CN110659839A - Intelligent logistics stowage scheduling method - Google Patents
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Abstract
The invention discloses a logistics intelligent loading and scheduling method, which comprises the following steps: s1: grouping the goods, namely grouping the goods to be loaded at the current station according to the goods direction; s2: preferentially extracting goods with express signs, and preferentially extracting a loading list; s3: calculating to form a list, comprehensively calculating the volume, weight and value of the goods in one direction to form a list M to be distributed, and arranging and combining the list M; s4: calculating an optimal list; s5: calculating a vehicle distance and a vehicle volume; s6: forming a vehicle list; s7: obtaining a vehicle; s8: vehicle loading; s9: selecting the next vehicle; s10: and recording the vehicle loading. According to the logistics intelligent allocation scheduling method, the allocation rate and the average kilometer benefit of a single vehicle are improved by means of positioning information of the vehicle, GPS navigation software, attributes of various goods, vehicle load and the like, and by means of analysis of early-stage configuration data and a reasonable allocation algorithm.
Description
Technical Field
The invention relates to the technical field of computer logistics distribution, in particular to a logistics intelligent distribution scheduling method.
Background
Cargo stowage is a core link in logistics operation, and plays an important role in improving the service level of the whole enterprise and reducing the logistics cost. The freight loading problem is to solve the problems of low transportation efficiency, serious no-load phenomenon and the like of the current road freight. New technologies such as cloud computing and big data are used, the vehicle loading rate is optimal, the transportation time is short, and therefore the maximum economic benefit is obtained. The efficient matching of the transport vehicle and the goods is realized, and the empty running rate is reduced.
Along with the continuous increase of freight volume, each logistics company, storage delivery company begin to expand the capacity to lead to the increase of self operation cost, how accurate expansion, allotment capacity, let every car reach the biggest profit become the problem that each commodity circulation and delivery company need solve, the delivery mode in current logistics garden is according to the volume of total freight order volume, presumably estimates and uses vehicle type and quantity, the shipment of coming car. The method ensures the delivery rate of the goods, but the delivery cost is increased due to the fact that the vehicle is not full of goods and the delivery distance between the goods is too long because of the lack of accurate measurement and calculation and the classification and planning of the route, and therefore a more scientific method for matching and matching the goods and planning the route is needed, the delivery rate of the goods is guaranteed, meanwhile, unnecessary transport capacity waste in the delivery process can be well reduced, and the profit rate of a logistics company is improved.
Therefore, a logistics intelligent allocation scheduling method is provided.
Disclosure of Invention
1. Technical problem to be solved
Aiming at the problems in the prior art, the invention aims to provide a logistics intelligent loading scheduling method, which can analyze and calculate the loading requirements of all goods to be loaded, such as volume, weight and the like, and adjustable vehicles, calculate the most suitable transport vehicle, do not waste the load of the vehicle, automatically provide a goods loading scheme with the maximum loading capacity and the highest economic value, obtain the maximum loading capacity and the maximum economic value, screen out the optimal scheme of loading and loading through an artificial intelligence repeated iteration algorithm under the same loading and volume limitations, ensure that the transportation cost is reduced to the maximum extent while the arrival time of the goods is not delayed by selecting the optimal path for distribution, maximize the transportation profit of a truck, minimize the number of storage tickets and avoid overloading, combine the attributes of various goods and the attributes of the vehicle such as the load by means of the positioning information of the vehicle and GPS navigation software, through the analysis of the early-stage configuration data, the load allocation rate and the average kilometer benefit of a single vehicle are improved by means of a reasonable load allocation algorithm, through data analysis, the intelligent load allocation algorithm is adopted to combine a single phase and is compared with an artificial combination list, and the average dispersion of intelligent load allocation is 8% higher than the average load rate of the artificial combination list; compared with manual combination, the intelligent loading enables the vehicle loading efficiency to be improved by 15%.
2. Technical scheme
In order to solve the above problems, the present invention adopts the following technical solutions.
