CN115062850A - Logistics distribution business optimization method and system based on Internet of things technology - Google Patents
Logistics distribution business optimization method and system based on Internet of things technology Download PDFInfo
- Publication number
- CN115062850A CN115062850A CN202210702547.0A CN202210702547A CN115062850A CN 115062850 A CN115062850 A CN 115062850A CN 202210702547 A CN202210702547 A CN 202210702547A CN 115062850 A CN115062850 A CN 115062850A
- Authority
- CN
- China
- Prior art keywords
- commodity
- information
- distribution
- logistics
- risk
- 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
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
- G06Q10/04—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
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- 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
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Human Resources & Organizations (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Marketing (AREA)
- Development Economics (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 provides a method and a system for optimizing logistics distribution business based on the technology of Internet of things, and relates to the technical field of logistics planning, wherein the method comprises the following steps: the system generates a primary distribution route according to distribution request information sent by a buyer, carries out logistics transportation risk analysis on purchased commodity information in the distribution request and outputs purchase supplement recommendation information, the buyer selects a substitute supplement commodity to carry out logistics risk avoidance, and a logistics optimized distribution route is generated according to the purchased commodity information and distribution position information after partial commodities are replaced. The technical problems that in the prior art, logistics transportation risks and purchasing demands of purchasers cannot be balanced, damage risks exist in commodity transportation, and purchasing satisfaction of the purchasers is low are solved. The technical effects that the commodity logistics safety degree is improved, and the actual commodity purchasing of a purchaser is higher in consistency with the purchasing plan are achieved.
Description
Technical Field
The invention relates to the technical field of logistics planning, in particular to a method and a system for optimizing logistics distribution business based on the technology of the Internet of things.
Background
With the development of the internet of things technology and modern logistics, the logistics cost of online purchased commodities and the calling transportation cost of bulk commodities are greatly reduced, but when many existing logistics systems respond to the purchasing requirement of a buyer, the consideration of the logistics cost and the logistics safety is often emphasized, a high-price transportation scheme is customized or transportation is rejected for commodities with special transportation requirements or commodities with transportation risks, so that the purchasing requirement of the buyer cannot be met, or special commodity transportation within a loss limit is carried out through a universal transportation mode, and unnecessary transportation loss is generated.
The technical problems that the logistics transportation risk and the commodity purchasing demand of a purchaser are difficult to balance in the logistics transportation process, so that the damage risk exists in the commodity transportation and the purchasing satisfaction of the purchaser is low exist in the prior art.
Disclosure of Invention
The application provides a logistics distribution business optimization method and system based on the technology of the Internet of things, which are used for solving the technical problems that in the prior art, logistics transportation risks and purchasing demands of purchasers are difficult to balance in the logistics transportation process, so that damage risks exist in commodity transportation, and purchasing satisfaction of the purchasers is low.
In view of the above problems, the present application provides a method and a system for optimizing logistics distribution services based on the technology of internet of things.
In a first aspect of the present application, a method for optimizing logistics distribution services based on internet of things technology is provided, where the method includes: acquiring distribution request information, wherein the distribution request information comprises purchased commodity information and distribution position information; generating a primary distribution route according to the distribution request information; carrying out logistics transportation risk analysis according to the purchased commodity information, and outputting a distribution risk analysis result; outputting purchasing supplement recommendation information according to the distribution risk analysis result; selecting the purchase supplement recommendation information to obtain a purchase substitute supplement result; generating purchasing commodity optimization information according to the purchasing substitution supplement result and the purchasing commodity information; and optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route.
In a second aspect of the present application, there is provided a system for optimizing logistics distribution services based on internet of things, the system including: the distribution request obtaining module is used for obtaining distribution request information, wherein the distribution request information comprises purchased commodity information and distribution position information; the distribution route generating module is used for generating a primary distribution route according to the distribution request information; the distribution risk analysis module is used for carrying out logistics transportation risk analysis according to the purchased commodity information and outputting a distribution risk analysis result; the supplementary commodity obtaining module is used for outputting purchase supplementary recommendation information according to the distribution risk analysis result; the supplementary commodity selection module is used for selecting the purchase supplement recommendation information to obtain a purchase substitute supplement result; the purchasing optimization generation module is used for generating purchasing commodity optimization information according to the purchasing substitution supplement result and the purchasing commodity information; and the distribution route optimization module is used for optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
the method provided by the embodiment of the application comprises the steps of receiving distribution request information comprising information of purchased commodities and distribution position information, and generating a primary distribution route according to the distribution request information; providing in-transit time sequence information for subsequent distribution risk analysis, carrying out logistics transportation risk analysis according to the purchased commodity information, outputting a distribution risk analysis result, realizing the determination of distribution risk commodities, outputting purchase supplement recommendation information based on the distribution risk analysis result, providing a larger purchase selection space for a logistics demand party, improving the purchase satisfaction of the logistics demand party, and selecting the purchase supplement recommendation information to obtain a purchase substitute supplement result; and generating purchasing commodity optimization information according to the purchasing substitute supplement result and the purchasing commodity information, providing performing warehouse change information for a primary distribution route which is determined by subsequent optimization, and optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route. The technical effects of improving the commodity logistics safety degree, ensuring higher consistency between the actual purchased commodities of the buyer and the purchasing plan and improving the purchasing satisfaction degree of the buyer are achieved.
Drawings
Fig. 1 is a schematic flow chart of a method for optimizing logistics distribution services based on the technology of the internet of things according to the present application;
fig. 2 is a schematic flow chart illustrating a primary distribution route generated in the method for optimizing logistics distribution services based on the internet of things technology provided by the present application;
fig. 3 is a schematic flow chart illustrating that purchasing supplement recommendation information is output in the method for optimizing logistics distribution services based on the internet of things technology according to the present application;
fig. 4 is a schematic structural diagram of an optimization system of logistics distribution services based on the technology of the internet of things.
