CN114548880A - Logistics planning method, equipment, device and storage medium - Google Patents
Logistics planning method, equipment, device and storage medium Download PDFInfo
- Publication number
- CN114548880A CN114548880A CN202210225592.1A CN202210225592A CN114548880A CN 114548880 A CN114548880 A CN 114548880A CN 202210225592 A CN202210225592 A CN 202210225592A CN 114548880 A CN114548880 A CN 114548880A
- Authority
- CN
- China
- Prior art keywords
- optimal
- time
- logistics
- transportation
- leveling
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 42
- 238000013439 planning Methods 0.000 title claims abstract description 37
- 238000003860 storage Methods 0.000 title claims abstract description 17
- 238000005192 partition Methods 0.000 claims abstract description 34
- 238000005457 optimization Methods 0.000 claims description 17
- 238000013316 zoning Methods 0.000 claims 3
- 238000004519 manufacturing process Methods 0.000 description 18
- 238000012545 processing Methods 0.000 description 6
- 238000011161 development Methods 0.000 description 4
- 230000006870 function Effects 0.000 description 4
- 238000004364 calculation method Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000007726 management method Methods 0.000 description 3
- 239000000463 material Substances 0.000 description 3
- 230000003247 decreasing effect Effects 0.000 description 2
- 230000013872 defecation Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000007613 environmental effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000006467 substitution reaction Methods 0.000 description 2
- 235000013311 vegetables Nutrition 0.000 description 2
- 238000004891 communication Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000009191 jumping Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 239000003016 pheromone Substances 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000010845 search algorithm Methods 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/047—Optimisation of routes or paths, e.g. travelling salesman problem
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/08—Logistics, e.g. warehousing, loading or distribution; Inventory or stock management
- G06Q10/083—Shipping
- G06Q10/0835—Relationships between shipper or supplier and carriers
- G06Q10/08355—Routing methods
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Game Theory and Decision Science (AREA)
- Educational Administration (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention discloses a logistics planning method, equipment, a device and a storage medium, wherein the method comprises the following steps: acquiring the leveling requirement of the pickup stream, and performing regional division on the pickup points to acquire partition information; if the logistics leveling requirements comprise the cargo quantity and position leveling requirements, acquiring an initial transportation route, and grouping the transportation routes according to the cargo quantity and position leveling requirements, the initial transportation route and the partition information to obtain an optimal transportation route; if the logistics leveling requirements comprise time leveling requirements, carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement; and generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement. Through the logistics supply chain system and the logistics supply chain method, the logistics supply chain efficiency can be improved.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to a logistics planning method, equipment, a device and a storage medium.
Background
The market environment that domestic automobile manufacturing enterprises face at present is more and more complicated changeable, and the trade competition is more and more intense, and the pressure that inside cost optimization and managerial efficiency promote is bigger and bigger. How to optimize and improve the management efficiency of the internal supply chain and flexibly and quickly face to complex internal and external competition and environmental pressure is an important subject faced by most domestic automobile manufacturing enterprises.
From the current situation, new logistics intelligent technologies (internet of things, big data and the like) are mature day by day, logistics express enterprises such as Jingdong, Shunfeng, vegetable and bird and the like are led in and practiced first, and a high-efficiency and high-quality intelligent logistics supply chain sample plate is created. Against the automobile industry, the intelligent development of a supply chain is still not as advanced as the logistics express industry, and the overall efficiency of the intelligent logistics development is relatively low. The problems of low logistics efficiency, high cost and the like of the existing automobile manufacturing enterprises are caused.
Disclosure of Invention
The invention provides a logistics planning method, equipment, a device and a storage medium, and aims to solve the technical problem of low logistics efficiency of automobile manufacturing enterprises.
In order to achieve the above object, the present invention provides a logistics planning method, which comprises the following steps:
acquiring the leveling requirement of the pickup stream, and performing regional division on the pickup points to acquire partition information;
if the logistics leveling requirements comprise the cargo quantity and position leveling requirements, acquiring an initial transportation route, and grouping the transportation routes according to the cargo quantity and position leveling requirements, the initial transportation route and the partition information to obtain an optimal transportation route;
if the logistics leveling requirements comprise time leveling requirements, carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement;
and generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement.
