CN112613700B - A cigarette multi-storage point multi-directional delivery scheduling and management system - Google Patents

A cigarette multi-storage point multi-directional delivery scheduling and management system 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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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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Hongyunhonghe 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)

1.一种卷烟多库点多方向发货调度管理系统，所述的卷烟多库点多方向发货调度管理系统能够提供关键节点实时跟踪，包括车辆跟踪、订单追溯，实现到货提前预约功能包括主动预约和被动预约，车辆到库报到功能；其特征在于：所述的系统包括如下架构：web网关、云网关、注册服务中心、配置管理服务、权限管理服务、数据中心服务、路径规划服务web应用服务，所述的架构组成了该系统的数据存储层、数据逻辑层、算法层、应用层、视图层、支持服务；1. A cigarette multi-storage point multi-directional delivery scheduling and management system. The cigarette multi-storage point multi-directional delivery scheduling and management system can provide real-time tracking of key nodes, including vehicle tracking, order tracing, and realize the function of advance reservation of arrival. Including active reservation and passive reservation, vehicle arrival and check-in functions; characterized in that: the system includes the following architecture: web gateway, cloud gateway, registration service center, configuration management service, authority management service, data center service, path planning service Web application service, the described architecture consists of the data storage layer, data logic layer, algorithm layer, application layer, view layer, and support services of the system; (1)数据存储层：存储业务、算法、权限数据，(2)数据逻辑层：处理各部分数据之间的管理，(3)算法层：通过系统算法库中相应的算法分析订单和车辆数据，规划出配送路线，(4)应用层：为订单、车辆、算法数据提供视图，提供用户交互逻辑处理，(5)视图层：以超文本的形式提供用户体验良好的数据视图，响应用户交互，(6)支持服务：整合微服务体系中各个独立的服务，使各个独立的服务部件构成完整的业务系统；(1) Data storage layer: stores business, algorithm, and permission data; (2) Data logic layer: handles the management of various parts of data; (3) Algorithm layer: analyzes order and vehicle data through corresponding algorithms in the system algorithm library , plan the delivery route, (4) Application layer: Provide views for orders, vehicles, and algorithm data, and provide user interaction logic processing, (5) View layer: Provide data views with good user experience in the form of hypertext, and respond to user interactions , (6) Support services: Integrate each independent service in the microservice system so that each independent service component constitutes a complete business system; (1)WEB网关：WEB端请求代理与校验，(2)云网关：车辆管理云与RPA订单系统数据接收接口，数据安全与权限校验，(3)服务注册中心：微服务体系服务治理，(4)配置管理服务：微服务体系配置信息统一管理与下发，(5)权限管理服务：提供用户创建与授权服务，(6)数据中心服务：核心数据存储与整合服务，(7)路径规划算法服务：路径规划与订单处理算法服务，(8)WEB应用服务：提供WEB端视图与控制器逻辑；(1) WEB gateway: WEB side request proxy and verification, (2) Cloud gateway: vehicle management cloud and RPA order system data receiving interface, data security and permission verification, (3) Service registration center: microservice system service governance , (4) Configuration management service: unified management and distribution of configuration information of the microservice system, (5) Permission management service: providing user creation and authorization services, (6) Data center service: core data storage and integration service, (7) Path planning algorithm service: path planning and order processing algorithm service, (8) WEB application service: providing WEB side view and controller logic; 所述的云网关用于云监控车辆的实时位置，并在车辆运行超出范围后进行实时报警，所述的管理系统还包括app终端，app终端通过web网关与数据中心服务连接；The cloud gateway is used to cloud monitor the real-time location of the vehicle and provide real-time alarm after the vehicle runs out of range. The management system also includes an app terminal, and the app terminal is connected to the data center service through a web gateway; 所述的算法层包括成品卷烟调库模型，调度算法设计；所述的成品卷烟调库模型需要搭建最小调库成本适应度函数，使调库所花费费用最少；The algorithm layer includes a finished cigarette inventory adjustment model and a scheduling algorithm design; the