Description of reference numerals: a distribution request obtaining module 11, a distribution route generating module 12, a distribution risk analyzing module 13, a supplementary product obtaining module 14, a supplementary product selecting module 15, a purchase optimization generating module 16, and a distribution route optimizing module 17.
Detailed Description
The application provides a logistics distribution business optimization method and system based on the technology of the Internet of things, which are used for solving the technical problems that in the prior art, logistics transportation risks and purchasing demands of purchasers are difficult to balance in the logistics transportation process, so that damage risks exist in commodity transportation, and purchasing satisfaction of the purchasers is low.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
generating a distribution route according to purchase information and distribution position information sent by a buyer, carrying out logistics transportation risk analysis on the purchased commodity information in the distribution request according to the in-transit time of various commodities in the distribution route and the attributes of the commodities, carrying out purchase supplement recommendation on the buyer according to the analysis result, selecting a substitute supplement commodity by the buyer to carry out logistics risk avoidance, and optimizing the original distribution route according to the purchased commodity information and the distribution position information after replacing part of commodities. The technical effects of improving the commodity logistics safety degree, ensuring higher consistency between the actual purchased commodities of the buyer and the purchasing plan and improving the purchasing satisfaction degree of the buyer are achieved.
Example one
As shown in fig. 1, the present application provides a method for optimizing a logistics distribution service based on an internet of things technology, where the method is applied to a system for optimizing a logistics distribution service based on an internet of things technology, and the method includes:
s100, acquiring distribution request information, wherein the distribution request information comprises purchased commodity information and distribution position information;
specifically, in this embodiment, the logistics transportation activity at least includes a logistics supply end, a logistics demand end and a logistics service providing end, the logistics demand end sends the delivery request information to the logistics supply end, and the delivery request information includes specific required commodity information and a receiving address of the required commodity, that is, the purchased commodity information and the delivery location information. And the logistics supply end delivers the distribution request information to the logistics service providing end for collecting and distributing the commodities so as to realize the commodity demand of the logistics demand end.
It should be understood that the delivery location information is based on a literal surface provided by a logistics demand end, and the delivery location information has no correlation with the current location of the logistics demand end, such as an Ali Barr delivery system. The specific commodity categories in the purchased commodity information have diversity and can be respectively stored in multiple warehouses according to commodity storage characteristics so as to avoid damage risks in commodity storage.
S200, generating a primary distribution route according to the distribution request information;
further, as shown in fig. 2, a primary distribution route is generated according to the distribution request information, and the method step S200 provided by the present application further includes:
s210, obtaining a priority level performance range and an auxiliary level performance range;
s220, traversing warehouse big data by taking the distribution position information as a circle center and the priority level fulfillment range and the auxiliary level fulfillment range as constraint conditions to obtain a distribution warehouse set;
s230, collecting dynamic storage information of the distribution warehouse set in real time;
s240, classifying the types of the articles based on the information of the purchased commodities to obtain a purchase classification result set;
s250, traversing the dynamic storage information based on the purchase classification result set to obtain a plurality of priority level fulfillment warehouses and a plurality of auxiliary level fulfillment warehouses which meet fulfillment delivery conditions;
and S260, generating the primary distribution route based on the distance relationship between the plurality of the priority level fulfillment warehouses and the auxiliary level fulfillment warehouses and the distribution position information.
Particularly, for the proper storage of the commodity with diversified storage requirements, the commodity loss caused by improper storage is avoided, and meanwhile, the commodity storage type and the storage capacity meet the requirements of a logistics demand end, the logistics transportation warehousing mode of multi-warehouse combined delivery is constructed in the embodiment.
In this embodiment, the distribution location information is used to divide the warehouse call priorities, a specific warehouse call priority division rule is that two concentric circular rings with different radiuses are set around the distribution location as the center of circle, the circular ring with shorter radius length is the priority performance range, the distribution warehouse within the priority performance range preferentially performs resource call and warehouse delivery, the circular region formed by the circular ring with longer radius length and the circular ring with shorter radius length is the auxiliary performance range, and the distribution warehouse within the auxiliary performance range is used to perform auxiliary resource call when the distribution warehouse within the priority performance range does not have an adjustable resource or when the travel path of the carrier vehicle consumes oil when performing resource call through a plurality of distribution warehouses within the priority performance range singly.
In this embodiment, with the distribution position information as a center of circle and the priority fulfillment range and the auxiliary fulfillment range as constraint conditions, traverse warehouse big data to obtain the distribution warehouse set, where the distribution warehouse set includes a priority fulfillment warehouse set and an auxiliary fulfillment warehouse set. And acquiring dynamic storage information of the goods distribution warehouse set in real time, wherein the dynamic storage information is commodity type information stored in each goods distribution warehouse and inventory information of each commodity type.
The method comprises the steps of classifying the types of the purchased commodities according to the commodity types, obtaining a purchasing classification result set, generating a retrieval instruction according to the purchasing classification result set, traversing the dynamic storage information, obtaining a plurality of priority fulfillment warehouses and a plurality of auxiliary fulfillment warehouses which are used for adding the commodity types and the commodity quantity and meet fulfillment delivery conditions, generating a plurality of delivery routes according to the distance relationship between the plurality of priority fulfillment warehouses and the auxiliary fulfillment warehouses and the delivery position information, enabling a carrying vehicle to pass through the plurality of fulfillment warehouses on the basis of each delivery route, scheduling commodities in the plurality of fulfillment warehouses in a travel route, and collecting the purchased commodity information required by a sufficient commodity flow demand end. And analyzing the oil consumption and the time consumption of the plurality of distribution routes, and outputting the distribution route with the shortest oil consumption and time consumption as the primary distribution route.
