CN112613700B - Multi-warehouse-point multi-direction delivery scheduling management system for cigarettes - Google Patents

Multi-warehouse-point multi-direction delivery scheduling management system for cigarettes Download PDF

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CN112613700B
CN112613700B CN202011399607.3A CN202011399607A CN112613700B CN 112613700 B CN112613700 B CN 112613700B CN 202011399607 A CN202011399607 A CN 202011399607A CN 112613700 B CN112613700 B CN 112613700B
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vehicle
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data
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CN112613700A (en
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徐跃明
安裕强
欧阳世波
陈晓伟
王磊
迟文超
谢俊明
李柏宇
余丽莎
王康
王鹍
秦希
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Hongyun Honghe Tobacco Group Co Ltd
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Abstract

The invention discloses a multi-point multi-direction delivery scheduling management system for cigarettes, which belongs to the field of cigarette logistics, wherein the multi-point multi-direction delivery scheduling management system for cigarettes can provide real-time tracking of key nodes, comprises vehicle tracking and order tracing, and realizes the functions of pre-ordering and pre-ordering of the cigarettes, including active reservation and passive reservation, and vehicle-to-warehouse report function; the system comprises the following architecture: web gateway, cloud gateway, registry service center, configuration management service, rights management service, data center service, path planning service web application service. The invention provides the related training and maintenance service of the system application, ensures smooth operation of the system, is convenient to use, and has safe and controllable data. The full-flow visualization of planned orders, planned scheduling and planned execution is realized through the system.

Description

Multi-warehouse-point multi-direction delivery scheduling management system for cigarettes
Technical Field
The invention belongs to tobacco logistics watershed, and particularly relates to a multi-warehouse multi-direction delivery scheduling management system for cigarettes.
Background
Due to the continuous deepening of the market orientation reform of the cigarette marketing in the tobacco industry in recent years, the finished product cigarette warehouse department is used as a logistics production department for organizing the delivery operation according to orders, the uncertainty of the faced orders is increased, and the early period of the delivery operation is shortened greatly than the past. Based on the dispatching optimization result of the existing order, dynamically receiving the newly arrived order and optimizing the existing dispatching result again, thereby becoming a core problem of dispatching optimization of the finished product cigarette warehouse department.
Under the current industrial structure of the China tobacco industry, customers of the cigarette industry enterprises are municipal tobacco business companies, the tobacco companies are arranged according to administrative division of municipal cities, and each municipal city has one tobacco company. Most cigarette industry enterprises govern a plurality of cigarette factories, and one cigarette factory generally also has a plurality of finished cigarette warehouses, each of which is a delivery point. The sales department of the cigarette industry enterprise can make an order for a finished cigarette delivery plan according to market demands, and usually, the order contains all information required for sales delivery as well as sales orders of other industries, and meanwhile, the order arrives at a warehouse and is delivered in batches in a rolling way by taking a day as a unit due to the uncertainty of the market demands. According to the relevant requirements of the special sale management of cigarettes, all orders have a unique delivery factory, and the same order cannot be shipped across the factory, but can be shipped across the warehouse between delivery points inside the factory. When the inventory structure of the delivery point is not matched with the demand of the order, the inventory structure can be adjusted by dumping, and the demand and the inventory can be matched by a mode of crossing the inventory. Each truck can carry a plurality of orders whose sum does not exceed its upper limit of load and which is not lower than its lower limit of load, each order can only have one carrier vehicle and cannot be shipped separately for the same order, while considering the planned order placed by the upstream department, the customer service timeliness and the maximum shipping capacity of the warehouse are already considered, thus requiring the warehouse to complete the planned day of the day. How to build a finished product cigarette dispatching management system is also a core problem faced by dispatching optimization of finished product logistics warehouse operation of tobacco industry enterprises.
Disclosure of Invention
The invention realizes the flow and the intellectualization of the finished product logistics plan dispatch through the system. The system is used for pushing and tracking the whole flow information of the group finished product logistics plan, and simultaneously, the system is used for realizing the auxiliary decision of the whole flow of the planning and dispatching, realizing the intelligence of the decision and promoting the standardization and the flow of the finished product logistics plan dispatching and managing.