Optionally, position information and volume information of the goods pick-up points are obtained, and the goods pick-up points are divided into areas according to the position information and the volume information of the goods to obtain partition information.
Optionally, performing iterative optimization on the initial transportation route according to the partition information through a heuristic algorithm to obtain a plurality of local optimal transportation routes;
scoring the plurality of locally optimal transportation routes and selecting an optimal transportation route from the plurality of locally optimal transportation routes based on the scoring result.
Optionally, transportation costs of the plurality of local optimal transportation routes are respectively obtained, and the plurality of local optimal transportation routes are respectively scored according to the transportation costs.
Optionally, calculating time intervals of each time in the optimal transportation route according to the time leveling requirement;
calculating ideal unloading time corresponding to each platform of the unloading point according to the time interval;
obtaining an optimal time arrangement based on the ideal unloading time.
Optionally, sequentially judging whether time conflicts exist in the ideal unloading time corresponding to each station;
and if the station platforms without time conflicts are detected to exist, setting the ideal unloading time corresponding to the station platforms without time conflicts as the optimal time arrangement.
Optionally, if there is no station where no time conflict occurs, recording total duration of conflict corresponding to all stations;
setting the station with the minimum total conflict duration as an actual unloading station;
and obtaining the ideal unloading time corresponding to the actual unloading platform as the optimal time arrangement.
In order to achieve the above object, the present application further provides a logistics planning apparatus, which includes: the system comprises a region division module, a transportation line grouping module, a transportation time arrangement module and a logistics plan generation module, wherein the region division module is used for acquiring the leveling requirement of the pickup stream and performing region division on the pickup points to acquire partition information; the transportation route grouping module is used for acquiring an initial transportation route if the logistics leveling requirements comprise a cargo quantity and a position leveling requirement, and grouping the transportation routes according to the cargo quantity and position leveling requirement, the initial transportation route and the partition information to obtain an optimal transportation route; the transportation time arrangement module is used for carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement if the logistics standardized requirements comprise time standardized requirements, and the logistics plan generation module is used for generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement.
In order to achieve the above object, the present application further provides a logistics planning apparatus, which includes a memory, a processor, and a logistics planning program stored in the memory and operable on the processor, wherein the logistics planning program, when executed by the processor, implements the logistics planning method.
In order to achieve the above object, the present application further provides a computer-readable storage medium, in which a logistics planning program is stored, and the logistics planning program, when executed by a processor, implements the logistics planning method.
According to the method, the area division is firstly carried out on the goods picking point to obtain the partition information, then if the object flow leveling needs to meet the position and the load flow leveling, iterative optimization is carried out on the initial transportation route according to the load flow and the position leveling requirements and the partition information to obtain the optimal transportation route, then after the optimal transportation route is determined, if the object flow leveling needs to meet the time leveling, the transportation time arrangement is carried out on the basis of the optimal transportation route to obtain the optimal time arrangement, the optimal logistics plan is generated according to the optimal transportation route and the optimal time arrangement, and compared with the requirement that the object flow is not leveled in the prior art, the optimal logistics plan in the method can be used for improving the efficiency of a logistics supply chain by carrying out flexible leveling selection on the object flow.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the structures shown in the drawings without creative efforts.
Fig. 1 is a schematic block diagram of a logistics planning method according to an embodiment of the invention;
FIG. 2 is a flow chart of a logistics planning method according to an embodiment of the present invention;
fig. 3 is a schematic block structure diagram of a logistics planning method according to an embodiment of the invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Referring to fig. 1, fig. 1 is a schematic diagram of a hardware structure of a logistics planning apparatus provided in each embodiment of the present invention. The logistics planning device comprises an execution module 01, a memory 02, a processor 03, a battery system and the like. Those skilled in the art will appreciate that the apparatus shown in fig. 1 may also include more or fewer components than those shown, or combine certain components, or a different arrangement of components. The processor 03 is connected to the memory 02 and the execution module 01, respectively, a logistics planning program is stored in the memory 02, and the logistics planning program is executed by the processor 03 at the same time.