S300, carrying out logistics transportation risk analysis according to the purchased commodity information, and outputting a distribution risk analysis result;
further, according to the information of the purchased goods, performing logistics transportation risk analysis, and outputting a distribution risk analysis result, the method provided by the application, in step S300, further includes:
s310, obtaining time sequence information of a plurality of priority fulfillment warehouses and a plurality of auxiliary fulfillment warehouses in the primary distribution route, and obtaining a commodity in-transit time set;
s320, traversing the time sequence information of the fulfillment warehouse to obtain the commodity attribute information of the purchased commodity information, wherein the commodity attribute information comprises commodity storage condition information, commodity transportation condition information and commodity quality guarantee time limit information;
and S330, respectively carrying out logistics risk analysis from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity in-transit time information, and outputting the distribution risk analysis result.
Further, the step S330 of the method provided by the present application further includes performing logistics risk analysis from the commodity storage condition information, the commodity transportation condition information, the commodity shelf life information, and the commodity transit time information, and outputting the distribution risk analysis result, where the method further includes:
s331, carrying out logistics risk analysis respectively from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity time-in-transit information to obtain commodity storage risk, commodity transportation risk, quality guarantee time limit risk and time-in-transit risk;
s332, respectively calculating the influence weights of the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk on the logistics risk;
and S333, carrying out weighting processing on the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk based on the influence weight, and outputting a distribution risk analysis result.
Specifically, in this embodiment, the purchased article information is obtained by performing resource calls through a plurality of fulfillment warehouses. The primary distribution route is the shortest time-consuming and shortest oil-consuming running route of the carrying vehicle, and the carrying vehicle loads the carrying vehicles through a plurality of fulfillment warehouses one by one according to the vehicle skill distribution route planning and is continuously close to the geographic position corresponding to the distribution position information.
According to the sequence of time before and after a delivery vehicle passes through a plurality of fulfillment warehouses in the middle of the primary distribution route, the present embodiment obtains the fulfillment warehouse time sequence information of a plurality of the preferred fulfillment warehouses and a plurality of the auxiliary fulfillment warehouses in the primary distribution route, and obtains the in-transit transportation time of each type of goods according to the fulfillment warehouse time sequence information to form the goods in-transit time set.
Traversing the sequence information of the fulfillment warehouses to obtain specific commodity information called out by each fulfillment warehouse, and identifying the commodity attribute information of the commodity called out by each fulfillment warehouse through a radio frequency identification technology (RFID), wherein the commodity attribute information comprises commodity storage condition information, commodity transportation condition information and commodity quality guarantee time limit information, the commodity storage condition information is storage conditions, such as cool environment storage, subzero environment storage and the like, of the commodity which does not deteriorate within the quality guarantee period, the commodity transportation condition information is transportation conditions, such as stable transportation, no air transportation and the like, of the commodity which does not deteriorate within the quality guarantee period, and the commodity quality guarantee time limit mainly refers to a time interval when the edible commodity or the consumable commodity does not undergo natural oxidation or decay.
Specifically, the distribution risk is based on the characteristics that different commodities have different transportation requirements and quality guarantee periods and the fact that the commodities dispatched from different distribution warehouses are transported to different distribution positions at different time-in-transit, and the risk that the actual value of the commodities is reduced or the value of the commodities is completely lost after the commodities reach the distribution positions due to the interaction of the time-in-transit, the commodity production time and the commodity quality guarantee periods is caused.
The commodity storage risk is the risk of deterioration caused by improper shipment and storage within the guarantee period, the commodity transportation risk is the risk of deterioration caused by improper transportation method within the guarantee period, the guarantee period risk is the risk of commodity overdue in the operation process caused by improper dispatching of transportation and storage warehouses, the time-in-transit risk is the risk of commodity arrival at a distribution position after superposition of the time-in-transit of the commodity, the production time and the guarantee period, and the commodity sales value is reduced due to the fact that the guarantee period is close.
Carrying out logistics risk analysis respectively from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity time-in-transit information to obtain commodity storage risk, commodity transportation risk, quality guarantee time limit risk and time-in-transit risk;
it should be understood that different types of commodities are affected by the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity time-in-transit information due to different commodity storage condition information, commodity transportation condition information, the commodity quality guarantee time limit information and the commodity time-in-transit information, so that the commodity value is reduced to different degrees after the commodities arrive at the distribution position, and the influence weights of the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk on the logistics risk are calculated respectively.
Specifically, the weight values for weight distribution of different types of commodity risk information may be obtained by directly using the weight values provided in the prior art as weight distribution results, or by using methods such as an analytic hierarchy process, a fuzzy method, a fuzzy analytic hierarchy process, and an expert evaluation method to obtain the weight values corresponding to different pet basic information as weight distribution results. The risk parameter values of different types of commodity risk information can be evaluated by referring to the risk parameter values provided by the prior art.
And weighting risk parameters of the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk based on the influence weight to obtain distribution risk values of all commodities contained in the purchased commodity information.
And presetting qualified score lines, wherein the qualified score lines can be set according to actual conditions, and the embodiment is not limited herein. The higher than the qualification score line shows that the corresponding purchased commodity has no risk of commodity value reduction in the transportation process, and the delivery of the delivery warehouse can be normally carried out to call the delivery from the warehouse; the lower than the qualification score line indicates that the corresponding purchased commodity has the risk of commodity value reduction in the transportation process, and the commodity distribution cannot be carried out according to the original distribution plan. And obtaining a plurality of purchased commodities which are lower than the preset qualification score line to form a distribution risk commodity set, and outputting the distribution risk commodity set as a distribution risk analysis result.
According to the embodiment, the logistics transportation risk analysis is carried out by obtaining the attribute information of the purchased commodities and the corresponding ex-warehouse information, the commodity information with the transportation risk is determined by combining weight analysis, the problem that the value loss of the purchased commodities which do not meet the transportation conditions in the transportation process is caused, the logistics accidents of the logistics supplier are caused, the purchasing satisfaction of the logistics demand supplier is reduced, the logistics transportation risk is avoided, and the technical effect of reducing the logistics loss is achieved.