In order to achieve the above purpose, the present invention is realized by the following technical scheme: the multi-warehouse point multi-direction delivery scheduling management system of the cigarettes can provide real-time tracking of key nodes, including vehicle tracking and order tracing, and the function of realizing delivery advance reservation comprises active reservation and passive reservation, and the function of reporting delivery to a warehouse; the method is characterized in that: the system comprises the following architecture: the system comprises a web gateway, a cloud gateway, a registration service center, a configuration management service, a rights management service, a data center service and a path planning service web application service, wherein the architecture forms a data storage layer, a data logic layer, an algorithm layer, an application layer, a view layer and a support service of the system.
(1) Data storage layer: storage service, algorithm, rights data, (2) data logic layer: management between processing parts of data, (3) algorithm layer: analyzing orders and vehicle data through corresponding algorithms in a system algorithm library, planning a delivery route, (4) an application layer: providing views for orders, vehicles, algorithmic data, providing user interaction logic processing, (5) view layer: providing a data view of good user experience in the form of hypertext in response to user interaction, (6) supporting services: and integrating each independent service in the micro-service system to enable each independent service component to form a complete service system.
(1) WEB gateway: a WEB end request agent and verification, (2) a cloud gateway: vehicle management cloud and RPA order system data receiving interface, data security and authority verification, (3) service registration center: micro-service architecture service governance, (4) configuration management service: uniformly managing and issuing configuration information of a micro-service system, (5) managing the authority management service: providing user creation and authorization services, (6) data center services: core data storage and integration services, (7) path planning algorithm services: path planning and order processing algorithm service, (8) WEB application service: providing WEB end view and controller logic.
The algorithm layer comprises a finished cigarette library adjustment model and a scheduling algorithm design; the finished cigarette warehouse-adjusting model needs to build a minimum warehouse-adjusting cost fitness function, so that the cost for warehouse adjustment is minimum;
then there are:
an nth warehouse representing a kth production shipment point
Library adjustment principle:
if there isWhen k=k ', the two warehouses n and n' can mutually adjust the warehouses; when k is not equal to k ', the warehouse n and n' of the two warehouses cannot be adjusted;
objective function:
according to 0.4756 yuan/ten thousand of transportation unit price and 0.72 yuan/ten thousand of loading and unloading operation cost calculation, 5 months in 2019-5 months in 2020, the internal allocation cost is reduced by more than 100 ten thousand yuan;
the calculation process is as follows:
wherein ,tonnage representing the tonnage of cargo loaded by the A-th vehicle from the nth warehouse at the kth point of production shipment to the other warehouse, a box of 50000 cigarettes, a cigarette weighing 1 gram;
total time of the vehicle A transportation (T A )：
Minimum delivery time cost fitness function (T):
the vehicle A carries the fee in the transportation process:
m A ＝u(wA i ×L ij +∑(wA i -∑wA j )×L jj' ) (4)
wherein ：
minimum shipping cost fitness function:
wherein ：
optimization vector:
V-min(M,T) (8)
the objective function is:
constraint conditions:
vehicle loading upper and lower limit constraints, determined by vehicle own performance:
w min ≤wA i ≤w max (10)
the daily shipment upper limit of the shipment point i is determined by a warehouse structure, staff and working time:
the standard transportation limit is determined by the standard transportation rule of the cigarette transportation:
wherein, i ith delivery point, j jth delivery point, omega tj Ton of cigarettes in t of j-th receiving point, a j The 0-1 variable represents whether the A vehicle delivers the j receiving point, A i 0-1 variable indicating whether the A-th vehicle originated at the i-shipment point, ωA j Tonnage, ωA, of cargo carried by the A-th vehicle to the j-th receiving point i Tonnage of cargo loaded by the A-th vehicle from the i-th delivery point, tA i Delivery time of the A-th vehicle at the i-th delivery point, tA j Warehouse-in time of A-th vehicle at j-th receiving pointTA ij The time of the journey of the A-th vehicle from the i-th delivery point to the j-th receiving point, TA jj' The distance time of the A-th vehicle from the j-th receiving point to the j' -th receiving point, V i Indicating the delivery speed of the ith delivery point, L ij Representing the slave iThe path from the point of sale to the j-th point of sale, L jj' Representing a path from a j-th receiving point to a j' -th receiving point, M representing a total shipping cost, M A Representing the total cost of vehicle a during transit, v representing the average speed of the vehicle.