The execution module 01 can acquire the logistics standardization demand and perform regional division on the goods picking point to obtain the partition information; if the logistics leveling requirements comprise the cargo quantity and position leveling requirements, acquiring an initial transportation route, and grouping the transportation routes according to the cargo quantity and position leveling requirements, the initial transportation route and the partition information to obtain an optimal transportation route; if the logistics leveling requirements comprise time leveling requirements, carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement; and generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement. And simultaneously feeds back the above information to the processor 03.
The memory 02 may be used to store software programs and various data. The memory 02 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for a plurality of functions, and the like; the storage data area may store data or information created according to the use of the internet of things terminal, or the like. Further, the memory 02 may include high speed random access memory, and may also include non-volatile memory, such as a plurality of magnetic disk storage devices, flash memory devices, or other volatile solid state storage devices.
The processor 03, which is a control center of the processing platform, is connected to each part of the whole internet of things terminal by using various interfaces and lines, and executes various functions and processing data of the internet of things terminal by running or executing software programs and/or modules stored in the memory 02 and calling data stored in the memory 02, thereby integrally monitoring the logistics planning equipment. Processor 03 may include one or more processing units; preferably, the processor 03 may integrate an application processor, which mainly handles operating systems, user interfaces, application programs, etc., and a modem processor, which mainly handles wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 03.
Those skilled in the art will appreciate that the logistics planning apparatus configuration shown in fig. 1 does not constitute a limitation of the apparatus and may include more or fewer components than those shown, or some components in combination, or a different arrangement of components.
Various embodiments of the method of the present invention are presented in terms of the above-described hardware architecture.
At present, the market environment of domestic automobile manufacturing enterprises faces is more and more complex and changeable, the industry competition is more and more intense, and the pressure of internal cost optimization and management efficiency improvement is more and more great. How to optimize and improve the management efficiency of the internal supply chain and flexibly and quickly face to complex internal and external competition and environmental pressure is an important subject faced by most domestic automobile manufacturing enterprises.
From the current situation, new logistics intelligent technologies (internet of things, big data and the like) are mature day by day, logistics express enterprises such as Jingdong, Shunfeng, vegetable and bird and the like are led in and practiced first, and a high-efficiency and high-quality intelligent logistics supply chain sample plate is created. Against the automobile industry, the intelligent development of a supply chain is still not as advanced as the logistics express industry, and the overall efficiency of the intelligent logistics development is relatively low. The problems of low logistics efficiency, high cost and the like of the existing automobile manufacturing enterprises are caused.
Specifically, in the prior art, the production of the automobile manufacturing enterprises has the characteristic of leveling, so-called leveling production is a production plan for evenly distributing production tasks from the aspects of both yield and product combination, and products are not produced according to customer orders which may have fluctuating heights and falls. And the part logistics is for production service, and if the production requirement is leveling, the goods taking and the goods receiving of the logistics are arranged according to leveling production. At present, no existing or developing logistics planning system for production parts in the market covers or relates to a 'leveling' function, so that no method which can be directly used in an automobile manufacturing enterprise exists, and a logistics planning system logic which meets the leveling requirement needs to be newly constructed.
In order to solve the above problem, the present application provides a logistics planning method, and referring to fig. 2, in a first embodiment of the logistics planning method of the present invention, the logistics planning method includes:
s100, acquiring a logistics standardization demand, and performing area division on a goods picking point to obtain partition information;
in this embodiment, the stream leveling can be split into position leveling, cargo quantity leveling, and time leveling. The position leveling refers to that goods need to be picked from the same part supplier in the same order every time in the same line group; the quantity standardization refers to that in the same route group, the goods which are taken from the same part supplier at each time need to be the same type of goods with the same quantity; the time-flattening refers to that the time intervals in the same line group are equal.
The 'leveling' considered by the system comprises three conditions: firstly, complete leveling, namely leveling of positions and cargo volumes and leveling of time are met; secondly, the position and the cargo quantity are required to be leveled, and the time is not required to meet the leveling; thirdly, the leveling principle is not required to be met, and the position, the cargo quantity and the time are not required to be met. The concrete adopted leveling situation depends on the logistics leveling requirements of users.
After the logistics leveling requirement is acquired, the pick-up point information of the pick-up points in the logistics plan is acquired, and the area division is carried out on each pick-up point based on the pick-up point information, so that each pick-up point is divided into different partitions, and the partition information corresponding to each pick-up point is acquired.