S400, outputting purchasing supplement recommendation information according to the distribution risk analysis result;
further, as shown in fig. 3, purchasing supplement recommendation information is output according to the distribution risk analysis result, and step S400 of the method provided by the present application further includes:
s410, acquiring a distribution risk commodity set according to the distribution risk analysis result;
s420, acquiring the commodity attribute information and the commodity in-transit time information of each delivery risk commodity in the delivery risk commodity set;
s430, acquiring commodity name information of each delivery risk commodity in the delivery risk commodity set;
s440, carrying out similarity correlation analysis according to the commodity name information to obtain a correlated commodity set;
s450, screening the associated commodity set according to the commodity attribute information and the commodity in-transit time information of each delivered risky commodity in the delivered risky commodity set to obtain an optimized associated commodity set;
s460, traversing the plurality of priority level fulfillment warehouses and the plurality of auxiliary level fulfillment warehouses according to the optimized associated commodity set to obtain a recommended associated commodity set;
and S470, outputting the purchase supplement recommendation information according to the recommendation associated commodity set.
In order to improve the purchase satisfaction of the logistics demand party, the embodiment obtains the associated goods with the similarity to the transportation risk goods for recommendation, and performs alternative selection for the logistics demand party. And obtaining a commodity set with delivery risks according to the delivery risk analysis result. Obtaining item name information for each delivered at-risk item in the set of delivered at-risk items, the item name information including an item artistic name and/or an item type name, such as chocolate bars and caloric chocolate foods, nested coffee and instant solid coffee drinks.
And performing semantic similarity correlation analysis according to the commodity name information to obtain a correlated commodity set with similarity to the currently distributed risky commodities, wherein each distributed risky commodity in the distributed risky commodities has a plurality of correlated commodities to form the correlated commodity set.
And acquiring commodity attribute information and commodity time-in-transit information of each delivery risk commodity in the delivery risk commodity set by using the RFID technology in cooperation with the Internet of things, generating a screening condition instruction to screen the associated commodity set, screening out the commodity attribute information and delivering the associated commodities of which the time-in-transit is inferior to the relevance to the delivery risk commodity in the step S200, and acquiring an optimized associated commodity set, wherein the associated commodities in the optimized associated commodity set are superior to the risk delivery commodities in terms of commodity attribute and time-in-transit.
Traversing the plurality of priority level fulfillment warehouses and the plurality of auxiliary level fulfillment warehouses according to the optimized associated commodity set, obtaining associated commodity information which currently has inventory in the fulfillment warehouses and the quantity of the same associated commodities distributed in the plurality of fulfillment warehouses meets the quantity information of the commodities at risk of delivery, generating the recommended associated commodity set, and outputting the purchasing supplement recommendation information according to the recommended associated commodity set.
In the embodiment, the associated commodities with the distribution risk are determined by performing semantic similarity association analysis on the commodities with the distribution risk, the associated commodities with the distribution risk lower than the current distribution risk are obtained by combining the commodity attributes and the in-transit time, and the commodity recommendation which can be recommended to a logistics demand party for replacing the purchased commodities is determined by combining whether the stock of the associated commodities in a fulfillment warehouse is equivalent to the stock of the original purchased distribution risk commodities, so that the technical effects of improving the purchase satisfaction degree of the logistics demand party and providing a larger purchase selection space for the logistics demand party are achieved.
S500, selecting the purchase supplement recommendation information to obtain a purchase substitute supplement result;
s600, generating optimized information of the purchased commodities according to the purchase replacement supplement result and the information of the purchased commodities;
specifically, after obtaining the risk delivery commodity list and the associated commodity recommendation list based on the system information, the logistics demander may initiate deletion of the risk delivery commodity purchase request or replacement of the associated commodity. The selection behavior of the logistics demand side may cause the increase, decrease or no change of the fulfillment warehouse, and the corresponding primary distribution route may have partial route change.
And the logistics demander selects the purchase supplement recommendation information to obtain a purchase replacement supplement result, replaces or deletes the delivery risk commodities in the purchased commodity information according to the purchase replacement supplement result to generate the purchased commodity optimization information, and each commodity in the purchased commodity optimization information avoids the logistics risk.
And S700, optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route.
Further, the primary distribution route is optimized according to the optimized information of the purchased goods and the distribution position information, and a logistics optimized distribution route is generated, in which the method step S700 further includes:
s710, building a logistics planning generation model, wherein the logistics planning generation model comprises the following Agent entities:
the master control Agent is used for coordinating all the agents;
the transfer judging Agent is used for determining a fulfillment warehouse within a fulfillment range;
the resource scheduling Agent is used for carrying out resource allocation scheduling on the fulfillment warehouses in combination with the allocation judging Agent, and the sum of the resource allocation of each fulfillment warehouse forms the optimization information of the purchased commodities;
the order management Agent is used for generating order information by combining with the resource scheduling Agent and sending the order information to the fulfillment warehouse, and the fulfillment warehouse responds to the order information and carries out inventory calling and packaging;
the traffic management Agent is used for calling the road condition information in the fulfillment range in real time and providing the traffic road information for determining the logistics optimization distribution route;
the distribution management Agent is used for determining the time sequence information of the fulfillment warehouse;
and S720, inputting the optimized information of the purchased commodities, the distribution position information and the primary distribution route into the logistics planning generation model to generate the logistics optimized distribution route.
