CN116228069A - Inventory path planning method and device, electronic equipment and storage medium - Google Patents

Inventory path planning method and device, electronic equipment and storage medium Download PDF

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CN116228069A
CN116228069A CN202310139928.7A CN202310139928A CN116228069A CN 116228069 A CN116228069 A CN 116228069A CN 202310139928 A CN202310139928 A CN 202310139928A CN 116228069 A CN116228069 A CN 116228069A
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inventory
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曾银莲
张莲民
刘书婷
刘天泽
彭洋洋
张海伦
陈勇全
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Shenzhen Technology University
Shenzhen Research Institute of Big Data SRIBD
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Abstract

The application provides an inventory path planning method and device, electronic equipment and a storage medium, which belong to the technical field of transportation optimization, and are characterized in that inventory level assessment and backorder level assessment are respectively carried out according to initial inventory data, backorder data and product demand data to obtain intermediate inventory data and backorder data, storage cost is obtained according to unit storage cost and intermediate inventory data, risk data is calculated, path planning is carried out on a path from a supply node to a demand node according to the unit backorder cost and backorder data to obtain an initial transportation path, transportation cost for product transportation according to the initial transportation path is calculated, target cost is obtained according to the storage cost, backorder cost and transportation cost, strategy adjustment is carried out on an initial backorder strategy according to the target cost and risk data to obtain a target inventory planning result, path adjustment is carried out on the initial transportation path according to the target cost to obtain a path planning result, and accuracy of inventory path planning is improved.

Description

Inventory path planning method and device, electronic equipment and storage medium
Technical Field
The present disclosure relates to the field of transportation optimization technologies, and in particular, to a method and apparatus for planning an inventory path, an electronic device, and a storage medium.
Background
In the related art, the total cost is taken as an objective function, inventory and paths are planned through minimization of the objective function, but the space with compressible cost is limited, so that the planning mode through the compression cost cannot be close to a real scene, an inventory path planning result is wrong, optimal inventory and paths cannot be obtained, and the accuracy of inventory path planning is reduced.
Disclosure of Invention
The embodiment of the application mainly aims to provide an inventory path planning method and device, electronic equipment and storage medium, and aims to improve the accuracy of inventory path planning.
To achieve the above object, a first aspect of an embodiment of the present application proposes an inventory path planning method, including:
dividing a preset planning time period into a plurality of periods; the preset planning time period comprises a starting time;
determining product demand data of demand nodes in the period according to a preset demand set;
acquiring an initial replenishment strategy of a supply node to the demand node and initial inventory data of the demand node at the starting time;
Obtaining replenishment data of the supply node to the demand node according to the initial replenishment strategy and the initial inventory data;
performing inventory level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain intermediate inventory data of the demand node in the period, and performing backorder level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain backorder data of the demand node in the period;
obtaining the storage cost of the demand node according to the preset unit storage cost and the intermediate inventory data, and calculating risk data of the intermediate inventory data which are not in a preset inventory level interval; obtaining the stock-out cost of the demand node according to the preset unit stock-out cost and the stock-out data; carrying out path planning on the path between the supply node and the demand node to obtain an initial transportation path; calculating the transportation cost of product transportation according to the initial transportation path;
obtaining a target cost from the storage cost, the stock out cost, and the transportation cost;
and carrying out strategy adjustment on the initial replenishment strategy according to the target cost and the risk data to obtain a target inventory planning result, and carrying out path adjustment on the initial transportation path according to the target cost to obtain a path planning result.
In some embodiments, the obtaining replenishment data of the supply node to the demand node according to the initial replenishment policy and the initial inventory data includes:
obtaining the maximum stock capacity of the demand node;
performing supply level assessment according to the initial replenishment strategy and the maximum inventory capacity to obtain supply data of the demand node;
and carrying out replenishment level assessment according to the supply data and the initial inventory data to obtain replenishment data.
In some embodiments, the performing inventory level assessment according to the initial inventory data, the replenishment data, and the product demand data to obtain intermediate inventory data of the demand node in the period, and performing backorder level assessment according to the initial inventory data, the replenishment data, and the product demand data to obtain backorder data of the demand node in the period, includes:
carrying out total product assessment according to the initial inventory data and the replenishment data to obtain total product data of the demand node in the period;
performing product allowance assessment according to the total product data and the product demand data to obtain the intermediate inventory data;
And carrying out product backorder assessment according to the total product data and the product demand data to obtain the backorder data.
In some embodiments, the preset inventory level interval includes first endpoint data and second endpoint data, the first endpoint data being smaller than the second endpoint data, the calculating risk data for the intermediate inventory data not in the preset inventory level interval includes:
performing difference calculation on the intermediate inventory data and the first end point data to obtain first difference data; performing difference calculation on the intermediate inventory data and the second endpoint data to obtain second difference data; obtaining the risk data according to the first difference data and the second difference data;
or alternatively, the process may be performed,
acquiring risk parameters, wherein the risk parameters are used for representing risks that the product demand data are not met; performing risk assessment according to the risk parameters, the first endpoint data and the intermediate inventory data to obtain a first risk; performing risk assessment according to the risk parameters, the second endpoint data and the intermediate inventory data to obtain a second risk; and obtaining the risk data according to the first risk and the second risk.
In some embodiments, the performing path planning on the path between the supply node and the demand node to obtain an initial transport path includes:
acquiring a first distance, a second distance and a third distance, wherein the first distance is used for representing the distance between the supply node and the demand node, the second distance is used for representing the distance between the supply node and another demand node, and the third distance is used for representing the distance between the demand node and the another demand node;
performing distance calculation according to the first distance, the second distance and the third distance to obtain saving mileage data between the demand node and the other demand node;
planning a path between the demand node and the other demand node according to the mileage saving data to obtain an initial path;
and constructing a route according to the initial route to obtain the initial transportation route.
In some embodiments, the performing policy adjustment on the initial replenishment policy according to the target cost and the risk data to obtain a target inventory planning result, and performing path adjustment on the initial transportation path according to the target cost to obtain a path planning result, including:
Screening the initial replenishment strategy according to the target cost and a preset cost threshold to obtain an intermediate replenishment strategy, and screening the initial transportation path according to the target cost and the preset cost threshold to obtain the path planning result;
selecting the intermediate replenishment strategy with the smallest risk data as a target replenishment strategy;
and carrying out inventory level assessment according to the target replenishment strategy to obtain the target inventory planning result.