Preferably, the scheduling algorithm is designed by: the method comprises the following steps of (1) combining a Hopfield neural network with a simulated annealing algorithm; (2) a combination of Hopfield neural network and Levy flight strategy; (3) Based on the steps, an IHNN mixing algorithm for solving the problem of tobacco logistics hierarchical scheduling is constructed by utilizing a mixing strategy;
the combination of the (1) Hopfield neural network and the simulated annealing algorithm adopts the following detailed method; (1) setting an initial state x i ；
(2) Will x i Setting as a starting point, substituting the starting point into the Hopfield neural network to perform iterative operation, and calculating E { x ] of the network at the moment i }；
(3) In state x i Randomly generated disturbances Δx in the vicinity i I.e. the state becomes x at this time i +Δx i Then the result is brought into the Hopfield neural network to carry out iterative operation, and the minimum value E { x } is output when the network is stable i +Δx i }；
(4) If it isThen->Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
(5) if E { x i +Δx i }＞E{x i If yes, then accept state E { x } by determining if the Metropolis criterion is met i +Δx i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge; if not meet E { x } i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
the junction of (2) Hopfield neural network and Levy flight strategyThe following detailed method is adopted: (1) setting an initial state x i ；
(2) Will x i Setting as a starting point, substituting the starting point into the Hopfield neural network to perform iterative operation, and calculating E { x ] of the network at the moment i }；
(3) For state x i Using Levy flight strategy movement based on flight probabilityStep size, i.e. the state becomes +.>Inputting into Hopfield neural network for iterative operation, and outputting minimum value +.>
(4) If it isThen->Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
(5) if it isThen E { x } i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
3) Based on the steps, the IHNN mixing algorithm for constructing the tobacco logistics hierarchical scheduling problem by utilizing the mixing strategy comprises the following detailed steps: (1) Constructing a Hopfield neural network, and inputting the collected historical carpooling order data into the Hopfield neural network to train the neural network;
(2) Randomly selecting a starting point x from a trained Hopfield neural network 0 I.e. the initial hierarchical scheduling scheme, f (x) is calculated according to the tobacco logistics hierarchical scheduling double-layer optimization objective function 0 ) Let k=0;
(3) The scheduling scheme and order dynamic data are input into the Hopfield neural network, and the search is performed by using a gradient descent method (assuming that the starting point of the search is x (k) ) Find the local minimum point x of f (x) (k)* . For a scheduling scheme of an order which needs to be subjected to scheduling but does not meet the constraint of a scheduling time window, marking the scheduling scheme in an algorithm, and performing independent hierarchical optimization by using a Hopfield neural network;
(4) From x (k)* Initially, the algorithm is partially and deeply explored, and the simulated annealing algorithm is operated until a new point x is found (k+1) This is to satisfy f (x (k+1) )-f(x (k)* )≤-δ k Of delta, where delta k Is a positive number;
(5) Updating x (k)* Let x (k ) * ＝x (k+1) . Performing global optimization on the algorithm, running a Levy flight strategy, and updating to obtain a new point x (k+1) This point satisfies f (x (k+1) )-f(x (k)* )≤-δ k ；
(6) Let k=k+1, return to step (2) until the algorithm converges;
(7) And (3) inputting a scheduling decision optimization result based on dynamic order data optimization into the Hopfield neural network as historical data, and updating the original knowledge of the neural network.
Preferably, a whale optimization algorithm based on simulated annealing solves a finished cigarette scheduling model:
1) All parameters involved in the initializing algorithm, including population size S, maximum iteration number T max Annealing speed delta, and setting search space upper limit B according to delivery point number up Lower limit B lo ；
2) Initializing a population meeting the upper limit and the lower limit of a search space, wherein each individual in the population represents a vehicle scheduling scheme generated according to orders, and the number of the orders is N and O i Represents the ith individual in the population, O ij (j=1, 2, …, N) represents a delivery point of the delivery order j and a transportation vehicle;
step2: calculating fitness value F (x i ) UpdatingGlobal optimal individual location and global extremum.
Step3: calculating the initial temperature of a simulated annealing algorithm, and executing simulated annealing operation on the global optimal whale individual to update the optimal individual position:
wherein ,Zbest The optimal fitness value is the initial particle population;
step4: performing surrounding prey, bubble-net attack and random search operations on all whale individuals in the population;
step5: checking whether the maximum iteration times are reached currently, if so, ending the optimizing and outputting an optimized vehicle scheduling scheme; if not, go back to Step2.
The cloud gateway is used for monitoring the real-time position of the vehicle by the cloud and giving an alarm in real time after the vehicle runs out of range, and the management system further comprises an app terminal which is connected with the data center service through the web gateway.