Specifically, the logistics planning generation model of this embodiment is a multi-Agent intelligent decision model, and all the agents in the logistics planning generation model have unique ID numbers as Agent identification identifiers, so as to implement traceability of task allocation and information management, and the logistics planning generation model includes the following Agent entities:
the main control Agent is used for carrying out task allocation on each Agent module and coordinating the cooperation problem among the agents so as to realize the optimization of the primary distribution route and obtain the logistics optimized distribution route with the least time consumption and the least oil consumption of a carrying vehicle;
the transfer judging Agent is used for determining the fulfillment warehouse in the priority fulfillment range and the auxiliary fulfillment range according to the dynamic storage information;
the resource scheduling Agent is used for carrying out resource allocation scheduling on the fulfillment warehouses in combination with the fulfillment warehouses obtained by the allocation judging Agent, and the sum of the resources called by the fulfillment warehouses is realized to form the optimization information of the purchased goods;
the order management Agent is used for generating order information by combining the resource scheduling result of the resource scheduling Agent, sending the order information to the fulfillment warehouse, prompting the fulfillment warehouse to respond to the order information, and carrying out automatic or manual inventory calling and packaging;
the traffic management Agent is used for calling the road condition information in the fulfillment range in real time and providing the traffic road information for determining the logistics optimization distribution route;
and the distribution management Agent is used for determining the time sequence information of the fulfillment warehouse and generating the logistics optimization distribution route in cooperation with the traffic management Agent.
In this embodiment, the optimized information of the purchased goods, the distribution position information and the primary distribution route are input into the logistics planning generation model, and task allocation is performed on each Agent module and cooperation between each Agent is coordinated based on the master Agent, so that the primary distribution route is optimized, and the logistics optimized distribution route which consumes the least time and fuel for the carrying vehicle is obtained.
In the embodiment, the logistics planning generation model based on multi-Agent negotiation is constructed, and the selection of the logistics distribution path is realized through the negotiation among the agents under the determined commodity distribution requirement, so that the technical effects of optimizing the primary distribution planning path to obtain the optimal distribution path and distributing the purchased commodities are achieved.
The method provided by the embodiment comprises the steps of receiving distribution request information comprising purchased commodity information and distribution position information, and generating a primary distribution route according to the distribution request information; providing in-transit time sequence information for subsequent distribution risk analysis, carrying out logistics transportation risk analysis according to the purchased commodity information, outputting a distribution risk analysis result, realizing the determination of distribution risk commodities, outputting purchase supplement recommendation information based on the distribution risk analysis result, providing a larger purchase selection space for a logistics demand party, improving the purchase satisfaction of the logistics demand party, and selecting the purchase supplement recommendation information to obtain a purchase substitute supplement result; and generating purchasing commodity optimization information according to the purchasing substitute supplement result and the purchasing commodity information, providing performing warehouse change information for a primary distribution route which is determined by subsequent optimization, and optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route. The technical effects of improving the commodity logistics safety degree, ensuring higher consistency between the actual purchased commodities of the buyer and the purchasing plan and improving the purchasing satisfaction degree of the buyer are achieved.
Further, the method provided by the present application further includes:
s810, obtaining road condition information of a distribution route according to the logistics optimization distribution route;
s820, evaluating the quality of road conditions according to the road condition information of the distribution route to obtain the restriction information of the road conditions for carrying;
s830, determining the logistics carrying volume according to the optimization information of the purchased commodities;
s840, obtaining commodity transportation and storage condition information according to the purchased commodity optimization information;
s850, evaluating the transport storage severity according to the commodity transport storage condition information to obtain commodity transport limitation information;
s860, selecting logistics carrying vehicles according to the carrying road condition limitation information and the logistics carrying volume;
s870, according to the commodity transportation limit information, the temperature debugging and the oxygen content debugging of the storage space of the logistics carrying vehicle are carried out.
Specifically, in this embodiment, the distribution route road condition information is information about lengths of running road segments of a plurality of roads and information about a degree of jolt of the plurality of roads, which are obtained by using a method for evaluating a road jolt level, for a plurality of traffic roads expected to pass by a logistics vehicle (vehicle) when the vehicle runs according to the logistics optimized distribution route.
The method comprises the steps of sequencing the lengths of the running road sections of a plurality of roads to obtain the length of the longest running road section, sequencing the bumping degrees of the plurality of roads to obtain the bumping limit of the road sections, and obtaining the carried road condition limiting information according to the length of the longest running road section and the bumping limit of the road sections, wherein the carried road condition limiting information represents the longest distance data capable of running on the most bumpy running road section.
And obtaining the commodity category and the quantity of the commodity finally subjected to the logistics transportation according to the purchased commodity optimization information, and determining the packaging volume during the logistics transportation according to the commodity category and the quantity so as to determine the logistics transportation volume, wherein the logistics transportation volume determines which carrying capacity vehicle is selected for the logistics transportation of the commodity.
The commodity transportation and storage condition information is the temperature, humidity, oxygen content and transportation stability requirements of the commodity on the carrying space in the transportation process, and each purchasing category is determined according to the purchasing commodity optimization information to obtain the commodity transportation and storage condition information.
The commodity transportation limit information is the lowest environmental oxygen content requirement, the lowest temperature requirement, the lowest humidity requirement and the lowest transportation stability requirement of the stable transportation of commodities in the commodities transported in the same batch.
And the evaluation of the transport storage severity according to the commodity transport storage condition information is to sort the oxygen content, the temperature, the humidity and the transport stability of the transport environment of each commodity to obtain the commodity transport limitation information.
According to delivery road conditions restriction information with the commodity circulation delivery volume carries out the selection of commodity circulation delivery vehicle, carries out through the thing networking commodity transportation restriction information's transmission carries out commodity circulation delivery vehicle's memory space temperature debugging and memory space oxygen content debugging and carry out commodity circulation delivery vehicle memory space debugging result accuracy verification through temperature sensor, oxygen content sensor, humidity transducer, make commodity circulation delivery vehicle's transport performance and on-vehicle environment satisfy the safe transportation requirement of each type of purchase commodity in the purchase commodity optimization information.