In some embodiments, the screening the initial replenishment policy according to the target cost and the preset cost threshold to obtain an intermediate replenishment policy, and screening the initial transportation path according to the target cost and the preset cost threshold to obtain the path planning result includes:
if the target cost is smaller than or equal to a preset cost threshold, the initial replenishment strategy is used as a first replenishment strategy, and the initial transportation path is used as the path planning result;
disturbing the first replenishment strategy according to a preset disturbance operator to obtain a second replenishment strategy;
and carrying out neighborhood search on the second replenishment strategy according to a preset neighborhood structure to obtain the intermediate replenishment strategy.
To achieve the above object, a second aspect of the embodiments of the present application proposes an inventory path planning apparatus, the apparatus including:
the dividing module is used for dividing the preset planning time period into a plurality of periods; the preset planning time period comprises a starting time;
the determining module is used for determining product demand data of the demand nodes in the period according to a preset demand set;
the acquisition module is used for acquiring an initial replenishment strategy of the supply node to the demand node and initial inventory data of the demand node at the starting time;
the first calculation module is used for obtaining replenishment data of the supply node to the demand node according to the initial replenishment strategy and the initial inventory data;
the second calculation module is used for carrying out inventory level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain intermediate inventory data of the demand node in the period, and carrying out backorder level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain backorder data of the demand node in the period;
the third calculation module is used for obtaining the storage cost of the demand node according to the preset unit storage cost and the intermediate inventory data and calculating risk data of the intermediate inventory data which are not in a preset inventory level interval; obtaining the stock-out cost of the demand node according to the preset unit stock-out cost and the stock-out data; carrying out path planning on the path between the supply node and the demand node to obtain an initial transportation path; calculating the transportation cost of product transportation according to the initial transportation path;
A fourth calculation module for obtaining a target cost from the storage cost, the stock out cost, and the transportation cost;
and the planning module is used for carrying out strategy adjustment on the initial replenishment strategy according to the target cost and the risk data to obtain a target inventory planning result, and carrying out path adjustment on the initial transportation path according to the target cost to obtain a path planning result.
To achieve the above object, a third aspect of the embodiments of the present application proposes an electronic device, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the inventory path planning method according to the first aspect when executing the computer program.
To achieve the above object, a fourth aspect of the embodiments of the present application proposes a computer-readable storage medium storing a computer program that, when executed by a processor, implements the inventory path planning method described in the first aspect.
According to the inventory path planning method, the inventory path planning device, the electronic equipment and the computer readable storage medium, the service level of the supply node to the demand node is quantitatively described through the risk data, the strategy adjustment is carried out on the initial replenishment strategy according to the target cost and the risk data, the target inventory planning result is obtained, the inventory planning is carried out through the target cost and the service level, the cost problem is considered in the inventory planning process, the service level of the supply node to the demand node is considered, the balance of the cost and the service level is achieved, the inventory planning is closer to a real scene, the accuracy of the inventory planning is improved, meanwhile, the path planning is carried out through the target cost which is balanced, and the accuracy of the path planning is improved.
Drawings
FIG. 1 is a flow chart of an inventory path planning method provided by an embodiment of the present application;
fig. 2 is a flowchart of step S140 in fig. 1;
fig. 3 is a flowchart of step S150 in fig. 1;
fig. 4 is a first flowchart of step S160 in fig. 1;
fig. 5 is a second flowchart of step S160 in fig. 1;
fig. 6 is a flowchart of step S180 in fig. 1;
fig. 7 is a flowchart of step S610 in fig. 6;
FIG. 8 is a schematic diagram of an inventory path planning apparatus according to an embodiment of the present application;
fig. 9 is a schematic hardware structure of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
It should be noted that although functional block division is performed in a device diagram and a logic sequence is shown in a flowchart, in some cases, the steps shown or described may be performed in a different order than the block division in the device, or in the flowchart. The terms first, second and the like in the description and in the claims and in the above-described figures, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.
In the related art, the total cost is taken as an objective function, inventory and paths are planned through minimization of the objective function, but the space with compressible cost is limited, so that the planning mode through the compression cost cannot be close to a real scene, an inventory path planning result is wrong, optimal inventory and paths cannot be obtained, and the accuracy of inventory path planning is reduced.
Based on this, the embodiment of the application provides an inventory path planning method, an inventory path planning device, electronic equipment and a computer readable storage medium, which aim to improve the accuracy of inventory path planning.
The inventory path planning method, the inventory path planning device, the electronic device and the computer readable storage medium provided in the embodiments of the present application are specifically described by the following embodiments, and the inventory path planning method in the embodiments of the present application is first described.
The embodiment of the application provides an inventory path planning method, and relates to the technical field of transportation optimization. The inventory path planning method provided by the embodiment of the application can be applied to a terminal, a server side and software running in the terminal or the server side. In some embodiments, the terminal may be a smart phone, tablet, notebook, desktop, etc.; the server side can be configured as an independent physical server, a server cluster or a distributed system formed by a plurality of physical servers, and a cloud server for providing cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, CDNs, basic cloud computing services such as big data and artificial intelligent platforms and the like; the software may be an application or the like that implements the inventory path planning method, but is not limited to the above form.
The subject application is operational with numerous general purpose or special purpose computer system environments or configurations. For example: personal computers, server computers, hand-held or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like. The application may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The application may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
Fig. 1 is an optional flowchart of a method for planning an inventory path according to an embodiment of the present application, where the method in fig. 1 may include, but is not limited to, steps S110 to S180.
Step S110, dividing a preset planning time period into a plurality of periods; the preset planning time period comprises a starting time;
step S120, determining product demand data of demand nodes in a period according to a preset demand set;
step S130, obtaining an initial replenishment strategy of a supply node to a demand node and initial inventory data of the demand node at the starting time;
step S140, obtaining replenishment data of the supply node to the demand node according to the initial replenishment strategy and the initial inventory data;
step S150, carrying out inventory level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain intermediate inventory data of the demand nodes in the period, and carrying out backorder level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain backorder data of the demand nodes in the period;
step S160, obtaining the storage cost of the demand node according to the preset unit storage cost and the intermediate inventory data, and calculating risk data of the intermediate inventory data which is not in the preset inventory level interval; obtaining the stock-out cost of the demand node according to the preset unit stock-out cost and stock-out data; path planning is carried out on paths from the supply nodes to the demand nodes, and an initial transportation path is obtained; calculating the transportation cost of product transportation according to the initial transportation path;
Step S170, obtaining target cost according to the storage cost, the stock-out cost and the transportation cost;
and step S180, performing strategy adjustment on the initial replenishment strategy according to the target cost and the risk data to obtain a target inventory planning result, and performing path adjustment on the initial transportation path according to the target cost to obtain a path planning result.