Step S200, if the logistics leveling requirements comprise a cargo quantity and a position leveling requirement, acquiring an initial transportation route, and grouping the transportation routes according to the cargo quantity and position leveling requirement, the initial transportation route and the partition information to obtain an optimal transportation route;
in this embodiment, if the logistics leveling needs to meet the location and cargo leveling, the transportation route of the logistics needs to be optimized. Specifically, a path optimization module is required to be called, and based on the partition information, continuous iterative optimization is performed on the initial transportation route and the initial goods delivery scheme along the route through a heuristic algorithm, so that the optimal transportation route is obtained.
Step S300, if the logistics leveling requirements comprise time leveling requirements, carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement;
and S400, generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement.
In this embodiment, if the demand for logistics leveling needs to meet not only the location and cargo leveling but also the time leveling, that is, the demand for logistics leveling includes the demand for time leveling, the unloading plan is further planned according to the time leveling principle on the basis of obtaining the optimal transportation route through calculation, so as to obtain the optimal time arrangement.
According to the method and the device, the area division is firstly carried out on the goods picking point to obtain the partition information, then if the leveling of the goods flow needs to meet the leveling of the position and the quantity of goods, the iteration optimization is carried out on the initial transportation route according to the leveling requirements of the quantity of goods and the position and the partition information to obtain the optimal transportation route, then after the optimal transportation route is determined, if the leveling of the goods flow needs to meet the leveling of the time, the transportation time arrangement is carried out on the basis of the optimal transportation route to obtain the optimal time arrangement. Compared with the requirement of not standardizing the logistics in the prior art, the logistics supply chain efficiency can be improved by flexibly and standardizing the logistics.
In an embodiment, the step of obtaining the partition information by dividing the pickup point into regions includes:
and acquiring position information and volume information of the goods at the goods pick-up points, and carrying out region division on the goods pick-up points according to the position information and the volume information of the goods at the goods pick-up points to obtain partition information.
In this embodiment, before performing route optimization, the pick-up point of the supplier needs to be divided into a plurality of areas. Specifically, the suppliers are split into multiple regions according to the prior knowledge and spatial clustering of the optimization objective. In the aspect of driving mileage, the pickup points with similar spatial positions are mainly divided into subareas, so that the loading rate of the driving mileage can be effectively reduced, and the loading rate of the subareas can be effectively improved by mainly considering that the order goods volume of the pickup points in the same subarea is closer to the integral multiple of the vehicle loading capacity. And by considering the spatial position and the loading rate, a clustering method is adopted to realize the function of region division.
Furthermore, in one embodiment, the pick-up points within the same preset range size are divided into a first area according to the pick-up point position information; the goods picking point position information comprises a goods picking point position which refers to a position where a supplier for picking goods is located; the predetermined range may be a circle with any radius or a square or rectangle with any size, and the size and shape of the range are not limited in the present invention. Specifically, the pick-up points in the same circle, square or rectangle are divided into first areas. It should be noted that there may be a plurality of the first regions.
After the position information of the goods picking points is obtained, calculating the total volume of goods of all the goods picking points in the first area according to the goods picking point goods volume information; the pick-up point goods volume information comprises the total volume of pick-up point goods and the volume of single pick-up point goods. In this embodiment, the total volume of the goods at the pick-up point in the first area may be added to obtain the total volume of the goods.
Then, the number of the goods picking points in the first area can be changed according to the total goods volume and the preset vehicle loading capacity, and the maximum loading rate in the first area is calculated; in the present embodiment, the preset vehicle load amount refers to the total load amount of the transportation vehicles, for example, when there is one transportation vehicle, the total load amount is the load amount of one transportation vehicle; when there are 3 transport vehicles, the overall load is the load of 3 transport vehicles. Changing the number of pick-up points in the first area refers to increasing or decreasing the number of pick-up points at the boundary of the first area, namely, putting the pick-up points at the boundary into other adjacent areas, or adding the pick-up points from the adjacent areas into the first area. The maximum loading rate in the first region is calculated once every time the number of the pick-up points is increased or decreased, specifically, the multiple of the total volume of the goods in the first region and the preset vehicle loading capacity, that is, 6 of the total volume of the goods and the preset vehicle loading capacity, can be calculated, and when the total volume of the goods is closer to the integral multiple of the preset vehicle loading capacity, the loading rate is larger.