The embodiment carries out commodity circulation road conditions goodness and badness judgement according to the commodity circulation route, carries out commodity circulation delivery environment requirement analysis and delivery volume according to the delivery commodity, carries out commodity circulation delivery vehicle's affirmation and vehicle delivery environment debugging according to road conditions, delivery volume and delivery environment requirement, has reached and has obtained better commodity circulation delivery vehicle and carry out the commodity circulation transportation, further reduces the technical effect that commodity circulation in-process commodity damaged the risk.
Example two
Based on the same inventive concept as the method for optimizing logistics distribution business based on internet of things technology in the foregoing embodiment, as shown in fig. 4, the present application provides a system for optimizing logistics distribution business based on internet of things technology, wherein the system includes:
a distribution request obtaining module 11, configured to receive distribution request information sent by a buyer by a logistics provider, where the distribution request information includes information of a purchased commodity and distribution position information;
a distribution route generating module 12, configured to generate a primary distribution route according to the distribution request information;
the distribution risk analysis module 13 is used for carrying out logistics transportation risk analysis according to the purchased commodity information and outputting a distribution risk analysis result;
a supplementary commodity obtaining module 14, configured to output purchase supplementary recommendation information according to the distribution risk analysis result;
a supplementary commodity selection module 15, configured to select the purchase supplement recommendation information to obtain a purchase replacement supplement result;
a purchase optimization generation module 16, configured to generate purchase commodity optimization information according to the purchase replacement supplement result and the purchase commodity information;
and the distribution route optimization module 17 is configured to optimize the primary distribution route according to the optimized information of the purchased goods and the distribution position information, and generate a logistics optimized distribution route.
Further, the delivery route generation module 12 further includes:
a performance scope obtaining unit, configured to obtain a preferred level performance scope and an auxiliary level performance scope;
a fulfillment warehouse obtaining unit, which traverses the warehouse big data by taking the distribution position information as a circle center and the priority fulfillment range and the auxiliary fulfillment range as constraint conditions to obtain a distribution warehouse set;
the warehousing dynamic acquisition unit is used for acquiring dynamic storage information of the distribution warehouse set in real time;
the purchasing classification execution unit is used for classifying the types of the articles based on the purchasing commodity information to obtain a purchasing classification result set;
a performing warehouse screening unit, configured to traverse the dynamic storage information based on the purchase classification result set, and obtain multiple priority performing warehouses and multiple auxiliary level performing warehouses that satisfy performing distribution conditions;
a distribution route generating unit, configured to generate the primary distribution route based on distance relationships between the plurality of priority fulfillment warehouses and the auxiliary level fulfillment warehouses and the distribution location information.
Further, the distribution risk analysis module 13 further includes:
the in-transit time calculation unit is used for obtaining the time sequence information of the fulfillment warehouses of the plurality of priority fulfillment warehouses and the plurality of auxiliary fulfillment warehouses in the primary distribution route and obtaining a commodity in-transit time set;
the commodity attribute acquisition unit is used for traversing the time sequence information of the fulfillment warehouse to acquire commodity attribute information of the purchased commodity information, wherein the commodity attribute information comprises commodity storage condition information, commodity transportation condition information and commodity quality guarantee time limit information;
and the distribution risk analysis unit is used for carrying out logistics risk analysis and outputting a distribution risk analysis result from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity in-transit time information.
Further, the distribution risk analysis unit further includes:
the logistics risk analysis unit is used for respectively carrying out logistics risk analysis on the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity time-in-transit information to obtain commodity storage risks, commodity transportation risks, quality guarantee time limit risks and time-in-transit risks;
the risk weight assignment unit is used for respectively calculating the influence weights of the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk on the logistics risk;
and the risk weight calculation unit is used for performing weighting processing on the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk based on the influence weight and outputting the distribution risk analysis result.
Further, the supplementary merchandise acquisition module 14 further includes:
the risk commodity determining unit is used for obtaining a distribution risk commodity set according to the distribution risk analysis result;
a risk information acquisition unit, configured to obtain the commodity attribute information and the commodity transit time information of each delivered risky commodity in the delivered risky commodity set;
the commodity name acquisition unit is used for acquiring commodity name information of each delivery risk commodity in the delivery risk commodity set;
the correlation analysis execution unit is used for carrying out similarity correlation analysis according to the commodity name information to obtain a correlation commodity set;
the related product screening unit is used for screening the related commodity set according to the commodity attribute information and the commodity in-transit time information of each distributed risky commodity in the distributed risky commodity set to obtain an optimized related commodity set;
a recommended product obtaining unit, configured to traverse the multiple priority performing warehouses and the multiple auxiliary performing warehouses according to the optimized associated commodity set to obtain a recommended associated commodity set;
and the purchase supplement determining unit is used for outputting the purchase supplement recommendation information according to the recommendation related commodity set.
Further, the delivery route optimization module 17 further includes:
the planning model building unit is used for building a logistics planning generation model, and the logistics planning generation model comprises the following Agent entities:
the master control Agent is used for coordinating all the agents;
the transfer judging Agent is used for determining a fulfillment warehouse within a fulfillment range;
the resource scheduling Agent is used for carrying out resource allocation scheduling on the fulfillment warehouses in combination with the allocation judging Agent, and the sum of the resource scheduling of each fulfillment warehouse forms the optimization information of the purchased commodities;
the order management Agent is used for generating order information by combining the resource scheduling Agent and sending the order information to the fulfillment warehouse, and the fulfillment warehouse responds to the order information and carries out inventory calling and packaging;
the traffic management Agent is used for calling the road condition information in the fulfillment range in real time and providing the traffic road information for determining the logistics optimization distribution route;
the distribution management Agent is used for determining the time sequence information of the fulfillment warehouse;
and the optimized route generating unit is used for inputting the optimized information of the purchased commodities, the distribution position information and the primary distribution route into the logistics planning and generating model and generating the logistics optimized distribution route.