finished cigarette inventory adjustment model needs to build a minimum adjustment cost fitness function to minimize the cost of inventory adjustment; 则有：Then there are: 表示第k个生产发货点的第n个仓库；/>表示第A辆车从第k个生产发货点的第n座仓库装载的货物吨数调往其他仓库的吨数； Represents the n-th warehouse of the k-th production and delivery point;/> Indicates the tonnage of cargo loaded by the A-th vehicle from the n-th warehouse at the k-th production and delivery point to other warehouses; 调库原则：Principles of library adjustment: 若有两座仓库，当k＝k'时，则两座仓库n、n'相互调库；当k≠k'时，则两座仓库n、n'之间不可以调库；If so For two warehouses, when k=k', the two warehouses n and n' can adjust the warehouses to each other; when k≠k', the two warehouses n and n' cannot adjust the warehouses between them; 目标函数：Objective function: 按照物流中心成品仓储科库间内部调拨运输单价0.4756元/万支，装卸作业费用单价0.72元/万支计算，2019年5月-2020年5月，共降低内部调拨费用超过100万元；Calculated based on the unit price of internal allocation transportation between finished product warehousing departments in the logistics center of 0.4756 yuan/10,000 pieces, and the unit price of loading and unloading operations costs 0.72 yuan/10,000 pieces, from May 2019 to May 2020, the internal allocation cost was reduced by more than 1 million yuan; 计算过程如下：The calculation process is as follows: 其中，表示第A辆车从第k个生产发货点的第n座仓库装载的货物吨数调往其他仓库的吨数，一箱有50000支香烟，一支香烟重1克；in, Indicates the tonnage of cargo loaded by vehicle A from the n-th warehouse at the k-th production and delivery point to other warehouses. There are 50,000 cigarettes in one box, and one cigarette weighs 1 gram; 车辆A运送总时间TA：Total delivery time T A of vehicle A: 最小配送时间成本适应度函数T：Minimum delivery time cost fitness function T: 车辆A在运输过程中运费：Vehicle A’s freight during transportation: mA＝u(wAi×Lij+∑(wAi-∑wAj)×Ljj') (4)m A =u(wA i ×L ij +∑(wA i -∑wA j )×L jj' ) (4) 其中：in: 最小运送费用适应度函数：Minimum shipping cost fitness function: 其中：in: 优化向量：Optimization vector: V-min(M,T) (8)V-min(M,T) (8) 目标函数为：The objective function is: 约束条件：Restrictions: 车辆装载上下限约束，由车辆自身性能所决定：The upper and lower limits of vehicle loading are determined by the vehicle's own performance: wmin≤wAi≤wmax (10)w min ≤ wA i ≤ w max (10) 发货点i的日出货上限，由仓库结构、工作人员、工作时间所决定：The daily shipping limit of shipping point i is determined by the warehouse structure, staff, and working hours: 准运证限制，由卷烟运输实行法定准运证制度所决定：The restrictions on transportation permits are determined by the statutory transportation permit system for cigarette transportation: 其中、i第i个发货点，j第j个收货点，ωtj第j个收货点的第t中香烟的吨数，Aj＝0或1变量表示第A辆车是否送j收货点，Ai＝0或1变量表示第A辆车是否在i发货点始发，ωAj第A辆车运往第j个收货点的货物吨数，ωAi第A辆车从第i个发货点装载的货物吨数，tAi第A辆车在第i个发货点的出货时间，tAj第A辆车在第j个收货点的入库时间TAij第A辆车从第i个发货点到第j个收货点的路程时间，TAjj'第A辆车从第j个收货点到第j’个收货点的路程时间，Vi表示第i个发货点的出货速度，Lij表示从第i个发货点到第j个收货点的路径，Ljj'表示从第j个收货点到第j’个收货点的路径，M表示运送总费用，mA表示车辆A在运送过程中的总费用，v表示车辆平均速度。Among them, i is the i-th shipping point, j is the j-th receiving point, ω tj is the number of tons of cigarettes in the tth j-th receiving point, A j = 0 or 1 variable indicates whether the A-th vehicle delivers j Receiving point, A i = 0 or 1 variable indicates whether the A-th vehicle originates from the i-th delivery point, ωA j The number of cargo tons transported by the A-th vehicle to the j-th receiving point, ωA i The A-th vehicle originates from The tonnage of goods loaded at the i-th shipping point, tA i The shipping time of the A-th vehicle at the i-th shipping point, tA j The warehousing time of the A-th vehicle at the j-th receiving point TA ij The journey time of the A-th vehicle from the i-th shipping point to the j-th receiving point, TA jj' The journey time of the A-th vehicle from the j-th receiving point to the j'-th receiving point, Vi represents the shipping speed of the i-th shipping point, L ij represents the path from the i-th shipping point to the j-th receiving point, and L jj' represents the path from the j-th receiving point to the j'-th The path to the receiving point, M represents the total transportation cost, m A represents the total cost of vehicle A during the transportation process, and v represents the average speed of the vehicle. 