Finally, the first region corresponding to the maximum loading rate may be set as the target divided region. In this embodiment, when the maximum loading rate in the first area is larger, it indicates that the vehicle can be filled with the vehicle in every transportation, so that the maximum loading rate of the vehicle is ensured, the waste of transportation resources is avoided, and the cost is reduced. The target division area is an area for transporting and picking up the goods by the vehicle, and the target division area can be a plurality of target division areas.
In an embodiment, the step of grouping the transportation routes according to the cargo volume and location leveling requirement, the initial transportation route and the partition information to obtain an optimal transportation route includes:
performing iterative optimization on the initial transportation route according to the partition information through a heuristic algorithm to obtain a plurality of local optimal transportation routes;
scoring the plurality of locally optimal transportation routes and selecting an optimal transportation route from the plurality of locally optimal transportation routes based on the scoring result.
In this embodiment, if the logistics leveling needs to meet the location and cargo leveling, iterative optimization needs to be performed on the initial transportation route according to the partition information to obtain a plurality of local optimal transportation routes. The method comprises the steps of firstly carrying out picking point grouping according to the maximum loading rate in a region to obtain a plurality of partition information, then determining distribution times according to the partition information, secondly carrying out route planning according to the geographical position of each picking point by taking the minimum mileage as a target, finally comprehensively evaluating the cost of output results, searching for a better route group based on a self-research operation optimization solver, and carrying out iterative optimization. Wherein, the local optimal transportation route refers to the transportation route with the lowest cost when only part of suppliers in the transportation route or part of logistics influence parameters are considered.
Further, in an embodiment, a specific iterative optimization process is as follows: and performing optimization search by adopting a domain search algorithm, designing record update of the intermediate result state by adopting an ant colony algorithm pheromone record update method, and jumping out of a local optimal transportation route by adopting a delay receiving algorithm. Wherein the intermediate result state comprises material flow influence parameters, such as loading rate and the like.
After the plurality of local optimal transportation routes are obtained, the optimal transportation routes are scored by calling a scoring module, and the local optimal transportation route with the highest score is selected from the plurality of local optimal transportation routes according to a scoring result to serve as the optimal transportation route.
In one embodiment, the step of scoring a plurality of the locally optimal transportation routes comprises:
and respectively obtaining the transportation cost of the local optimal transportation routes, and respectively scoring the local optimal transportation routes according to the transportation cost.
In this embodiment, the transportation cost is the only evaluation criterion for measuring the score corresponding to each local optimal transportation route, that is, the lower the transportation cost is, the higher the score corresponding to the local optimal transportation route is; the lower the transportation cost, the lower the score for the locally optimal transportation route. Because the plurality of local optimal transportation routes all meet the demand of the leveling of the cargo quantity and the position, the local optimal transportation route with the highest score is the transportation route with the lowest cost which meets the demand of the leveling of the cargo quantity and the position. And in the process of scoring each local optimal transportation route based on the transportation cost, different calculations are carried out on the transportation cost according to different unloading points.
Specifically, if the unloading point is a relay, such as a storage warehouse, the transportation cost is equal to the hard cost in the logistics transportation process; if the point of discharge is a physical entry point, such as a manufacturing plant, the cost of transportation is equal to the sum of the hard cost and the soft cost of the logistics transportation process.
In an embodiment, the step of arranging the transportation time according to the optimal transportation route to obtain an optimal time arrangement includes:
calculating time intervals of each time in the optimal transportation route according to the time leveling requirement;
calculating ideal unloading time corresponding to each platform of the unloading point according to the time interval;
obtaining an optimal time arrangement based on the ideal unloading time.