Further, the system further comprises:
the distribution road condition obtaining unit is used for obtaining distribution route road condition information according to the logistics optimization distribution route;
the road condition restriction generating unit is used for evaluating the road condition according to the road condition information of the distribution route to obtain the carried road condition restriction information;
the carrying volume calculation unit is used for determining the logistics carrying volume according to the optimized information of the purchased commodities;
the transportation environment determining unit is used for obtaining the information of the commodity transportation and storage conditions according to the optimized information of the purchased commodities;
the environment limitation determining unit is used for evaluating the transport storage severity according to the commodity transport storage condition information to obtain commodity transport limitation information;
the carrying vehicle selection unit is used for selecting the logistics carrying vehicles according to the carrying road condition limitation information and the logistics carrying volume;
and the delivery environment debugging unit is used for debugging the temperature of the storage space and the oxygen content of the storage space of the logistics delivery vehicle according to the commodity transportation limit information.
Any of the methods or steps described above may be stored as computer instructions or programs in various non-limiting types of computer memory that are recognized by various non-limiting types of computer processors to implement any of the methods or steps described above.
Based on the above embodiments of the present invention, those skilled in the art should make any improvements and modifications to the present invention without departing from the principle of the present invention, and therefore, the present invention should fall into the protection scope of the present invention.
Claims (8)
1. The method for optimizing the logistics distribution business based on the Internet of things technology is applied to a system for optimizing the logistics distribution business based on the Internet of things technology, and comprises the following steps:
acquiring distribution request information, wherein the distribution request information comprises purchased commodity information and distribution position information;
generating a primary distribution route according to the distribution request information;
carrying out logistics transportation risk analysis according to the purchased commodity information, and outputting a distribution risk analysis result;
outputting purchase supplement recommendation information according to the distribution risk analysis result;
selecting the purchase supplement recommendation information to obtain a purchase substitute supplement result;
generating purchasing commodity optimization information according to the purchasing substitution supplement result and the purchasing commodity information;
and optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route.
2. The method of claim 1, wherein generating a primary delivery route based on the delivery request information comprises:
obtaining a priority level performance range and an auxiliary level performance range;
traversing warehouse big data by taking the distribution position information as a circle center and the priority level fulfillment range and the auxiliary level fulfillment range as constraint conditions to obtain a distribution warehouse set;
collecting dynamic storage information of the distribution warehouse set in real time;
classifying the types of the articles based on the information of the purchased commodities to obtain a purchase classification result set;
traversing the dynamic storage information based on the purchase classification result set to obtain a plurality of priority level fulfillment warehouses and a plurality of auxiliary level fulfillment warehouses which meet fulfillment delivery conditions;
generating the primary delivery route based on distance relationships between a plurality of the priority fulfillment warehouses and the auxiliary level fulfillment warehouses and the delivery location information.
3. The method of claim 2, wherein the analyzing the risk of the logistics transportation according to the information of the purchased goods and outputting the analysis result of the distribution risk comprises:
obtaining the time sequence information of the fulfillment warehouses of the plurality of priority fulfillment warehouses and the plurality of auxiliary fulfillment warehouses in the primary distribution route, and obtaining a commodity in-transit time set;
traversing the fulfillment warehouse time sequence information to obtain commodity attribute information of the purchased commodity information, wherein the commodity attribute information comprises commodity storage condition information, commodity transportation condition information and commodity quality guarantee time limit information;
and carrying out logistics risk analysis and outputting the distribution risk analysis result from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity in-transit time information.
4. The method according to claim 3, wherein the performing logistics risk analysis and outputting the distribution risk analysis result from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee period information, and the commodity time-in-transit information, respectively, comprises:
carrying out logistics risk analysis respectively from the commodity storage condition information, the commodity transportation condition information, the commodity quality guarantee time limit information and the commodity time-in-transit information to obtain commodity storage risk, commodity transportation risk, quality guarantee time limit risk and time-in-transit risk;
respectively calculating the influence weight of the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk on the logistics risk;
and carrying out weighting processing on the commodity storage risk, the commodity transportation risk, the quality guarantee time limit risk and the time-in-transit risk based on the influence weight, and outputting the distribution risk analysis result.
5. The method of claim 3, wherein outputting procurement replenishment recommendation information based on the delivery risk analysis result comprises:
obtaining a distribution risk commodity set according to the distribution risk analysis result;
obtaining the commodity attribute information and the commodity time-in-transit information of each delivery risk commodity in the delivery risk commodity set;
obtaining commodity name information of each delivery risk commodity in the delivery risk commodity set;
performing similarity correlation analysis according to the commodity name information to obtain a correlated commodity set;
screening the associated commodity set according to the commodity attribute information and the commodity transit time information of each distributed risky commodity in the distributed risky commodity set to obtain an optimized associated commodity set;
traversing the plurality of priority level fulfillment warehouses and the plurality of auxiliary level fulfillment warehouses according to the optimized associated commodity set to obtain a recommended associated commodity set;
and outputting the purchasing supplement recommendation information according to the recommended associated commodity set.
6. The method of claim 1, wherein optimizing the primary delivery route based on the purchased good optimization information and the delivery location information to generate a logistics optimized delivery route comprises:
building a logistics planning generation model, wherein the logistics planning generation model comprises the following Agent entities:
the master control Agent is used for coordinating all the agents;
the transfer judging Agent is used for determining a fulfillment warehouse within a fulfillment range;
the resource scheduling Agent is used for carrying out resource allocation scheduling on the fulfillment warehouses in combination with the allocation judging Agent, and the sum of the resource allocation of each fulfillment warehouse forms the optimization information of the purchased commodities;
the order management Agent is used for generating order information by combining the resource scheduling Agent and sending the order information to the fulfillment warehouse, and the fulfillment warehouse responds to the order information and carries out inventory calling and packaging;
the traffic management Agent is used for calling the road condition information in the fulfillment range in real time and providing the traffic road information for determining the logistics optimization distribution route;
the distribution management Agent is used for determining the time sequence information of the fulfillment warehouse;
and inputting the optimized information of the purchased commodities, the distribution position information and the primary distribution route into the logistics planning generation model to generate the logistics optimized distribution route.