2.根据权利要求1所述的一种卷烟多库点多方向发货调度管理系统：其特征在于：所述的调度算法设计：采用以下步骤(1)Hopfield神经网络与模拟退火算法的结合；(2)Hopfield神经网络与Levy飞行策略的结合；(3)基于上述步骤，利用混合策略，构建烟草物流分级调度问题的IHNN混合算法；2. A cigarette multi-storage point multi-directional delivery scheduling and management system according to claim 1: characterized in that: the scheduling algorithm design adopts the following steps (1) the combination of Hopfield neural network and simulated annealing algorithm; (2) The combination of Hopfield neural network and Levy flight strategy; (3) Based on the above steps, using the hybrid strategy, construct an IHNN hybrid algorithm for the tobacco logistics hierarchical scheduling problem; 所述的(1)Hopfield神经网络与模拟退火算法的结合采用以下详细方法；The combination of (1) Hopfield neural network and simulated annealing algorithm adopts the following detailed methods; ①设置初始状态xi；①Set the initial state x i ; ②将xi设置为起点，代入到Hopfield神经网络中进行迭代运算，计算此时的网络的E{xi}；② Set x i as the starting point, substitute it into the Hopfield neural network for iterative operation, and calculate the E{x i } of the network at this time; ③在状态xi附近随机产生扰动Δxi，即此时状态变为xi+Δxi，再带入到Hopfield神经网络中进行迭代运算，此时网络稳定时输出极小值E{xi+Δxi}；③ Randomly generate disturbance Δx i near the state x i , that is, the state becomes x i +Δx i at this time, and then bring it into the Hopfield neural network for iterative operation. At this time, when the network is stable, it outputs the minimum value E{x i + Δx i }; ④若则/>若算法收敛则输出结果，若算法不收敛返回步骤③；④If then/> If the algorithm converges, output the result. If the algorithm does not converge, return to step ③; ⑤若E{xi+Δxi}＞E{xi}，则釆用判断是否满足Metropolis准则，若满足Metropolis准则，则接受状态E{xi+Δxi}＝E{xi}，若算法收敛则输出结果，若算法不收敛返回步骤③；若不满足则E{xi}＝E{xi}，若算法收敛则输出结果，若算法不收敛返回步骤③；⑤If E{x i +Δx i }>E{x i }, then use the method to determine whether the Metropolis criterion is satisfied. If the Metropolis criterion is satisfied, then accept the state E{x i +Δx i }=E{x i }, if If the algorithm converges, the result will be output. If the algorithm does not converge, return to step ③; if it is not satisfied, E{x i } = E{x i }. If the algorithm converges, the result will be output. If the algorithm does not converge, return to step ③; 所述(2)Hopfield神经网络与Levy飞行策略的结合采用以下详细方法：①设置初始状态xi；The combination of (2) Hopfield neural network and Levy flight strategy adopts the following detailed methods: ① Set the initial state x i ; ②将xi设置为起点，代入到Hopfield神经网络中进行迭代运算，计算此时的网络的E{xi}；② Set x i as the starting point, substitute it into the Hopfield neural network for iterative operation, and calculate the E{x i } of the network at this time; ③对状态xi依飞行概率利用Levy飞行策略移动步长，即此时状态变为/>输入到Hopfield神经网络中进行迭代运算，此时网络稳定时输出极小值/> ③Use the Levy flight strategy to move the state x i according to the flight probability Step size, that is, the status changes to/> Input to the Hopfield neural network for iterative operation. At this time, the minimum value is output when the network is stable/> ④若则/>若算法收敛则输出结果，若算法不收敛返回步骤③；④If then/> If the algorithm converges, output the result. If the algorithm does not converge, return to step ③; ⑤若则E{xi}＝E{xi}，若算法收敛则输出结果，若算法不收敛返回步骤③；⑤If Then E{x i }=E{x i }. If the algorithm converges, the result will be output. If the algorithm does not converge, return to step ③; 3)基于上述步骤，利用混合策略，构建烟草物流分级调度问题的IHNN混合算法详细步骤如下：(1)构建Hopfield神经网络，将收集的历史拼车订单数据输入进Hopfield神经网络训练神经网络；3) Based on the above steps, using a hybrid strategy, the detailed steps of constructing an IHNN hybrid algorithm for the tobacco logistics hierarchical scheduling problem are as follows: (1) Construct a Hopfield neural network, and input the collected historical ride-sharing order data into the Hopfield neural network to train the neural network; (2)在训练好的Hopfield神经网络中随机选取起始点x0，即初始的分级调度方案，根据烟草物流分级调度双层优化目标函数计算f(x0)，令k＝0；(2) Randomly select the starting point x 0 in the trained Hopfield neural network, which is the initial hierarchical