In this embodiment, since there are a limited number of platforms at the unloading point for unloading, there are a limited number of vehicles at each unloading point that can simultaneously perform the unloading task, and therefore the unloading time needs to be readjusted based on the optimal unloading route according to the number of platforms. Specifically, the time-leveled planning module may plan a specific unloading plan for the optimal transportation route with respect to the route group and the platform corresponding to the relay site or the single physical ingress frequency sub-segment according to the time-leveled principle. In one embodiment, the specific planning steps are as follows:
firstly, selecting an optimal transportation route according to the number of times of defecation from more to less, and calculating the time interval of each defecation of the route group according to a time leveling principle;
secondly, the number of each platform of the unloading point is present, unloading is tried from the platform with the lower number, and unloading is completed at the same platform in the whole optimal transportation route each time. The optimal discharge time of the first time is calculated. The optimal unloading time is the earliest unloading time with the smallest conflict of the unloading times in the optimal transport route on the premise that the last convenient unloading can be still finished. After the optimal unloading time of the first time is obtained, the unloading time of each time is calculated according to the time interval and is used as the ideal unloading time.
And thirdly, after the ideal unloading time corresponding to the optimal transportation route is obtained through calculation, the optimal time arrangement can be further obtained on the basis of the ideal unloading time.
In one embodiment, the step of obtaining the optimal time arrangement based on the ideal discharge time comprises:
sequentially judging whether time conflicts exist in the ideal unloading time corresponding to each station;
and if the station platforms without time conflicts are detected to exist, setting the ideal unloading time corresponding to the station platforms without time conflicts as the optimal time arrangement.
In this embodiment, there are multiple stations at each unloading point, and each station has an ideal unloading time corresponding to the optimal transportation route. Sequentially judging whether unloading is carried out according to the ideal unloading time corresponding to each platform, and whether time conflicts exist; if the platform without time conflict is detected, namely if the platform is unloaded, all vehicles in the optimal transport route are unloaded according to the ideal unloading time, and the time conflict cannot be generated in the unloading process, the platform is directly used as the actual unloading platform, and the ideal unloading time is distributed as the optimal time.
In one embodiment, the step of sequentially determining whether there is a time conflict between the ideal off-loading times corresponding to each station further comprises:
if the stations which do not have time conflict do not exist, recording the total conflict duration corresponding to all the stations;
setting the station with the minimum total conflict duration as an actual unloading station;
and obtaining the ideal unloading time corresponding to the actual unloading platform as the optimal time arrangement.
In this embodiment, if all vehicles in the optimal transportation route are unloaded according to the ideal unloading time, and a time conflict is generated in the unloading process, a leveling time conflict processing module needs to be called to perform conflict processing. Specifically, the leveling time conflict processing module needs to readjust the unloading time of the vehicle, and if there is no time conflict after the unloading time is adjusted, the adjusted unloading time output by the platform is selected as the optimal time arrangement to obtain the unloading time; and if the time conflict still exists, recording the total number of the time conflicts and the total time length of the time conflicts. The next station is then tried to repeat the above steps. If the time conflict cannot be avoided after all the stations are tried, the unloading station and the unloading plan with the minimum time conflict are selected as the final optimal time arrangement of the line group. The optimization target with the minimum conflict is a two-level target, the first priority considers the minimum total number of conflict orders, and the second priority considers the minimum total duration of conflict.
As shown in fig. 3, the present invention further provides a logistics planning apparatus, which includes:
the area division module A10 is used for acquiring the leveling requirement of the pick-up stream and carrying out area division on the pick-up point to acquire partition information;
a transportation route grouping module a20, configured to obtain an initial transportation route if the logistics leveling requirement includes a cargo volume and a location leveling requirement, and group transportation routes according to the cargo volume and location leveling requirement, the initial transportation route, and the partition information to obtain an optimal transportation route;
and the transportation time arrangement module A30 is configured to, if the logistics leveling requirement includes a time leveling requirement, perform transportation time arrangement according to the optimal transportation route to obtain an optimal time arrangement.
And the logistics plan generating module A40 is used for generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement.
The invention further provides logistics planning equipment, which comprises a memory, a processor and a logistics planning program stored on the memory and capable of running on the processor, wherein the logistics planning program is used for executing the method of each embodiment of the invention.
The invention also proposes a computer-readable storage medium on which a logistics planning program is stored. The storage medium includes a computer-readable storage medium, which may be the Memory in fig. 1, and may also be at least one of a ROM (Read-Only Memory)/RAM (Random Access Memory), a magnetic disk, and an optical disk, where the storage medium includes several instructions to enable an internet of things terminal device (which may be a mobile phone, a computer, a server, an internet of things terminal, or a network device) having a processor to execute the method according to each embodiment of the present invention.