A logistics intelligent allocation scheduling method comprises the following steps:
s1: grouping the goods, namely grouping the goods to be loaded at the current station according to the goods direction;
s2: preferentially extracting goods with express signs, and preferentially extracting a loading list;
s3: calculating to form a list, comprehensively calculating the volume, the weight and the value of the goods in one direction to form a list M to be distributed, and arranging and combining the list M to be distributed to form N goods distribution sets;
s4: calculating an optimal list, calculating the loading rate and the cargo value of each cargo stowage set, calculating the optimal cargo stowage set by using vehicle data volume, and calculating the vehicle requirements through the optimal cargo stowage set to form the optimal cargo stowage list;
s5: calculating the vehicle distance and the vehicle volume, acquiring available vehicle information based on the vehicle arrival information and the vehicle GPS distance, and acquiring all available vehicle information through the estimated arrival time and the GPS distance;
s6: forming a vehicle list, carrying out loading sequencing on the vehicles, estimating the vehicle type, the estimated arrival time and the GPS distance, carrying out loading sequencing on the vehicles comprehensively, and arranging the vehicles with large loading space and short distance in front;
s7: acquiring vehicles, matching nearby allocable vehicles in real time through a station and a vehicle GPS, acquiring vehicle type loading information, screening out the vehicle type capacity with the volume or weight larger than the maximum vehicle type capacity, and rejecting and handing over to manual processing through a combined procedure;
s8: loading the vehicle, and picking up the goods from the goods stowage list until the weight and volume limit of the current vehicle is reached;
s9: selecting the next vehicle, selecting the next vehicle from the vehicle list, and repeating the cargo loading action until the cargo loading is completed;
s10: and recording the vehicle loading, and outputting the loading order of the vehicle after the vehicle is loaded.
Further, in S3, a list is formed by calculation, and delivery area information a (a ═ {1, 2, 3 … m }) and all outstanding order information D (D ═ 1, 2, 3 … n }) are input, and the order information includes a volume Vd, a weight qd, and a destination da.
Further, the orders are divided into M types according to the order destination coordinates, the M types of orders are sorted in descending order according to the volume, and M order sequences d are output, wherein the sequence d is da (z) < … da (2) <da (1), z represents the order number of each area, and z may be different in each area.
Further, the vehicle list is formed in S6, the vehicle types are sorted in descending order of vehicle type sorting volume, and a vehicle type sorting sequence T (T ═ {1, 2, 3 … T }) and a corresponding upper limit number nl of vehicles (l ═ 1, 2, 3 … T) are output.
Further, in S10, the vehicle stowage rate is recorded, and the vehicle stowage rate (actual stowage weight/vehicle load + actual stowage amount/vehicle loadable volume)/2 × 100% is calculated, and the vehicle stowage rate is recorded, and the weight, volume, and stowage rate of the cargo are calculated for the next stowage calculation.
Further, the vehicle loading in S8 selects the order sequence from the goods loading list after the permutation and combination to take out the first sequence, and loads the order therein into the top vehicle in the vehicle type sequence T.
Further, in the step S8, if the current vehicle load does not place the order, selecting the next order in the distribution list sequence, and if none of the orders in the distribution list can satisfy the remaining volume of the vehicle, selecting the next vehicle for distribution until all the goods are loaded.
Further, the available vehicle information in S5 includes vehicle types, upper vehicle quantity limits corresponding to the vehicle types, and optimal vehicle ranks preferred by the loading spaces of the vehicle types.
3. Advantageous effects
Compared with the prior art, the invention has the advantages that:
the invention can analyze and calculate the loading requirements of all goods to be loaded, such as volume, weight and the like, and the allocable vehicles, calculate the most suitable transport vehicle, do not waste the load of the automobile, automatically provide a goods allocation scheme with the maximum loading capacity and the highest economic value, obtain the maximum loading capacity and the maximum economic value, screen out the optimal scheme for loading and allocating the goods by an artificial intelligence multi-iteration algorithm under the restriction of the same load and volume, simultaneously ensure that the arrival time of the goods is not delayed by selecting the optimal path for distribution, reduce the transport cost to the maximum extent, maximize the transport profit of one truck, minimize the number of the left warehouse tickets and avoid overload, improve the allocation rate and average kilometer benefit of a single vehicle by means of the positioning information of the vehicle, GPS navigation software, combining the attributes of various goods, the attributes of the vehicle, such as the load and the like, and by analyzing the early-stage configuration data and depending on a reasonable allocation algorithm, through data analysis, the intelligent loading algorithm is adopted to combine the single phase and the manual combination list, and the average dispersion of the intelligent loading is 8% higher than the average loading rate of the manual combination list; compared with manual combination, the intelligent loading enables the vehicle loading efficiency to be improved by 15%.