In the steps S110 to S180 illustrated in the embodiments of the present application, by dividing the preset planning period into a plurality of periods, the inventory of the demand node may be planned in each period, and the path from the supply node to the demand node may be planned. Because the product demand data is random and has uncertainty, the product demand data of the demand nodes in the period is determined according to the preset demand set, and the product demand data can be quantitatively described in a deterministic manner. Further, the target cost is obtained according to the storage cost, the stock-out cost and the transportation cost, and the storage cost, the stock-out cost and the transportation cost are introduced, so that the solution of the inventory path planning problem is closer to a real scene. Finally, the initial replenishment strategy is subjected to strategy adjustment according to the target cost and the risk data to obtain a target inventory planning result, the service level can be quantitatively described through the risk data, inventory planning is performed by utilizing the target cost and the service level, the cost problem is considered in the inventory planning process, the service level of the supply node to the demand node is considered, the balance of the cost and the service level is achieved, the accuracy of inventory planning is improved, meanwhile, path planning is performed according to the optimized target cost, and the accuracy of path planning is improved.
In step S110 of some embodiments, the preset programming period is divided into a plurality of periods according to a time step, and the preset programming period is a time range including a start time and an end time. The preset programming period is a set of a plurality of cycles, and if the preset programming period is denoted as T, the plurality of cycles is denoted as t= {1, 2. T., |q| where T e T represents the T-th period, |q| is determined by the ratio of the preset planning period to the time step.
In step S120 of some embodiments, in the multi-cycle random inventory path problem in the limited planning time, one supply node is responsible for distributing the product to a plurality of demand nodes in the preset planning time period to meet the random demand of each demand node, the supply node and each demand node belong to the same set N, if the number of the supply nodes is N, n= {0, 1..i..i..i..n },0 represents the supply node, i represents the i-th demand node, N demand nodes form a set S, denoted s=n\0, the supply node may be a merchant that supplies the product and the demand node may be a merchant that obtains the product from the supply node and sells the product. The supply node has an unlimited number of homogeneity fleets with limited loading capacity, i.e. the loading capacity, transport efficiency and transport costs of each vehicle in the fleets are about the same, and the supply node needs to determine the number of products to be delivered to n demand nodes and the delivery route in each cycle, so that the homogeneity fleets deliver a preset number of products to the demand nodes along the delivery route. It should be noted that, the unlimited number means that the fleet is unlimited in scale, the fleet has sufficient vehicles to transport products, the limited loading capacity means that the loading capacity of the vehicles is limited, each vehicle can only load a certain amount of products, and the transport efficiency means the distribution speed, the loading and unloading efficiency, etc. of the vehicles.
In the t period, the product demand data of each demand node are uncertain, a preset demand set, namely a demand uncertainty set, is established through distributed robust optimization, the product demand data of each demand node in different periods are described according to the demand uncertainty set, wherein the product demand data are used for representing the product demand quantity of users to the demand nodes, and the specific representation of the demand uncertainty set is shown in a formula (1).
Figure BDA0004087176350000071
Wherein F represents a set of demand uncertainty;
Figure BDA0004087176350000072
product demand representing demand nodes as integersSolving data, wherein the product demand data obeys probability distribution P;ξfor the lower limit value of the product demand data, +.>
Figure BDA0004087176350000073
The product demand data is the upper limit value of the product demand data, and the product demand data is more than or equal to the lower limit value or less than or equal to the upper limit value; u is the average value of the product demand data, and each demand node has an average value.
In step S130 of some embodiments, if the inventory level of the current period of the demand node cannot meet the demand of the next period, the supply node needs to replenish the demand node in the current period according to an initial replenishment strategy, where the initial replenishment strategy is a ratio of the replenishment amount to the maximum inventory capacity of the demand node, and the value is [0, rmax ], and rmax is 1. It will be appreciated that when the initial restocking strategy has a value rmax, which indicates that the inventory is equal to the maximum inventory capacity, the initial inventory data at the start time of the demand node is the number of products stored by the demand node at the start time. The initial restocking strategy may be 0, i.e., the demand nodes may only meet the product demand for the next cycle depending on the current inventory level, and may be 0.25, 0.5, 0.75, and 1.
Referring to fig. 2, in some embodiments, step S140 may include, but is not limited to, steps S210 to S230:
step S210, obtaining the maximum stock capacity of the demand node;
step S220, carrying out supply level assessment according to the initial replenishment strategy and the maximum stock capacity to obtain supply data of the demand node;
step S230, the replenishment level is evaluated according to the supply data and the initial inventory data to obtain replenishment data.
In step S210 of some embodiments, the supply capability of the supply node is not limited, and is responsible for inventory management of n demand nodes, where the warehouse of the demand nodes has a storage capacity limitation, and the maximum inventory capacity of the demand nodes is obtained, where the maximum inventory capacity is used to represent the maximum storable product quantity of the demand nodes.
In step S220 of some embodiments, the initial restocking strategy is multiplied by the maximum inventory capacity to obtain supply data that the supply node supplies to the demand node.
In step S230 of some embodiments, the inventory data of the demand node in the previous cycle is obtained according to the initial inventory data of the demand node in the initial time, the inventory data of the previous cycle is subtracted from the supply data to obtain the replenishment data of the demand node in the current cycle, the replenishment data is used for indicating the replenishment quantity of the product received by the demand node from the supply node in the current cycle, and the calculation mode of the replenishment data is shown in formula (2).
Figure BDA0004087176350000081
Wherein r is an initial replenishment strategy; d, d it [r]Representing replenishment data of the demand node i in a period t when the initial replenishment strategy is r; c (C) i Maximum inventory capacity for demand node i;
Figure BDA0004087176350000082
the inventory data of the demand node i at the beginning of the period t is equal to the inventory data of the demand node i at the end of the previous period t-1.
It should be noted that, the inventory data is divided into inventory data at the beginning of a period and inventory data at the end of the period, the inventory data at the beginning of the current period is equal to the inventory data at the end of the previous period, the inventory data at the end of the period is the inventory level after the supply node transports the product to the demand node and the demand node sells the product, and the inventory data at the end of the period in the embodiment of the present application refers to the inventory data at the end of the period.
Through the steps S210 to S230, the replenishment data of each cycle of the demand node under the initial replenishment strategy can be obtained, so as to perform inventory planning or path planning according to the replenishment data.
Referring to fig. 3, in some embodiments, step S150 may include, but is not limited to, steps S310 to S330:
step S310, evaluating the total quantity of products according to the initial inventory data and the replenishment data to obtain total product data of the demand nodes in the period;
Step S320, product allowance evaluation is carried out according to the total product data and the product demand data, and intermediate inventory data are obtained;
step S330, product backorder assessment is carried out according to the total product data and the product demand data, and backorder data is obtained.