In the present invention, the terms "first", "second", "third", "fourth" and "fifth" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance, and those skilled in the art can understand the specific meanings of the above terms in the present invention according to specific situations.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in multiple embodiments or examples of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Although the embodiment of the present invention has been shown and described, the scope of the present invention is not limited thereto, it should be understood that the above embodiment is illustrative and not to be construed as limiting the present invention, and that those skilled in the art can make changes, modifications and substitutions to the above embodiment within the scope of the present invention, and that these changes, modifications and substitutions should be covered by the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method of logistics planning, said method comprising the steps of:
acquiring the leveling requirement of the pickup stream, and performing regional division on the pickup points to acquire partition information;
if the logistics leveling requirements comprise the cargo quantity and position leveling requirements, acquiring an initial transportation route, and grouping the transportation routes according to the cargo quantity and position leveling requirements, the initial transportation route and the partition information to obtain an optimal transportation route;
if the logistics leveling requirements comprise time leveling requirements, carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement;
and generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement.
2. The logistics planning method of claim 1 wherein said step of obtaining zoning information by zoning said pick-up points comprises:
and acquiring position information and volume information of the goods at the goods pick-up points, and carrying out region division on the goods pick-up points according to the position information and the volume information of the goods at the goods pick-up points to obtain partition information.
3. The logistics planning method of claim 1, wherein the step of grouping the transportation routes according to the cargo volume and location leveling requirement, the initial transportation route and the zoning information to obtain an optimal transportation route comprises:
performing iterative optimization on the initial transportation route according to the partition information through a heuristic algorithm to obtain a plurality of local optimal transportation routes;
scoring the plurality of locally optimal transportation routes and selecting an optimal transportation route from the plurality of locally optimal transportation routes based on the scoring results.
4. The logistics planning method of claim 3, wherein said step of scoring a plurality of said locally optimal transportation routes comprises:
and respectively obtaining the transportation cost of the local optimal transportation routes, and respectively scoring the local optimal transportation routes according to the transportation cost.
5. The logistics planning method of claim 1, wherein said step of arranging the transportation time according to the optimal transportation route to obtain the optimal time arrangement comprises:
calculating time intervals of each time in the optimal transportation route according to the time leveling requirement;
calculating ideal unloading time corresponding to each platform of the unloading point according to the time interval;
obtaining an optimal time arrangement based on the ideal unloading time.
6. The logistics planning method of claim 5 wherein said step of deriving an optimal time arrangement based on said ideal discharge time comprises:
sequentially judging whether time conflicts exist in the ideal unloading time corresponding to each station;
and if the station platforms without time conflicts are detected to exist, setting the ideal unloading time corresponding to the station platforms without time conflicts as the optimal time arrangement.
7. The method according to claim 6, wherein the step of sequentially determining whether there is a time conflict between the ideal discharge times for each station further comprises:
if the stations which do not have time conflict do not exist, recording the total conflict duration corresponding to all the stations;
setting the station with the minimum total conflict duration as an actual unloading station;
and obtaining the ideal unloading time corresponding to the actual unloading platform as the optimal time arrangement.
8. A logistics planning apparatus, comprising:
the area division module is used for acquiring the leveling requirement of the goods taking flow and carrying out area division on the goods taking points to acquire partition information;
the transportation route grouping module is used for acquiring an initial transportation route if the logistics leveling requirements comprise a cargo quantity and a position leveling requirement, and grouping the transportation routes according to the cargo quantity and position leveling requirement, the initial transportation route and the partition information to obtain an optimal transportation route;
the transportation time arrangement module is used for carrying out transportation time arrangement according to the optimal transportation route to obtain optimal time arrangement if the logistics standardized requirements comprise time standardized requirements;
and the logistics plan generating module is used for generating an optimal logistics plan according to the optimal transportation route and the optimal time arrangement.