scheduling plan, and calculate f(x 0 ) according to the two-layer optimization objective function of hierarchical scheduling in tobacco logistics, let k=0; (3)将调度方案和订单动态数据输入Hopfield神经网络，利用梯度下降法进行搜索(假设本次搜索的起始点为x(k))，找出f(x)的局部极小点x(k)*；对于订单需要调库但又不满足调库时间窗约束的调度方案，将其在算法中标记，使用Hopfield神经网络进行单独分层优化；(3) Input the scheduling plan and order dynamic data into the Hopfield neural network, use the gradient descent method to search (assuming that the starting point of this search is x (k) ), and find the local minimum point x ( k ) of f(x) )* ; For scheduling solutions that require inventory adjustment for orders but do not meet the inventory adjustment time window constraints, mark them in the algorithm and use Hopfield neural network for separate hierarchical optimization; (4)从x(k)*开始，进行算法局部深入探索，运行模拟退火算法直到找到一个新的点x(k +1)，这是满足f(x(k+1))-f(x(k)*)≤-δk的，其中δk是某个正数；(4) Starting from x (k)* , perform local in-depth exploration of the algorithm, and run the simulated annealing algorithm until a new point x (k +1) is found, which satisfies f(x (k+1) )-f(x (k)* )≤-δ k , where δ k is a positive number; (5)更新x(k)*，令x(k)*＝x(k+1)；对算法进行全局寻优，运行Levy飞行策略，更新得到一个新的点x(k+1)，这个点满足f(x(k+1))-f(x(k)*)≤-δk；(5) Update x (k)* , let x (k)* = x (k+1) ; perform global optimization of the algorithm, run the Levy flight strategy, and update to obtain a new point x (k+1) , this The point satisfies f(x (k+1) )-f(x (k)* )≤-δ k ; (6)令k＝k+1，返回步骤(2)直到算法收敛；(6) Let k=k+1 and return to step (2) until the algorithm converges; (7)将此次基于动态订单数据优化的调度决策优化结果作为历史数据输入进Hopfield神经网络，更新神经网络的原始知识。(7) Input the scheduling decision optimization results based on dynamic order data optimization as historical data into the Hopfield neural network to update the original knowledge of the neural network. 3.根据权利要求1所述的一种卷烟多库点多方向发货调度管理系统：其特征在于：基于模拟退火的鲸鱼优化算法对成品卷烟调度模型求解：3. A cigarette multi-storage point multi-directional delivery scheduling management system according to claim 1: characterized in that: the whale optimization algorithm based on simulated annealing solves the finished cigarette scheduling model: 1)初始化算法涉及到的所有参数，包括种群规模S，最大迭代次数Tmax，退火速度δ，以及按出货点编号设置搜索空间上限Bup、下限Blo；1) All parameters involved in the initialization algorithm, including population size S, maximum number of iterations T max , annealing speed δ, and setting the upper limit B up and lower limit B lo of the search space according to the shipping point number; 2)初始化满足搜索空间上下限的种群，种群中每个个体代表根据订单所产生的一种车辆调度方案，设订单个数为N，Oi表示种群中第i个个体，则Oij(j＝1，2，…，N)表示运送订单j的发货点以及运输车辆；2) Initialize a population that satisfies the upper and lower limits of the search space. Each individual in the population represents a vehicle scheduling plan generated according to the order. Suppose the number of orders is N, O i represents the i-th individual in the population, then O ij (j =1,2,…,N) represents the shipping point and transportation vehicle of delivery order j; Step2：计算群体中每个个体的适应度值F(xi)，更新全局最优个体位置和全局极值；Step2: Calculate the fitness value F( xi ) of each individual in the group, and update the global optimal individual position and global extreme value; Step3：计算模拟退火算法的初始温度，对全局最优鲸鱼个体执行模拟退火操作更新最优个体位置：Step3: Calculate the initial temperature of the simulated annealing algorithm, and perform the simulated annealing operation on the global optimal whale individual to update the optimal individual position: 其中，Zbest为初始粒子种群中，最优的适应度值；Among them, Z best is the optimal fitness value in the initial particle population; Step4：对种群中所有鲸鱼个体执行包围猎物、Bubble-net攻击、随机搜索操作；Step 4: Perform surrounding prey, Bubble-net attack, and random search operations on all individual whales in the population; Step5：检查当前是否达到最大迭代次数，如果达到，结束寻优，输出优化后的车辆调度方案；如果未达到，返回Step2。Step5: Check whether the maximum number of iterations is currently reached. If it is reached, end the optimization and output the optimized vehicle scheduling plan; if it is not reached, return to Step2.
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