In step S310 of some embodiments, inventory data I for the last period t-1 is based on demand node I i,t-1 Restocking data d at current period t it And product demand data at the current period t
Figure BDA0004087176350000083
Performing inventory level assessment to obtain intermediate inventory data I of the demand node in the current period t i,t The calculation method of the intermediate stock data is shown in formula (3).
Figure BDA0004087176350000091
The previous period of the previous period is period t-2, inventory data of the previous period t-1 is obtained according to inventory data of the period t-2, replenishment data of the previous period t-1 and product demand data of the previous period t-1, intermediate inventory data is obtained by iterating the inventory data of the previous period layer by layer, and a calculation method of the intermediate inventory data is shown in a formula (4).
Figure BDA0004087176350000092
Wherein I is i0 Initial inventory data at an initial time for a demand node i; s represents a period s, the s-th period; d, d is The method comprises the steps of providing replenishment data of a demand node i in a period s;
Figure BDA0004087176350000093
product demand data for demand node i at period s.
Specifically, it will be required thatSumming the replenishment data of each cycle from the cycle 1 to the current cycle by the nodes to obtain accumulated replenishment data, summing the initial inventory data and the accumulated replenishment data to evaluate the total product quantity in the demand node warehouse to obtain the total product data of the demand node i, namely the total product data is
Figure BDA0004087176350000094
In step S320 of some embodiments, the inventory of the demand node is consumed by the user' S demand, and in order to obtain the actual inventory of the demand node, the product demand data of each cycle from cycle 1 to the current cycle is subtracted from the total product data to obtain the intermediate inventory data of the demand node i as
Figure BDA0004087176350000095
The intermediate inventory data is used to represent the actual inventory level of the demand node i for the current period t. Since the intermediate stock data is a non-negative integer, the intermediate stock data of the demand node i is +.>
Figure BDA0004087176350000096
It will be appreciated that the intermediate inventory data of the demand node i at the period t is the difference between the initial inventory data of the demand node i at the initial time and the accumulated product quantity in and out, wherein the accumulated product quantity in and out is the difference between the replenishment data accumulated from the period 1 to the period t and the product demand data.
In step S330 of some embodiments, a condition in which the demand node is allowed to be out of stock occurs, and when the product demand data of the demand node i at the period t is greater than the total product data, it is indicated that the demand node i is out of stock at the period t. Summing the product demand data of each period from period 1 to period t to obtain total demand data, subtracting the total product data from the total demand data to obtain the backorder data of the demand node i in the period t as
Figure BDA0004087176350000097
The backorder data is a non-negative integer.
Through the steps S310 to S330, the actual product quantity and the absent product quantity of each demand node in each period can be obtained, so as to plan the inventory of the demand nodes and the paths from the supply nodes to the demand nodes in each period according to the actual product quantity and the absent product quantity.
Referring to fig. 4, in some embodiments, the preset inventory level interval includes first endpoint data and second endpoint data, and step S160 may include, but is not limited to, step S410 or step S420:
step S410, performing difference calculation on the intermediate stock data and the first endpoint data to obtain first difference data; performing difference calculation on the intermediate inventory data and the second endpoint data to obtain second difference data; obtaining risk data according to the first difference data and the second difference data;
step S420, acquiring risk parameters, wherein the risk parameters are used for representing risks of unsatisfied product demand data; performing risk assessment according to the risk parameters, the first endpoint data and the intermediate inventory data to obtain a first risk; performing risk assessment according to the risk parameters, the second endpoint data and the intermediate inventory data to obtain a second risk; and obtaining risk data according to the first risk and the second risk.
In step S410 of some embodiments, the unit storage cost is multiplied by the intermediate inventory data of a certain demand node in a certain period to obtain the storage cost of the demand node in the certain period, the storage cost of each period of the demand node in a preset planning period is summed to obtain the storage cost of the demand node in the preset planning period, and the storage cost of each demand node in the set S in the preset planning period is summed to obtain the storage cost of the demand node. The calculation method of the storage cost of the demand node is shown in formula (5).
Figure BDA0004087176350000101
Wherein h is i Cost per unit of storage. Each demand node is in each periodThe target interval of the stock level is required to be maintained, the target interval is a preset stock level interval and is used for limiting intermediate stock data of a demand node, the preset stock level interval comprises first end point data and second end point data, the first end point data is smaller than the second end point data, the first end point data is used for determining a lower limit value of the stock level, the second end point data is used for determining an upper limit value of the stock level, and the intermediate stock data can be larger than or equal to the first end point data or smaller than or equal to the second end point data through the preset stock level interval. It will be appreciated that if the first endpoint data is denoted as τ, the second endpoint data is denoted as
Figure BDA0004087176350000102
Presetting stock level interval as
Figure BDA0004087176350000103
The intermediate stock data of the demand node should be greater than or equal to the lower limit value τ or less than or equal to the upper limit value +.>
Figure BDA0004087176350000104
I.e.
Figure BDA0004087176350000105
The service level of the supply node is improved by maintaining the intermediate inventory data of the n demand nodes within a preset inventory level interval within a preset planning period.
The objective of the inventory path problem in the embodiment of the present application is to maximize the service level of the supply node, add the service level to the basic inventory path model, construct a robust inventory path model with the service level as the objective, and the intermediate inventory data of each period of the demand node has interval requirements, where the service level is measured by the expected probability that the intermediate inventory data is maintained in the objective interval in the preset planning period, and maximize the service level, that is, minimize the risk that the intermediate inventory data is not in the preset inventory level interval.
And (3) distributing the same initial replenishment strategy to each period of each demand node i epsilon S and S=N\ {0}, namely, each unit cell with the size of the SxT matrix has the same initial replenishment strategy value, and replenishing all the demand nodes in each period by adopting the same initial replenishment strategy. After the initial replenishment strategy is determined, calculating a risk value of the initial replenishment strategy violating a preset inventory level interval in a preset planning time period according to the product demand data and the inventory consumption condition. And summing risks of the intermediate inventory data of each period of the demand node in a preset inventory level interval by adopting an initial replenishment strategy by a certain demand node to obtain risk data of the demand node in a preset planning time period, and summing the risk data of all the demand nodes in the preset planning time period to obtain risk data of the demand node in the preset inventory level interval under the initial supply strategy. It should be noted that, the risk that the intermediate inventory data in a certain period is not in the preset inventory level interval is measured by subtracting the intermediate inventory data in the current period from the first endpoint data to obtain first difference data, subtracting the intermediate inventory data in the current period from the second endpoint data to obtain second difference data, and taking the minimum value of the first difference data and the second difference data as the risk value that the intermediate inventory data in the current period of the demand node is not in the preset inventory level interval.