9. A logistics planning apparatus comprising a memory, a processor, and a logistics planning program stored on the memory and executable on the processor, the logistics planning program when executed by the processor implementing the steps of the logistics planning method of any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the storage medium has stored thereon a logistics planning program, which when executed by a processor implements the steps of the logistics planning method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210225592.1A CN114548880A (en) | 2022-03-07 | 2022-03-07 | Logistics planning method, equipment, device and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210225592.1A CN114548880A (en) | 2022-03-07 | 2022-03-07 | Logistics planning method, equipment, device and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114548880A true CN114548880A (en) | 2022-05-27 |
Family
ID=81662889
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210225592.1A Pending CN114548880A (en) | 2022-03-07 | 2022-03-07 | Logistics planning method, equipment, device and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114548880A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114881536A (en) * | 2022-06-13 | 2022-08-09 | 一汽物流(佛山)有限公司 | Logistics vehicle leveling management method |
| CN117252496A (en) * | 2023-03-09 | 2023-12-19 | 江苏齐博冷链科技有限公司 | Regional intelligent logistics coordination system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005189925A (en) * | 2003-12-24 | 2005-07-14 | Toyota Motor Corp | Physical distribution cost prediction device, prediction method, and program for it |
| CN110060007A (en) * | 2019-03-28 | 2019-07-26 | 国能新能源汽车有限责任公司 | New-energy automobile production components supplying managing and control system, method and device |
-
2022
- 2022-03-07 CN CN202210225592.1A patent/CN114548880A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2005189925A (en) * | 2003-12-24 | 2005-07-14 | Toyota Motor Corp | Physical distribution cost prediction device, prediction method, and program for it |
| CN110060007A (en) * | 2019-03-28 | 2019-07-26 | 国能新能源汽车有限责任公司 | New-energy automobile production components supplying managing and control system, method and device |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114881536A (en) * | 2022-06-13 | 2022-08-09 | 一汽物流(佛山)有限公司 | Logistics vehicle leveling management method |
| CN117252496A (en) * | 2023-03-09 | 2023-12-19 | 江苏齐博冷链科技有限公司 | Regional intelligent logistics coordination system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109034481B (en) | Constraint programming-based vehicle path problem modeling and optimizing method with time window | |
| Guo et al. | Dynamic yard crane dispatching in container terminals with predicted vehicle arrival information | |
| CN114548880A (en) | Logistics planning method, equipment, device and storage medium | |
| Roy et al. | A non-linear traffic flow-based queuing model to estimate container terminal throughput with AGVs | |
| US20130159208A1 (en) | Shipper-oriented logistics base optimization system | |
| KR20220059531A (en) | Cold chain delivery routing method, device, server and storage medium | |
| CN114415610B (en) | Scheduling method and device for robot, electronic equipment and storage medium | |
| CN112884409A (en) | Distribution route recommendation method, device, equipment and storage medium | |
| CN115619198B (en) | Library displacement dynamic programming method, device, electronic equipment and storage medium | |
| CN116562738B (en) | Intelligent freight dispatching method, device, equipment and storage medium | |
| CN114881564A (en) | Multi-deep goods location allocation method and device, computer equipment and storage medium | |
| Wang et al. | Pickup and delivery of automobiles from warehouses to dealers | |
| CN109359905B (en) | Automatic unmanned warehouse goods space allocation method and device and storage medium | |
| CN114529241A (en) | Highway fractional freight logistics path planning algorithm based on operational research theory | |
| CN114596029A (en) | Logistics planning method, logistics planning system and computer-readable storage medium | |
| CN112418475A (en) | Logistics path planning method and device, electronic equipment and storage medium | |
| CN115705593A (en) | Logistics transportation method and device, computer equipment and storage medium | |
| López-Plata et al. | Minimizing the Waiting Times of block retrieval operations in stacking facilities | |
| CN110930092B (en) | Distribution route adjusting method and device, electronic equipment and storage medium | |
| CN114429333A (en) | Logistics planning method, equipment, device and storage medium | |
| CN114580928A (en) | Logistics planning method, equipment, device and storage medium | |
| CN115375237A (en) | Cold-chain logistics intelligent management method, system, equipment and storage medium | |
| CN115062868A (en) | Pre-polymerization type vehicle distribution path planning method and device | |
| CN112036794A (en) | Engineering material warehousing method, system and device for cost control | |
| Blom et al. | Inventory routing for defense: Moving supplies in adversarial and partially observable environments |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |