time, which fully considers the diversified business models, different business hours and operating rules of merchants. This enables customers to reasonably determine the delivery time according to their actual situation, such as store staff arrangement, sales rhythm, etc., and avoids many problems caused by the delivery of goods during the merchant's rest or busy hours.

[0023] 2. The present invention uses an adaptive large neighborhood search algorithm to perform vehicle scheduling and route planning while taking delivery time into consideration. It can comprehensively consider various constraints based on information such as the delivery time and delivery location selected by the customer, and further improve the accuracy of the delivery time of the distribution plan.

[0024] 3. The present invention enhances the flexibility and reliability of logistics distribution through real-time monitoring and dynamic adjustment. When merchants need to modify the delivery time, timely feedback is provided to the calculation system to re-plan the delivery plan, further improving the adaptability of the logistics transportation method. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a flow chart of the control method of the present invention;

[0026] Figure 2 This is a flow chart of the adaptive large-domain search algorithm of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] Example:

[0029] Please see attached Figure 1 - Attachment Figure 2 The embodiment of the present invention provides a tobacco logistics on-time control method, comprising the following steps:

[0030] S1. Customer time selection and information collection: Build an online tobacco delivery reservation platform. The platform also provides a calendar-style time selection interface. Customers need to enter the location and name information, and can select the desired delivery time in half an hour as the minimum unit;

[0031] S2. Data integration and demand analysis: Summarize and integrate customer information entered on the platform, and consider the customer's storage capacity according to the customer. For customers with limited storage capacity, avoid arranging delivery when their warehouse is full, reasonably adjust the delivery time or deliver in batches, and notify the above customers in a timely manner;

[0032] S3. Vehicle scheduling and route planning: Calculate the delivery priority and delivery time according to the delivery time and delivery location of different users. The specific calculation process is as follows:

[0033] Use the adaptive large area search algorithm:

[0034] 1. Construct the initial solution: Randomly assign vehicles to customer points to form the initial delivery route set s0. Let the vehicle set be V = {v1,v2,…,v m}, the customer set is Ci={c1,c2,…,c n}, and the initial solution s0 is expressed as S0 = {r1, r2, …, r m}, where r i represents the delivery route of vehicle Ui, r i =(c i1 ,c i2 ,…,c ik ), c i1 Represents the jth customer point on the delivery route of vehicle Ui, and k is the number of customer points on the route.

[0035] 2. Select operator: Let D = {d1, d2, ..., d p} is the set of destruction operators, R = {r1, r2, …, r q} is a set of repair operators. The initial weight of each operator is equal, set to w0, and the destruction operator d is selected. i Probability Select the repair operator r i Probability During the iteration process, the weights are updated according to the operator performance.

[0036] 3. Destruction operation (taking destruction based on time correlation as an example): Calculate the conflict degree CD (c i ), let the delivery time window of vehicle U be [ET v ,LT v ](ET v is the earliest allowed departure time, LT v is the latest allowed arrival time), the expected delivery time window of the customer’s order is [eT i ,lT i ], the vehicle goes from the warehouse to the customer point c i The estimated travel time is t 0i , from customer point c i The estimated travel time to the next customer point (or back to the warehouse) is t i(i+1) ; Conflict degree CD(c i ) is calculated as:

[0037] 4. Update operator weights: Let the current optimal solution be S best , after destruction and repair operations, a new solution is obtained new , calculate the difference between the new solution and the optimal solution Δf=f(S new )-f(S best ), where the objective function f(S) is the total delivery time of the vehicle (including driving time, waiting time, etc.). When Δf<0, it means that the new solution is better, then the weights of the selected destruction operator d and repair operator r are increased, and the update formula is w(d)=w(d)+α,w(r)=w(r)+α (α is the weight increase step size). When Δf≥0, the weight is reduced, and the update formula is w(d)=w(d)-β,w(r)=w(r)-β (β is the weight reduction step size). At the same time, the weight is guaranteed to be within a reasonable range (0≤w(d),w(r)≤w max );

[0038] 5. Repair operation (taking greedy repair as an example): For the solution S after destruction dFor each removed customer point Ci in , calculate the increase in the total delivery time of the vehicle when inserting it into different positions of each feasible route (vehicle load and volume allowable and not violating the time window constraint) ΔT ij , assuming that the vehicle before insertion v j The delivery route is r j =(c j1 ,c j2 ,…,c jk ), the route after inserting the customer point Ci to the position l (1≤l≤k+1) is r j ′ =(c j1 ,…,c j(l-1) ,c i ,c jl ,…,c jk ) Calculate the total delivery time T(r) of the inserted vehicle j ′ ), then ΔT ij (l) = T(r j ′ )-T(r j ), select ΔT ij Insert the customer point Ci at the minimum position to obtain the repaired solution Sr;

[0039] 6. Decide whether to accept the new solution (simulated annealing acceptance criteria): Let the current temperature be T and the new solution S new With the current solution S cur The difference in the objective function value is Δf (such as the total delivery time difference of the above vehicles), and the probability of accepting the new solution is P accept The calculation formula is: Generate a random number r (0≤r≤1), if r <P accept , then accept the new solution S new As the current solution S for the next iteration cur , otherwise, keep the current solution unchanged;

[0040] 7. Determine the end condition: when the maximum number of iterations N is reached max , or the optimal solution is in N consecutive stop When there is no improvement in one iteration, the algorithm ends and the current optimal solution S is output best As the final delivery solution.

[0041] S4. Real-time monitoring and adjustment: GPS positioning systems and IoT sensors are installed on delivery vehicles to provide real-time feedback on location, speed, and driving direction to the logistics management system. The data is updated every 3-5 minutes. During transportation, when the user changes the delivery time, the delivery personnel are reminded in time, and the changed time is sent to the calculation system. The above calculation steps are used to adjust the delivery plan.

[0042] S5. Delivery completed: After the vehicle arrives at the customer's point, the customer confirms receipt through the electronic signature device, and the system records the actual delivery time, thereby confirming that the delivery work is successfully completed.

[0043] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A tobacco logistics on-time control method, characterized in that: The following steps are involved: S1. Customer time selection and information collection: Build an online tobacco delivery reservation platform. The platform also provides a calendar-style time selection interface. Customers need to enter the location and name information and select the desired delivery time. S2. Data integration and demand analysis: Summarize and integrate customer information entered on the platform, and consider customer storage capacity based on the customer; S3. Vehicle scheduling and route planning: Calculate the delivery priority and delivery time according to the delivery time and delivery location between different users. The specific calculation process includes: using an adaptive large-area search algorithm; S4. Real-time monitoring and adjustment: GPS positioning systems and IoT sensors are installed on delivery vehicles to provide real-time feedback on location, speed, and driving direction to the logistics management system. During transportation, when users change the delivery time due to travel, an adaptive large-area search algorithm is used to adjust the delivery plan. S5. Delivery completed: After the vehicle arrives at the customer's point, the customer confirms receipt through the electronic signature device, and the system records the actual delivery time.

2. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large-area search algorithm includes:

1. Constructing an initial solution: randomly assigning vehicles to customer points to form an initial delivery route set s0, assuming that the vehicle set is V = {v1, v2, ..., v m }, the customer set is Ci={c1,c2,…,c n }, and the initial solution s0 is expressed as S0 = {r1, r2, …, r m }, where r i represents the delivery route of vehicle Ui, r i =(c i1 ,c i2 ,…,c ik ), c i1 Represents the jth customer point on the delivery route of vehicle Ui, and k is the number of customer points on the route.

3. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large domain search algorithm also includes:

2. Selecting an operator: Let D = {d1, d2, ..., d p } is the set of destruction operators, R = {r1, r2, …, r q } is a set of repair operators. The initial weight of each operator is equal, set to w0, and the destruction operator d is selected. i Probability Select the repair operator r i Probability During the iteration process, the weights are updated according to the operator performance.

4. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large-area search algorithm also includes:

3. Destruction operation: Calculate the conflict degree CD (c i ), let the delivery time window of vehicle U be [ET v ,LT v ], the expected delivery time window of the customer's click Ci is [eT i ,lT i ], the vehicle goes from the warehouse to the customer point c i The estimated travel time is t 0i , from customer point c i The estimated travel time to the next customer point is t i(i+1) .

5. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large domain search algorithm also includes:

4. Updating operator weights: assuming the current optimal solution is S best , after destruction and repair operations, a new solution is obtained new , calculate the difference between the new solution and the optimal solution Δf=f(S new )-f(S best ), where the objective function f(S) is the total delivery time of the vehicle. When Δf<0, it means that the new solution is better, then the weights of the selected destruction operator d and repair operator r are increased, and the update formula is w(d)=w(d)+α,w(r)=w(r)+α. When Δf≥0, the weights are reduced, and the update formula is w(d)=w(d)-β,w(r)=w(r)-β, while ensuring that the weights are within a reasonable range.

6. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large domain search algorithm also includes:

5. Repair operation: for the destroyed solution S d For each removed customer point Ci in , calculate the increase in the total vehicle delivery time ΔT when inserting it into different positions of each feasible route ij , assuming that the vehicle before insertion v j The delivery route is r j =(c j1 ,c j2 ,…,c jk ), the route after inserting the customer point Ci to the position l (1≤l≤k+1) is r j ′ =(c j1 ,…,c j(l-1) ,c i ,c jl ,…,c jk ) Calculate the total delivery time T(r) of the inserted vehicle j ′ ), then ΔT ij (l) = T(r j ′ )-T(r j ), select ΔT ij Insert the customer point Ci at the minimum position to obtain the repaired solution Sr.

7. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large-area search algorithm also includes:

6. Deciding whether to accept a new solution: assuming that the current temperature is T, the new solution S new With the current solution S cur The difference in the objective function value is Δf, and the probability of accepting the new solution is P accept The calculation formula is: Generate a random number r (0≤r≤1), when r <P accept , then accept the new solution S new As the current solution S for the next iteration cur , otherwise, keep the current solution unchanged.

8. A tobacco logistics on-time control method according to claim 1, characterized in that: The use of the adaptive large domain search algorithm also includes:

7. Determining the end condition: when the maximum number of iterations N is reached max , or the optimal solution is in N consecutive stop , when there is no improvement in the first iteration, the algorithm ends and the current optimal solution S is output best As the final delivery solution.

9. A tobacco logistics on-time control method according to claim 1, characterized in that: In the destruction operation, the specific conflict degree CD(c i ) is calculated as:

10. An application of a tobacco logistics on-time control method, characterized in that: A tobacco logistics on-time control method as described in claims 1-9 is applied in the tobacco field, especially in the tobacco logistics field.

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