7. The method according to claim 1, characterized in that it comprises:
acquiring the road condition information of a distribution route according to the logistics optimization distribution route;
evaluating the road condition according to the road condition information of the distribution route to obtain the carried road condition restriction information;
determining the logistics carrying volume according to the optimized information of the purchased commodities;
acquiring commodity transportation and storage condition information according to the purchased commodity optimization information;
carrying out transport storage severity evaluation according to the commodity transport storage condition information to obtain commodity transport limitation information;
selecting logistics carrying vehicles according to the carrying road condition limitation information and the logistics carrying volume;
and debugging the temperature of the storage space and the oxygen content of the storage space of the logistics carrying vehicle according to the commodity transportation limit information.
8. The logistics distribution business optimization system based on the technology of the Internet of things is characterized by comprising the following components:
the distribution request obtaining module is used for obtaining distribution request information, wherein the distribution request information comprises purchased commodity information and distribution position information;
the distribution route generating module is used for generating a primary distribution route according to the distribution request information;
the distribution risk analysis module is used for carrying out logistics transportation risk analysis according to the purchased commodity information and outputting a distribution risk analysis result;
the supplementary commodity obtaining module is used for outputting purchase supplementary recommendation information according to the distribution risk analysis result;
the supplementary commodity selection module is used for selecting the purchasing supplementary recommendation information to obtain a purchasing substitute supplementary result;
the purchasing optimization generation module is used for generating purchasing commodity optimization information according to the purchasing substitution supplement result and the purchasing commodity information;
and the distribution route optimization module is used for optimizing the primary distribution route according to the purchasing commodity optimization information and the distribution position information to generate a logistics optimization distribution route.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210702547.0A CN115062850A (en) | 2022-06-21 | 2022-06-21 | Logistics distribution business optimization method and system based on Internet of things technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210702547.0A CN115062850A (en) | 2022-06-21 | 2022-06-21 | Logistics distribution business optimization method and system based on Internet of things technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115062850A true CN115062850A (en) | 2022-09-16 |
Family
ID=83201763
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210702547.0A Pending CN115062850A (en) | 2022-06-21 | 2022-06-21 | Logistics distribution business optimization method and system based on Internet of things technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115062850A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542592A (en) * | 2023-07-03 | 2023-08-04 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN117875724A (en) * | 2024-03-12 | 2024-04-12 | 深圳市晟晟科技有限公司 | Purchasing risk management and control method and system based on cloud computing |
-
2022
- 2022-06-21 CN CN202210702547.0A patent/CN115062850A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116542592A (en) * | 2023-07-03 | 2023-08-04 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN116542592B (en) * | 2023-07-03 | 2023-10-10 | 深圳市华运国际物流有限公司 | International logistics transportation path analysis and assessment system |
| CN117875724A (en) * | 2024-03-12 | 2024-04-12 | 深圳市晟晟科技有限公司 | Purchasing risk management and control method and system based on cloud computing |
| CN117875724B (en) * | 2024-03-12 | 2024-06-14 | 深圳市晟晟科技有限公司 | Purchasing risk management and control method and system based on cloud computing |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rios et al. | Recent dynamic vehicle routing problems: A survey | |
| CN115062850A (en) | Logistics distribution business optimization method and system based on Internet of things technology | |
| Mahar et al. | Using online pickup site inclusion policies to manage demand in retail/E-tail organizations | |
| Nasiri et al. | Incorporating supplier selection and order allocation into the vehicle routing and multi-cross-dock scheduling problem | |
| US20050165629A1 (en) | Systems and methods for planning the delivery of goods | |
| Fredriksson et al. | Assessing consequences of low‐cost sourcing in China | |
| Vazquez-Noguerol et al. | An optimisation approach for the e-grocery order picking and delivery problem | |
| Bopage et al. | A strategic model to improve the last mile delivery performance in e-commerce parcel delivery | |
| CN110060013B (en) | Method and device for processing orders to be distributed | |
| Dadouchi et al. | Recommender systems as an agility enabler in supply chain management | |
| Gu et al. | A sequential approach for a multi-commodity two-echelon distribution problem | |
| Xu et al. | System for enhancing supply chain agility through exception handling | |
| Costantino et al. | Multi-criteria logistics distribution network design for mass customisation | |
| Schary | A concept of customer service | |
| Saghiri et al. | Grocery omnichannel perishable inventories: performance measures and influencing factors | |
| Chen et al. | A cost perspective on the location of value-added logistics services in supply chains | |
| CN112016871A (en) | Order dispatching method, device, equipment and storage medium | |
| CN115641047A (en) | Delivery routing system and e-commerce management system | |
| Van Asperen et al. | Centrality, flexibility and floating stocks: A quantitative evaluation of port-of-entry choices | |
| Hwang et al. | Reverse channel selection for commercial product returns under time-to-market and product value considerations | |
| Wallach | Reducing wave cycle time at a multi-channel distribution center | |
| Honarvar et al. | A Particle Swarm Optimization Algorithm for Solving Pricing and Lead Time Quotation in a Dual‐Channel Supply Chain with Multiple Customer Classes | |
| JP2005060108A (en) | Physical distribution management system | |
| Siregar et al. | Analysis of Service Delivery Improvement of Manufactured Products with Lean Method Management | |
| Rizzi | Representation of the supply chain through service flows |
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 |