Specifically, when the demand node i adopts the initial replenishment policy r in the period t to obtain intermediate inventory data, if the intermediate inventory data is in the preset inventory level interval, the risk is 0, if the intermediate inventory data is not in the preset inventory level interval, the differences between the intermediate inventory data and the first endpoint data and between the intermediate inventory data and the second endpoint data are calculated respectively, and the minimum difference is taken as the risk value, namely, when the initial replenishment policy is r, the demand node i violates the risk value of the preset inventory level interval
Figure BDA0004087176350000111
Wherein->
Figure BDA0004087176350000112
Initial inventory level for period t+1, corresponding to end inventory level for period tI.e., intermediate inventory data for period t; τ it The first endpoint data of the demand node i in the period t; />
Figure BDA0004087176350000113
Is the second endpoint data of the demand node i at the period t. Adding the risk values of the demand node i in each period to obtain a risk value sigma of the demand node in a preset planning time period by adopting an initial replenishment strategy r t∈T risk it [r]Adding Risk values of all the demand nodes in a preset planning time period by adopting an initial replenishment strategy r to obtain Risk data Risk [ r ] of the demand nodes under the initial replenishment strategy r]Is sigma (sigma) i∈S ∑ t∈T risk it [r]。
Once the demand node is out of stock, the demand node represents the loss of product selling opportunities, cannot be compensated by replenishment, and needs to have corresponding punishment cost. According to the method and the system, unit stock-out cost is multiplied by stock-out data of a certain demand node in a certain period to obtain stock-out cost of the demand node in the period, stock-out cost of each period of the demand node in a preset planning time period is summed to obtain stock-out cost of the demand node in the preset planning time period, stock-out cost of each demand node in the set S in the preset planning time period is summed to obtain stock-out cost of the demand node. The calculation method of the stock out cost of the demand node is shown in formula (6).
Figure BDA0004087176350000114
Wherein s is i The unit backorder cost for demand node i.
In step S420 of some embodiments, the service level is measured by applying the multi-period short-term risk measure model based on the utility function, which considers both the probability of violating the target and the amplitude of violating the target, and compared with the method of describing the service level by using the probability model, the method can overcome the defect that the probability model cannot reflect the deviation from the target level, and simultaneously uses the service level as the target, thereby solving the problem that the random demand distribution function is difficult to describe due to insufficient data. The multicycle short-term risk measure model embodies the service level as:
Figure BDA0004087176350000115
wherein alpha is it A risk parameter, which is used for representing the risk that the product demand data of the demand node i in the period t is not satisfied; μ is a concave utility function;
Figure BDA0004087176350000116
is a first risk; />
Figure BDA0004087176350000117
Is a second risk.
It should be noted that, the available inventory of the demand node i in the period t is the sum of the intermediate inventory data of the demand node in the period t-1 and the replenishment data of the period t, the available inventory is divided by the product demand data of the demand node in the period t to obtain the actual demand satisfaction rate of the demand node i in the period t, and the probability that the actual demand satisfaction rate is smaller than the expected demand satisfaction rate is used as the risk parameter.
To reduce the computational effort, facilitate the solution of large-scale inventory path problems, a piecewise linear function is used instead of the utility function μ, i.e
Figure BDA0004087176350000121
Converting service levels into:
Figure BDA0004087176350000122
substituting the formula (4) into the formula (8) and decomposing to ensure that the intermediate inventory data is in the target interval, and finally converting the service level into:
Figure BDA0004087176350000123
Figure BDA0004087176350000124
Figure BDA0004087176350000125
Figure BDA0004087176350000126
wherein the probability distribution P belongs to a distribution family F,
Figure BDA0004087176350000127
representing the lower bound of the probability distribution.
Substituting the first risk into the formula (10) to obtain a first probability
Figure BDA0004087176350000128
Substituting the second risk into formula (11) to obtain a second probability +.>
Figure BDA0004087176350000129
And taking the minimum value of the first probability and the second probability as risk data.
Through the steps S410 to S420, risk data of intermediate inventory data not in the target interval can be obtained, and the problem of inventory path is solved by minimizing the risk data, so that the accuracy of inventory path planning is improved.
Referring to fig. 5, in some embodiments, step S160 may further include, but is not limited to, steps S510 to S540:
step S510, obtaining a first distance, a second distance and a third distance, wherein the first distance is used for representing the distance between a supply node and a demand node, the second distance is used for representing the distance between the supply node and another demand node, and the third distance is used for representing the distance between the demand node and another demand node;
Step S520, performing distance calculation according to the first distance, the second distance and the third distance to obtain the saving mileage data between the demand node and another demand node;
step S530, planning a path between a demand node and another demand node according to the mileage saving data to obtain an initial path;
step S540, constructing a route according to the initial route to obtain the initial transportation route.
In step S510 of some embodiments, a vehicle can only deliver products to a plurality of demand nodes along a path in a same period, product demand data of a same demand node in a same period can be split, and products are delivered by a plurality of vehicles. Acquiring a first distance l between a supply node 0 and a demand node i 0i Acquiring a second distance l between the supply node and another demand node j 0j Obtaining a third distance l between the demand node i and another demand node j ij 。
In step S520 of some embodiments, the saved mileage data from the demand node i to another demand node j is calculated according to the C-W saving algorithm, and the calculation method of the saved mileage data is shown in formula (13).
S(i,j)＝l 0i +l 0j -l ij Formula (13)
In step S530 of some embodiments, S (i, j) is ordered from large to small, the greater the mileage data is, the more the path from i to j should be prioritized, resulting in an initial path.
In step S540 of some embodiments, the initial route is connected to a complete access route, resulting in an initial transportation route. The supply nodes distribute products to n demand nodes along an initial transportation path through vehicles with the capacity limited to V, the number of the vehicles and the number of the access demand nodes are not limited, the transportation cost for transporting the products according to the initial transportation path is calculated, and the calculation method of the transportation cost is shown in a formula (14).
Figure BDA0004087176350000131
Wherein the first item of transportation cost is variable transportation cost, the second item is fixed transportation cost, the third item is empty return cost, c ij For the unit transportation cost from node i to node j, q ijt For the number of product transportation from node i to node j in period t, f is the fixed use cost of the vehicle once, x 0it To supply node 0 to demand node j a number of times during period t,
Figure BDA0004087176350000132
the cost is returned for one empty unit.
It should be noted that there is a vehicle loading constraint during transportation, and the number of vehicles transported from node i to node j in period t does not exceed the vehicle load limit, i.e.
Figure BDA0004087176350000133
The quantity of product transported is an integer greater than or equal to 0, i.e. q ijt Not less than 0; in the transportation process, the balance constraint of vehicle access exists, and the times of the vehicle access through each node in the same period are equal, namely the times are equal j∈N,j≠i x ijt ＝∑ j∈N,j≠i x jit And x is ijt Is a positive integer; there is also a sub-loop elimination constraint in the transportation process, and the replenishment data of the demand node i at the period t is equal to the product transportation quantity of the vehicle at the period t route (j, i) minus the product transportation quantity of the vehicle at the period t route (i, j), namely
Figure BDA0004087176350000134
The total quantity of products sent from the supply node in period t is equal to the total quantity of restocking data of all the demand nodes in period t, namely +.>
Figure BDA0004087176350000135
Through the above steps S510 to S540, it is possible to determine a route along which the vehicle accesses each demand node, so as to distribute the product to the demand nodes according to the route.
In step S170 of some embodiments, a total cost is evaluated based on the storage cost, the stock-out cost, and the transportation cost, and the storage cost, the stock-out cost, and the transportation cost are summed up to obtain a target cost.
Referring to fig. 6, in some embodiments, step S180 may include, but is not limited to, steps S610 to S630:
step S610, screening the initial replenishment strategy according to the target cost and the preset cost threshold value to obtain an intermediate replenishment strategy, and screening the initial transportation path according to the target cost and the preset cost threshold value to obtain a path planning result;
Step S620, selecting an intermediate replenishment strategy with minimum risk data as a target replenishment strategy;
step S630, carrying out inventory level assessment according to the target replenishment strategy to obtain a target inventory planning result.
In step S610 of some embodiments, a heuristic algorithm, such as a variable neighborhood search algorithm (Variable Neighborhood Search, VNS), is used to perform inventory path planning, so as to solve a large-scale inventory path problem, improve accuracy and efficiency of inventory path planning, and improve service level of a supply node, where the variable neighborhood search algorithm is divided into two steps, namely, initial solution construction and variable neighborhood search optimization, the initial solution construction step is divided into two stages, risk data of different initial replenishment strategies not in a preset inventory level interval is calculated in the first stage, a specific path is planned by using a C-W saving algorithm for each initial replenishment strategy in the second stage, and a target cost is calculated.
In step S620 of some embodiments, in order to improve the service level of the supply node, an intermediate restocking policy with the smallest risk data is selected as the target restocking policy.
In step S630 of some embodiments, the demand nodes are restocked according to the target restocking strategy to obtain restocking data, and the inventory data of the demand nodes in the previous period and the restocking data are added to perform inventory level assessment to obtain the target inventory planning result.
In the steps S610 to S630, the stock path decision is performed by adopting the variable neighborhood search algorithm, so that the problem of the stock path of the large planning can be efficiently solved, and the efficiency of the stock path planning is improved.
Referring to fig. 7, in some embodiments, step S610 may include, but is not limited to, steps S710 to S730:
step S710, if the target cost is less than or equal to the preset cost threshold, taking the initial replenishment strategy as a first replenishment strategy, and taking the initial transportation path as a path planning result;
step S720, disturbing the first replenishment strategy according to a preset disturbance operator to obtain a second replenishment strategy;
step S730, performing neighborhood search on the second replenishment strategy according to a preset neighborhood structure to obtain an intermediate replenishment strategy.
In step S710 of some embodiments, if the target cost is less than or equal to the preset cost threshold, indicating that the target cost is within the budget of the decision maker, the initial replenishment strategy is taken as the first replenishment strategy, and the initial transportation path is taken as the path planning result. If the target cost is greater than the preset cost threshold, repeating the first stage and the second stage to readjust the initial replenishment strategy and the path planning until the first replenishment strategy is obtained.
In step S720 of some embodiments, in order to ensure that an accurate replenishment strategy is obtained, a variable neighborhood search optimization is performed on the initial replenishment strategy. Adding each first replenishment strategy meeting budget constraint into a feasible solution set basesol, adding the first replenishment strategy with the smallest risk data into an excellent solution set elitesol, randomly generating a decimal between [0,1] as a replacement replenishment strategy through a disturbance operator, updating the first replenishment strategy according to the replacement replenishment strategy to obtain a second replenishment strategy, and taking the second replenishment strategy as a new solution newsol.
It should be noted that, the disturbance principle is to randomly select an element from the original replenishment strategy matrix for modification, the number of modification times is determined by the preset disturbance times, and as the number of modification times increases, the number of randomly modified elements in the original replenishment strategy matrix also increases gradually, and the change of the original replenishment strategy matrix also increases gradually.
It should be further noted that, in the initial solution construction step, each cell of the matrix with the size of s×t has the same initial replenishment policy value, an integer is randomly generated in (0, |s| -1) as a target row, an integer is randomly generated in (0, |t| -1) as a target column col, and a target cell basesol [ row ] [ col ] is selected from a plurality of cells of the matrix according to the target row and the target column col, and the first replenishment policy on the target cell is updated as the replacement replenishment policy, so that a new solution newsol is obtained through disturbance of a preset disturbance number.
In step S730 of some embodiments, a new solution is generated by perturbation, and local search is performed on a neighborhood of the new solution by a neighborhood structure, so as to obtain a local minimum in the current new solution neighborhood, and then the new solution is obtained again, where the neighborhood structure is a local neighborhood search operator. Specifically, a local neighborhood search operator is adopted to conduct neighborhood search on the second replenishment strategy on the basis of disturbance, the second replenishment strategy with the smallest risk data is selected as an intermediate replenishment strategy, and the intermediate replenishment strategy is used as a new solution newsol.
It will be appreciated that the principle of local neighborhood search is to randomly determine the position of an element in the replenishment strategy matrix after perturbation, and modify the value of the element multiple times to obtain a locally optimal solution in the current solution neighborhood.
Judging whether the solution quantity of the excellent solution set elitsol is smaller than a quantity threshold value, if so, adding a new solution newsol into the excellent solution set elitsol; if the judgment result is no, calculating Risk data Risk (newsol) of the intermediate replenishment strategy, identifying worst solution wortsol from the excellent solution set elitsol, calculating Risk data Risk (worstsol) of worst solution wortsol, replacing the wortsol in the excellent solution set with newsol if Risk (newsol) is less than Risk (worstsol), and if Risk (newsol) is greater than or equal to Risk (worstsol), not updating wortsol. It is understood that the worst solution worstsol is the solution with the greatest risk data in the excellent solution set.
Judging whether the Risk (newsol) of the new solution newsol is smaller than the Risk (basesol) of the feasible solution set basesol, and if so, adding the new solution newsol into the feasible solution set basesol; if the judgment result is negative, the updating is not carried out, and the number of times of non-updating is recorded until the number of times of non-updating is larger than S multiplied by T.
And continuously iterating, updating the excellent solution set elitsol and the feasible solution set basesol until the iteration number reaches the maximum iteration number, stopping iteration, and taking an intermediate replenishment strategy with the minimum risk data in the excellent solution set elitsol as a target replenishment strategy bestsol.
In the steps S710 to S730, the accuracy of the replenishment decision can be improved by optimizing the initial replenishment strategy through disturbance and local neighborhood search.
Referring to fig. 8, an embodiment of the present application further provides an inventory path planning apparatus, which may implement the above inventory path planning method, where the apparatus includes:
a dividing module 810, configured to divide a preset planning period into a plurality of periods; the preset planning time period comprises a starting time;
a determining module 820, configured to determine product demand data of the demand node in the period according to the preset demand set;
an obtaining module 830, configured to obtain an initial replenishment policy of the supply node for the demand node, and initial inventory data of the demand node at a start time;
a first calculation module 840, configured to obtain replenishment data for the supply node to the demand node according to the initial replenishment policy and the initial inventory data;
a second calculation module 850, configured to perform inventory level assessment according to the initial inventory data, the replenishment data, and the product demand data, obtain intermediate inventory data of the demand node in the cycle, and perform backorder level assessment according to the initial inventory data, the replenishment data, and the product demand data, obtain backorder data of the demand node in the cycle;
The third calculation module 860 is configured to obtain a storage cost of the demand node according to a preset unit storage cost and intermediate inventory data, and calculate risk data that the intermediate inventory data is not in a preset inventory level interval; obtaining the stock-out cost of the demand node according to the preset unit stock-out cost and stock-out data; path planning is carried out on paths from the supply nodes to the demand nodes, and an initial transportation path is obtained; calculating the transportation cost of product transportation according to the initial transportation path;
a fourth calculation module 870 for deriving a target cost from the storage cost, the backorder cost, and the transportation cost;
the planning module 880 is configured to perform policy adjustment on the initial replenishment policy according to the target cost and the risk data, obtain a target inventory planning result, and perform path adjustment on the initial transportation path according to the target cost, so as to obtain a path planning result.
The specific implementation of the inventory path planning device is basically the same as the specific embodiment of the inventory path planning method described above, and will not be described herein.
The embodiment of the application also provides electronic equipment, which comprises a memory and a processor, wherein the memory stores a computer program, and the processor realizes the inventory path planning method when executing the computer program. The electronic equipment can be any intelligent terminal including a tablet personal computer, a vehicle-mounted computer and the like.
Referring to fig. 9, fig. 9 illustrates a hardware structure of an electronic device according to another embodiment, the electronic device includes:
the processor 910 may be implemented by a general-purpose CPU (central processing unit), a microprocessor, an application-specific integrated circuit (ApplicationSpecificIntegratedCircuit, ASIC), or one or more integrated circuits, etc. for executing related programs to implement the technical solutions provided by the embodiments of the present application;
memory 920 may be implemented in the form of read-only memory (ReadOnlyMemory, ROM), static storage, dynamic storage, or random access memory (RandomAccessMemory, RAM). Memory 920 may store an operating system and other application programs, and when implementing the technical solutions provided in the embodiments of the present application through software or firmware, relevant program codes are stored in memory 920, and the processor 910 invokes an inventory path planning method to execute the embodiments of the present application;
an input/output interface 930 for inputting and outputting information;
the communication interface 940 is configured to implement communication interaction between the device and other devices, and may implement communication in a wired manner (e.g., USB, network cable, etc.), or may implement communication in a wireless manner (e.g., mobile network, WIFI, bluetooth, etc.);
A bus 950 for transferring information between components of the device (e.g., processor 910, memory 920, input/output interface 930, and communication interface 940);
wherein processor 910, memory 920, input/output interface 930, and communication interface 940 implement communication connections among each other within the device via a bus 950.
The embodiment of the application also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the inventory path planning method when being executed by a processor.
The memory, as a non-transitory computer readable storage medium, may be used to store non-transitory software programs as well as non-transitory computer executable programs. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory optionally includes memory remotely located relative to the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
According to the inventory path planning method, the inventory path planning device, the electronic equipment and the computer readable storage medium, the service level of the demand node is quantitatively described through the risk data, the initial replenishment strategy is subjected to strategy adjustment according to the target cost and the risk data, the target inventory planning result is obtained, the inventory planning is carried out through the target cost and the service level, the cost problem is considered in the inventory planning process, the service level of a user is considered, the balance of the cost and the service level is achieved, the inventory planning is closer to a real scene, the accuracy of the inventory planning is improved, meanwhile, the path planning is carried out through the target cost which is balanced, and the accuracy of the path planning is improved.
The embodiments described in the embodiments of the present application are for more clearly describing the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided by the embodiments of the present application, and as those skilled in the art can know that, with the evolution of technology and the appearance of new application scenarios, the technical solutions provided by the embodiments of the present application are equally applicable to similar technical problems.
It will be appreciated by those skilled in the art that the technical solutions shown in the figures do not constitute limitations of the embodiments of the present application, and may include more or fewer steps than shown, or may combine certain steps, or different steps.
The above described apparatus embodiments are merely illustrative, wherein the units illustrated as separate components may or may not be physically separate, i.e. may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
Those of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
The terms "first," "second," "third," "fourth," and the like in the description of the present application and in the above-described figures, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that in this application, "at least one" means one or more, and "a plurality" means two or more. "and/or" for describing the association relationship of the association object, the representation may have three relationships, for example, "a and/or B" may represent: only a, only B and both a and B are present, wherein a, B may be singular or plural. The character "/" generally indicates that the context-dependent object is an "or" relationship. "at least one of" or the like means any combination of these items, including any combination of single item(s) or plural items(s). For example, at least one (one) of a, b or c may represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", wherein a, b, c may be single or plural.
In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the above-described division of units is merely a logical function division, and there may be another division manner in actual implementation, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described above as separate components may or may not be physically separate, and components shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including multiple instructions to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods of the various embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing a program.
Preferred embodiments of the present application are described above with reference to the accompanying drawings, and thus do not limit the scope of the claims of the embodiments of the present application. Any modifications, equivalent substitutions and improvements made by those skilled in the art without departing from the scope and spirit of the embodiments of the present application shall fall within the scope of the claims of the embodiments of the present application.

Claims (10)

1. An inventory path planning method, characterized in that the method comprises:
dividing a preset planning time period into a plurality of periods; the preset planning time period comprises a starting time;
determining product demand data of demand nodes in the period according to a preset demand set;
acquiring an initial replenishment strategy of a supply node to the demand node and initial inventory data of the demand node at the starting time;
obtaining replenishment data of the supply node to the demand node according to the initial replenishment strategy and the initial inventory data;
performing inventory level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain intermediate inventory data of the demand node in the period, and performing backorder level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain backorder data of the demand node in the period;
Obtaining the storage cost of the demand node according to the preset unit storage cost and the intermediate inventory data, and calculating risk data of the intermediate inventory data which are not in a preset inventory level interval; obtaining the stock-out cost of the demand node according to the preset unit stock-out cost and the stock-out data; carrying out path planning on the path between the supply node and the demand node to obtain an initial transportation path; calculating the transportation cost of product transportation according to the initial transportation path;
obtaining a target cost from the storage cost, the stock out cost, and the transportation cost;
and carrying out strategy adjustment on the initial replenishment strategy according to the target cost and the risk data to obtain a target inventory planning result, and carrying out path adjustment on the initial transportation path according to the target cost to obtain a path planning result.
2. The inventory path planning method according to claim 1, wherein the obtaining replenishment data of the supply node to the demand node according to the initial replenishment strategy and the initial inventory data comprises:
obtaining the maximum stock capacity of the demand node;
Performing supply level assessment according to the initial replenishment strategy and the maximum inventory capacity to obtain supply data of the demand node;
and carrying out replenishment level assessment according to the supply data and the initial inventory data to obtain replenishment data.
3. The inventory path planning method according to claim 1, wherein the performing inventory level assessment based on the initial inventory data, the replenishment data, and the product demand data to obtain intermediate inventory data of the demand node in the cycle, performing backorder level assessment based on the initial inventory data, the replenishment data, and the product demand data to obtain backorder data of the demand node in the cycle, comprises:
carrying out total product assessment according to the initial inventory data and the replenishment data to obtain total product data of the demand node in the period;
performing product allowance assessment according to the total product data and the product demand data to obtain the intermediate inventory data;
and carrying out product backorder assessment according to the total product data and the product demand data to obtain the backorder data.
4. The inventory path planning method according to claim 1, wherein the preset inventory level interval includes first endpoint data and second endpoint data, the first endpoint data being smaller than the second endpoint data, the calculating risk data for the intermediate inventory data not being in the preset inventory level interval includes:
Performing difference calculation on the intermediate inventory data and the first end point data to obtain first difference data; performing difference calculation on the intermediate inventory data and the second endpoint data to obtain second difference data; obtaining the risk data according to the first difference data and the second difference data;
or alternatively, the process may be performed,
acquiring risk parameters, wherein the risk parameters are used for representing risks that the product demand data are not met; performing risk assessment according to the risk parameters, the first endpoint data and the intermediate inventory data to obtain a first risk; performing risk assessment according to the risk parameters, the second endpoint data and the intermediate inventory data to obtain a second risk; and obtaining the risk data according to the first risk and the second risk.
5. The inventory path planning method according to claim 1, wherein the path planning the path between the supply node and the demand node to obtain an initial transport path includes:
acquiring a first distance, a second distance and a third distance, wherein the first distance is used for representing the distance between the supply node and the demand node, the second distance is used for representing the distance between the supply node and another demand node, and the third distance is used for representing the distance between the demand node and the another demand node;
Performing distance calculation according to the first distance, the second distance and the third distance to obtain saving mileage data between the demand node and the other demand node;
planning a path between the demand node and the other demand node according to the mileage saving data to obtain an initial path;
and constructing a route according to the initial route to obtain the initial transportation route.
6. The inventory path planning method according to any one of claims 1 to 5, wherein the performing policy adjustment on the initial replenishment policy according to the target cost and the risk data to obtain a target inventory planning result, performing path adjustment on the initial transportation path according to the target cost to obtain a path planning result, includes:
screening the initial replenishment strategy according to the target cost and a preset cost threshold to obtain an intermediate replenishment strategy, and screening the initial transportation path according to the target cost and the preset cost threshold to obtain the path planning result;
selecting the intermediate replenishment strategy with the smallest risk data as a target replenishment strategy;
And carrying out inventory level assessment according to the target replenishment strategy to obtain the target inventory planning result.
7. The inventory path planning method according to claim 6, wherein the screening the initial replenishment strategy according to the target cost and a preset cost threshold to obtain an intermediate replenishment strategy, and the screening the initial transportation path according to the target cost and the preset cost threshold to obtain the path planning result includes:
if the target cost is smaller than or equal to a preset cost threshold, the initial replenishment strategy is used as a first replenishment strategy, and the initial transportation path is used as the path planning result;
disturbing the first replenishment strategy according to a preset disturbance operator to obtain a second replenishment strategy;
and carrying out neighborhood search on the second replenishment strategy according to a preset neighborhood structure to obtain the intermediate replenishment strategy.
8. An inventory path planning apparatus, said apparatus comprising:
the dividing module is used for dividing the preset planning time period into a plurality of periods; the preset planning time period comprises a starting time;
the determining module is used for determining product demand data of the demand nodes in the period according to a preset demand set;
The acquisition module is used for acquiring an initial replenishment strategy of the supply node to the demand node and initial inventory data of the demand node at the starting time;
the first calculation module is used for obtaining replenishment data of the supply node to the demand node according to the initial replenishment strategy and the initial inventory data;
the second calculation module is used for carrying out inventory level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain intermediate inventory data of the demand node in the period, and carrying out backorder level assessment according to the initial inventory data, the replenishment data and the product demand data to obtain backorder data of the demand node in the period;
the third calculation module is used for obtaining the storage cost of the demand node according to the preset unit storage cost and the intermediate inventory data and calculating risk data of the intermediate inventory data which are not in a preset inventory level interval; obtaining the stock-out cost of the demand node according to the preset unit stock-out cost and the stock-out data; carrying out path planning on the path between the supply node and the demand node to obtain an initial transportation path; calculating the transportation cost of product transportation according to the initial transportation path;
A fourth calculation module for obtaining a target cost from the storage cost, the stock out cost, and the transportation cost;
and the planning module is used for carrying out strategy adjustment on the initial replenishment strategy according to the target cost and the risk data to obtain a target inventory planning result, and carrying out path adjustment on the initial transportation path according to the target cost to obtain a path planning result.
9. An electronic device, characterized in that the electronic device comprises a memory and a processor, the memory storing a computer program, the processor implementing the inventory path planning method of any one of claims 1 to 7 when executing the computer program.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the inventory path planning method of any one of claims 1 to 7.
CN202310139928.7A 2023-02-13 2023-02-13 Inventory path planning method and device, electronic equipment and storage medium Pending CN116228069A (en)

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CN115358658A (en) * 2022-07-20 2022-11-18 华南理工大学 Inventory path optimal scheduling method for LPG drop-and-drop transportation

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