The invention has the beneficial effects that:
the invention realizes the flow and the intellectualization of the finished product logistics plan dispatch through the system. The system is used for pushing and tracking the whole flow information of the group finished product logistics plan, and simultaneously, the system is used for realizing the auxiliary decision of the whole flow of the planning and dispatching, realizing the intelligence of the decision and promoting the standardization and the flow of the finished product logistics plan dispatching and managing.
Drawings
FIG. 1 is a topology of the present invention;
FIG. 2 is a blueprint of the technical architecture of the present invention;
FIG. 3 is a flowchart of an algorithm of the present invention;
FIG. 4 is a diagram of a development technique architecture of the present invention;
FIG. 5 is a data flow diagram of the present invention;
FIG. 6 is a schematic diagram of a multi-point multi-direction shipping scheduling business process for a logistics center of the present invention.
Detailed Description
In order to facilitate the understanding and implementation of the present invention by those skilled in the art, the technical solution of the present invention will be further described with reference to the accompanying drawings and specific embodiments.
1-2, the multi-point multi-direction delivery scheduling management system for cigarettes can provide real-time tracking of key nodes, including vehicle tracking and order tracking, and realize the reservation function in advance of delivery, including active reservation and passive reservation, and the function of reporting from vehicle to warehouse; the method is characterized in that: the system comprises the following architecture: the system comprises a web gateway, a cloud gateway, a registration service center, a configuration management service, a rights management service, a data center service and a path planning service web application service, wherein the architecture forms a data storage layer, a data logic layer, an algorithm layer, an application layer, a view layer and a support service of the system.
(1) WEB gateway: a WEB end request agent and verification, (2) a cloud gateway: vehicle management cloud and RPA order system data receiving interface, data security and authority verification, (3) service registration center: micro-service architecture service governance, (4) configuration management service: uniformly managing and issuing configuration information of a micro-service system, (5) managing the authority management service: providing user creation and authorization services, (6) data center services: core data storage and integration services, (7) path planning algorithm services: path planning and order processing algorithm service, (8) WEB application service: providing WEB end view and controller logic.
(1) Data storage layer: storage service, algorithm, rights data, (2) data logic layer: management between processing parts of data, (3) algorithm layer: analyzing orders and vehicle data through corresponding algorithms in a system algorithm library, planning a delivery route, (4) an application layer: providing views for orders, vehicles, algorithmic data, providing user interaction logic processing, (5) view layer: providing a data view of good user experience in the form of hypertext in response to user interaction, (6) supporting services: and integrating each independent service in the micro-service system to enable each independent service component to form a complete service system.
In recent years, no matter an enterprise technology architecture or an internet technology architecture, the main direction is not greatly changed, but the specific technology and tools are greatly changed, a large number of old technologies are eliminated or compressed in application space, new technologies are very diversified, no technology takes a remarkable advantage, as shown in fig. 4, the transverse technology is layered more thoroughly, the coupling degree is lower, a front end (client) and a rear end (server) which are completely independent are evolved, and the front end and the rear end can be integrated across a technical route.
As shown in fig. 5, the system data flow structure 1. Order and vehicle information base data is collected from the RPA system. 2. The system receives the data, processes and integrates the data into structured data and stores the structured data in a warehouse. 3. The algorithm service obtains orders and vehicle data analysis operations from the repository. 4. The algorithm service re-dumps the analysis results to the storage service. 5. The business application system obtains the order and the algorithm data to provide the display. 6. And the user issues a vehicle reservation instruction according to the algorithm result. 7. The business system sends the vehicle reservation information to the vehicle dispatch system.
The multi-warehouse multi-direction delivery dispatching business flow of the logistics center is shown in fig. 6, and the auxiliary decision-making system for dispatching the finished product delivery plans of cigarettes distributes the delivery plans of each factory by dispatching the group-level plans, distributes the order plans and dispatching states of the delivery plans of the warehouse of the factory-level plans, and provides auxiliary decision-making and implementation optimization under a dynamic order arrival mode for dispatching operation of a warehouse delivery site by mathematical model calculation. The system transmits the plan information, the inventory information, the warehouse status, the vehicle arrival status and the plan execution status information to the warehouse to assist the warehouse in vehicle scheduling. The carrier synchronizes available vehicle information and dispatching state information to the system, the system sends auxiliary dispatching information to the carrier to assist dispatching, the carrier synchronizes the information to a driver to execute a transportation plan, and meanwhile the driver can share information of reservation results and delivery results with warehouse dispatching and upload the information to the information system to acquire real-time data of the driver to execute the transportation plan.
The core algorithm flow of the system is shown in figure 3, and the algorithm layer comprises a finished cigarette library-scheduling model and a scheduling algorithm design; the finished cigarette warehouse-adjusting model needs to build a minimum warehouse-adjusting cost fitness function, so that the cost for warehouse adjustment is minimum;
then there are:
an nth warehouse representing a kth production shipment point
Library adjustment principle:
if there isWhen k=k ', the two warehouses n and n' can mutually adjust the warehouses; when k is not equal to k ', the warehouse n and n' of the two warehouses cannot be adjusted;
objective function:
according to 0.4756 yuan/ten thousand of transportation unit price and 0.72 yuan/ten thousand of loading and unloading operation cost calculation, 5 months in 2019-5 months in 2020, the internal allocation cost is reduced by more than 100 ten thousand yuan;
the calculation process is as follows:
wherein ,tonnage representing the tonnage of cargo loaded by the A-th vehicle from the nth warehouse at the kth point of production shipment to the other warehouse, a box of 50000 cigarettes, a cigarette weighing 1 gram;
total time of the vehicle A transportation (T A )：
Minimum delivery time cost fitness function (T):
the vehicle A carries the fee in the transportation process:
m A ＝u(wA i ×L ij +∑(wA i -∑wA j )×L jj ') (4)
wherein ：
minimum shipping cost fitness function:
wherein ：
optimization vector:
V-min(M,T) (8)
the objective function is:
constraint conditions:
vehicle loading upper and lower limit constraints, determined by vehicle own performance:
w min ≤wA i ≤w max (10)
the daily shipment upper limit of the shipment point i is determined by a warehouse structure, staff and working time:
the standard transportation limit is determined by the standard transportation rule of the cigarette transportation:
wherein, i ith delivery point, j jth delivery point, omega tj Ton of cigarettes in t of j-th receiving point, a j The 0-1 variable represents whether the A vehicle delivers the j receiving point, A i 0-1 variable indicating whether the A-th vehicle originated at the i-shipment point, ωA j Tonnage, ωA, of cargo carried by the A-th vehicle to the j-th receiving point i Tonnage of cargo loaded by the A-th vehicle from the i-th delivery point, tA i Delivery time of the A-th vehicle at the i-th delivery point, tA j Warehouse-in time of A-th vehicle at j-th receiving pointTA ij The time of the journey of the A-th vehicle from the i-th delivery point to the j-th receiving point, TA jj' The distance time of the A-th vehicle from the j-th receiving point to the j' -th receiving point, V i Indicating the delivery speed of the ith delivery point, L ij Representing a path from an ith delivery point to a jth delivery point, L jj' Representing a path from a j-th receiving point to a j' -th receiving point, M representing a total shipping cost, M A Representing the total cost of vehicle a during transit, v representing the average speed of the vehicle.
The scheduling algorithm is designed: the method comprises the following steps of (1) combining a Hopfield neural network with a simulated annealing algorithm; (2) a combination of Hopfield neural network and Levy flight strategy; (3) Based on the steps, an IHNN mixing algorithm for the tobacco logistics hierarchical scheduling problem is constructed by utilizing the mixing strategy;
the combination of the (1) Hopfield neural network and the simulated annealing algorithm adopts the following detailed method; (1) setting an initial state x i ；
(2) Will x i Setting as a starting point, substituting the starting point into the Hopfield neural network to perform iterative operation,e { x } of the network at this time is calculated i }；
(3) In state x i Randomly generated disturbances Δx in the vicinity i I.e. the state becomes x at this time i +Δx i Then the result is brought into the Hopfield neural network to carry out iterative operation, and the minimum value E { x } is output when the network is stable i +Δx i }；
(4) If it isThen->Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
(5) if E { x i +Δx i }＞E{x i If yes, then accept state E { x } by determining if the Metropolis criterion is met i +Δx i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge; if not meet E { x } i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
the combination of the (2) Hopfield neural network and the Levy flight strategy adopts the following detailed method: (1) setting an initial state x i ；
(2) Will x i Setting as a starting point, substituting the starting point into the Hopfield neural network to perform iterative operation, and calculating E { x ] of the network at the moment i }；
(3) For state x i Using Levy flight strategy movement based on flight probabilityStep size, i.e. the state becomes +.>Inputting into Hopfield neural network for iterative operation, and outputting minimum value +.>
(4) If it isThen->Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
(5) if it isThen E { x } i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
3) Based on the steps, the IHNN mixing algorithm for constructing the tobacco logistics hierarchical scheduling problem by utilizing the mixing strategy comprises the following detailed steps: (1) Constructing a Hopfield neural network, and inputting the collected historical carpooling order data into the Hopfield neural network to train the neural network;
(2) Randomly selecting a starting point x from a trained Hopfield neural network 0 I.e. the initial hierarchical scheduling scheme, f (x) is calculated according to the tobacco logistics hierarchical scheduling double-layer optimization objective function 0 ) Let k=0;
(3) The scheduling scheme and order dynamic data are input into the Hopfield neural network, and the search is performed by using a gradient descent method (assuming that the starting point of the search is x (k) ) Find the local minimum point x of f (x) (k)* . For a scheduling scheme of an order which needs to be subjected to scheduling but does not meet the constraint of a scheduling time window, marking the scheduling scheme in an algorithm, and performing independent hierarchical optimization by using a Hopfield neural network;
(4) From x (k)* Initially, the algorithm is partially and deeply explored, and the simulated annealing algorithm is operated until a new point x is found (k+1) This is to satisfy f (x (k+1) )-f(x (k)* )≤-δ k Of delta, where delta k Is a positive number;
(5) Updating x (k)* Let x (k)* ＝x (k+1) . Global algorithmOptimizing, running Levy flight strategy, updating to obtain a new point x (k+1) This point satisfies f (x (k+1) )-f(x (k)* )≤-δ k ；
(6) Let k=k+1, return to step (2) until the algorithm converges;
(7) And (3) inputting a scheduling decision optimization result based on dynamic order data optimization into the Hopfield neural network as historical data, and updating the original knowledge of the neural network.
Preferably, a whale optimization algorithm based on simulated annealing solves a finished cigarette scheduling model:
1) All parameters involved in the initializing algorithm, including population size S, maximum iteration number T max Annealing speed delta, and setting search space upper limit B according to delivery point number up Lower limit B lo ；
2) Initializing a population meeting the upper limit and the lower limit of a search space, wherein each individual in the population represents a vehicle scheduling scheme generated according to orders, and the number of the orders is N and O i Represents the ith individual in the population, O ij (j=1, 2, …, N) represents a delivery point of the delivery order j and a transportation vehicle;
step2: calculating fitness value F (x i ) And updating the global optimal individual position and the global extremum.
Step3: calculating the initial temperature of a simulated annealing algorithm, and executing simulated annealing operation on the global optimal whale individual to update the optimal individual position:
wherein ,Zbest The optimal fitness value is the initial particle population;
step4: performing surrounding prey, bubble-net attack and random search operations on all whale individuals in the population;
step5: checking whether the maximum iteration times are reached currently, if so, ending the optimizing and outputting an optimized vehicle scheduling scheme; if not, go back to Step2.
1. The system acquires order data and real-time inventory data of the logistics comprehensive pipe platform in real time through the RPA. 2. The scheduling algorithm result of the existing order is calculated in real time through the algorithm layer of the system. 3. And the carrier matches the dispatching result with corresponding carrier company, vehicle model, license plate number and driver vehicle basic information 4 according to the dispatching of the system. 5. The scheduling management platform acquires the planned scheduling-transportation scheduling data through the RPA. 6. The dispatching management platform transmits the transportation dispatching data and dispatching algorithm result data to the mobile phone WeChat applet, ensures the consistency of the data, and supports the data for reservation of a delivery driver, confirmation of arrival at a warehouse and information push. 7. And the operations of reserving goods, confirming the goods to a warehouse, pushing information and the like are carried out through a mobile phone WeChat applet, so that the standardization and the service quality of delivery of a delivery driver are ensured.
The foregoing has shown and described the basic principles, principal features and advantages of the invention. It will be understood by those skilled in the art that the present invention is not limited to the above-described embodiments, and that the above-described embodiments and descriptions are only preferred embodiments of the present invention, and are not intended to limit the invention, and that various changes and modifications may be made therein without departing from the spirit and scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (3)

1. The multi-point multi-direction delivery scheduling management system for the cigarettes can provide real-time tracking of key nodes, comprises vehicle tracking and order tracing, and realizes the functions of pre-ordering and pre-ordering of the cigarettes, including active reservation and passive reservation, and the functions of reporting the cigarettes to a warehouse; the method is characterized in that: the system comprises the following architecture: the system comprises a web gateway, a cloud gateway, a registration service center, a configuration management service, a rights management service, a data center service and a path planning service web application service, wherein the architecture forms a data storage layer, a data logic layer, an algorithm layer, an application layer, a view layer and a support service of the system;
(1) Data storage layer: storage service, algorithm, rights data, (2) data logic layer: management between processing parts of data, (3) algorithm layer: analyzing orders and vehicle data through corresponding algorithms in a system algorithm library, planning a delivery route, (4) an application layer: providing views for orders, vehicles, algorithmic data, providing user interaction logic processing, (5) view layer: providing a data view of good user experience in the form of hypertext in response to user interaction, (6) supporting services: integrating each independent service in the micro-service system to enable each independent service component to form a complete service system;
(1) WEB gateway: a WEB end request agent and verification, (2) a cloud gateway: vehicle management cloud and RPA order system data receiving interface, data security and authority verification, (3) service registration center: micro-service architecture service governance, (4) configuration management service: uniformly managing and issuing configuration information of a micro-service system, (5) managing the authority management service: providing user creation and authorization services, (6) data center services: core data storage and integration services, (7) path planning algorithm services: path planning and order processing algorithm service, (8) WEB application service: providing a WEB end view and controller logic;
the cloud gateway is used for monitoring the real-time position of the vehicle by the cloud and giving an alarm in real time after the vehicle runs out of range, and the management system further comprises an app terminal which is connected with the data center service through the web gateway;
the algorithm layer comprises a finished cigarette library adjustment model and a scheduling algorithm design; the finished cigarette warehouse-adjusting model needs to build a minimum warehouse-adjusting cost fitness function, so that the cost for warehouse adjustment is minimum;
then there are:
an nth warehouse representing a kth production shipment point; />Tonnage representing the tonnage of cargo loaded by the a-th vehicle from the nth warehouse at the kth production shipment point to the other warehouse;
library adjustment principle:
if there isWhen k=k ', the two warehouses n and n' mutually adjust the warehouses; when k is not equal to k ', the warehouse n and n' of the two warehouses cannot be adjusted;
objective function:
according to 0.4756 yuan/ten thousand of transportation unit price and 0.72 yuan/ten thousand of loading and unloading operation cost calculation, 5 months in 2019-5 months in 2020, the internal allocation cost is reduced by more than 100 ten thousand yuan;
the calculation process is as follows:
wherein ,tonnage representing the tonnage of cargo loaded by the A-th vehicle from the nth warehouse at the kth point of production shipment to the other warehouse, a box of 50000 cigarettes, a cigarette weighing 1 gram;
total time T of vehicle a transport A ：
Minimum delivery time cost fitness function T:
the vehicle A carries the fee in the transportation process:
m A ＝u(wA i ×L ij +∑(wA i -∑wA j )×L jj' ) (4)
wherein ：
minimum shipping cost fitness function:
wherein ：
optimization vector:
V-min(M,T) (8)
the objective function is:
constraint conditions:
vehicle loading upper and lower limit constraints, determined by vehicle own performance:
w min ≤wA i ≤w max (10)
the daily shipment upper limit of the shipment point i is determined by a warehouse structure, staff and working time:
the standard transportation limit is determined by the standard transportation rule of the cigarette transportation:
wherein, i ith delivery point, j jth delivery point, omega tj Ton of cigarettes in t of j-th receiving point, a j The variable =0 or 1 indicates whether the a-th vehicle is delivered to the j receiving point, a i The variable =0 or 1 indicates whether the a-th vehicle originated at the i-shipment point, ωa j Tonnage, ωA, of cargo carried by the A-th vehicle to the j-th receiving point i Tonnage of cargo loaded by the A-th vehicle from the i-th delivery point, tA i Delivery time of the A-th vehicle at the i-th delivery point, tA j Warehouse-in time of A-th vehicle at j-th receiving pointTA ij The time of the journey of the A-th vehicle from the i-th delivery point to the j-th receiving point, TA jj' The distance time of the A-th vehicle from the j-th receiving point to the j' -th receiving point, V i Indicating the delivery speed of the ith delivery point, L ij Representing a path from an ith delivery point to a jth delivery point, L jj' Representing a path from a j-th receiving point to a j' -th receiving point, M representing a total shipping cost, M A Representing the total cost of vehicle a during transit, v representing the average speed of the vehicle.
2. The multi-point multi-directional shipment scheduling management system for cigarettes according to claim 1: the method is characterized in that: the scheduling algorithm is designed: the method comprises the following steps of (1) combining a Hopfield neural network with a simulated annealing algorithm; (2) a combination of Hopfield neural network and Levy flight strategy; (3) Based on the steps, an IHNN mixing algorithm for solving the problem of tobacco logistics hierarchical scheduling is constructed by utilizing a mixing strategy;
the combination of the (1) Hopfield neural network and the simulated annealing algorithm adopts the following detailed method;
(1) setting an initial state x i ；
(2) Will x i Setting as a starting point, substituting the starting point into the Hopfield neural network to perform iterative operation,e { x } of the network at this time is calculated i }；
(3) In state x i Randomly generated disturbances Δx in the vicinity i I.e. the state becomes x at this time i +Δx i Then the result is brought into the Hopfield neural network to carry out iterative operation, and the minimum value E { x } is output when the network is stable i +Δx i }；
(4) If it isThen->Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
(5) if E { x i +Δx i }＞E{x i If yes, then accept state E { x } by determining if the Metropolis criterion is met i +Δx i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge; if not meet E { x } i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
the combination of the (2) Hopfield neural network and the Levy flight strategy adopts the following detailed method: (1) setting an initial state x i ；
(2) Will x i Setting as a starting point, substituting the starting point into the Hopfield neural network to perform iterative operation, and calculating E { x ] of the network at the moment i }；
(3) For state x i Using Levy flight strategy movement based on flight probabilityStep size, i.e. the state becomes +.>Inputting into Hopfield neural network for iterative operation, and outputting minimum value +.>
(4) If it isThen->Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
(5) if it isThen E { x } i }＝E{x i Outputting a result if the algorithm converges, and returning to the step (3) if the algorithm does not converge;
3) Based on the steps, the IHNN mixing algorithm for constructing the tobacco logistics hierarchical scheduling problem by utilizing the mixing strategy comprises the following detailed steps: (1) Constructing a Hopfield neural network, and inputting the collected historical carpooling order data into the Hopfield neural network to train the neural network;
(2) Randomly selecting a starting point x from a trained Hopfield neural network 0 I.e. the initial hierarchical scheduling scheme, f (x) is calculated according to the tobacco logistics hierarchical scheduling double-layer optimization objective function 0 ) Let k=0;
(3) The scheduling scheme and order dynamic data are input into the Hopfield neural network, and the search is performed by using a gradient descent method (assuming that the starting point of the search is x (k) ) Find the local minimum point x of f (x) (k)* The method comprises the steps of carrying out a first treatment on the surface of the For a scheduling scheme of an order which needs to be subjected to scheduling but does not meet the constraint of a scheduling time window, marking the scheduling scheme in an algorithm, and performing independent hierarchical optimization by using a Hopfield neural network;
(4) From x (k)* Initially, the algorithm is partially and deeply explored, and the simulated annealing algorithm is operated until a new point x is found (k +1) This is to satisfy f (x (k+1) )-f(x (k)* )≤-δ k Of delta, where delta k Is a positive number;
(5) Updating x (k)* Let x (k)* ＝x (k+1) The method comprises the steps of carrying out a first treatment on the surface of the Global optimization is carried out on the algorithm, and a Levy flight strategy is operatedUpdating to obtain a new point x (k+1) This point satisfies f (x (k+1) )-f(x (k)* )≤-δ k ；
(6) Let k=k+1, return to step (2) until the algorithm converges;
(7) And (3) inputting a scheduling decision optimization result based on dynamic order data optimization into the Hopfield neural network as historical data, and updating the original knowledge of the neural network.
3. The multi-point multi-directional shipment scheduling management system for cigarettes according to claim 1: the method is characterized in that: solving a finished product cigarette scheduling model by using a whale optimization algorithm based on simulated annealing:
1) All parameters involved in the initializing algorithm, including population size S, maximum iteration number T max Annealing speed delta, and setting search space upper limit B according to delivery point number up Lower limit B lo ；
2) Initializing a population meeting the upper limit and the lower limit of a search space, wherein each individual in the population represents a vehicle scheduling scheme generated according to orders, and the number of the orders is N and O i Represents the ith individual in the population, O ij (j=1, 2, …, N) represents a delivery point of the delivery order j and a transportation vehicle;
step2: calculating fitness value F (x i ) Updating the global optimal individual position and the global extremum;
step3: calculating the initial temperature of a simulated annealing algorithm, and executing simulated annealing operation on the global optimal whale individual to update the optimal individual position:
wherein ,Zbest The optimal fitness value is the initial particle population;
step4: performing surrounding prey, bubble-net attack and random search operations on all whale individuals in the population;
step5: checking whether the maximum iteration times are reached currently, if so, ending the optimizing and outputting an optimized vehicle scheduling scheme; if not, go back to Step2.
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