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FIG. 1 is a schematic overall flow chart of the present invention.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
Example 1:
referring to fig. 1, an intelligent logistics stowage scheduling method includes the following steps:
s1: grouping the goods, namely grouping the goods to be loaded at the current station according to the goods direction;
s2: preferentially extracting goods with express signs, and preferentially extracting a loading list;
s3: calculating to form a list, comprehensively calculating the volume, the weight and the value of goods in one direction to form a to-be-distributed list M, arranging and combining the to-be-distributed list M to form N goods distribution sets, inputting distribution area information A (A is {1, 2, 3 … M }) and all outstanding order information D (D is {1, 2, 3 … N }), wherein the order information comprises a volume Vd, a weight qd and a destination da, dividing the order into M types according to order destination coordinates, respectively sorting the M types of orders according to the volume in a descending order mode, and outputting M order sequences D, da (z) } … da (2) <da (1), wherein z represents the order number of each area, and may be different in each area;
s4: calculating an optimal list, calculating the loading rate and the cargo value of each cargo stowage set, calculating the optimal cargo stowage set by using vehicle data volume, and calculating the vehicle requirements through the optimal cargo stowage set to form the optimal cargo stowage list;
s5: calculating the vehicle distance and the vehicle volume, acquiring available vehicle information based on the vehicle-to-post information and the vehicle GPS distance, and acquiring all available vehicle information by predicting the arrival time and the GPS distance, wherein the available vehicle information comprises vehicle types, vehicle quantity upper limits corresponding to the vehicle types and loading spaces of the vehicle types, and the optimal vehicle ranking is preferably selected;
s6: forming a vehicle list, carrying out loading sequencing on vehicles, predicting station arrival time and GPS distance, carrying out loading sequencing on the vehicles comprehensively, arranging the vehicles with large loading space and short distance in front, sequencing the vehicles in a descending order according to the sequencing volume of the vehicles, and outputting a vehicle type sequencing sequence T (T ═ 1, 2, 3 … T), and the corresponding upper limit number nl of the vehicles (l ═ 1, 2, 3 … T);
s7: acquiring vehicles, matching nearby allocable vehicles in real time through a station and a vehicle GPS, acquiring vehicle type loading information, screening out the vehicle type capacity with the volume or weight larger than the maximum vehicle type capacity, and rejecting and handing over to manual processing through a combined procedure;
s8: vehicle loading, namely picking up cargos from a cargo loading list until the weight and volume limits of a current vehicle are reached, selecting an order sequence from the arranged and combined cargo loading list to take out a first sequence, loading the orders in the first sequence into a top vehicle in a vehicle type sequence T, selecting the next order in the loading list sequence if the current vehicle is not loaded with the orders, and selecting the next vehicle for loading until all cargos are loaded completely if the order with the minimum volume in the loading list cannot meet the residual volume of the vehicle and if none of the orders in the cargo loading list meets the residual volume of the vehicle;
s9: selecting the next vehicle, selecting the next vehicle from the vehicle list, and repeating the cargo loading action until the cargo loading is completed;
s10: and recording the vehicle loading, outputting a loading order of the vehicle after the vehicle is loaded, calculating the vehicle loading rate (actual loading weight/vehicle load + actual loading formula/vehicle loadable volume)/2 x 100%, recording the vehicle loading rate, and calculating the weight, volume and loading rate of the goods for the next loading calculation.
The technical scheme can analyze and calculate the loading requirements of all goods to be loaded, such as volume, weight and the like, and the allocable vehicles, calculate the most suitable transport vehicle, does not waste the load of the automobile, automatically provides a goods loading scheme with the maximum loading capacity and the highest economic value, obtains the maximum loading capacity and the maximum economic value, screens out the optimal scheme for loading the truck by an artificial intelligence multi-iteration algorithm under the restriction of the same load and volume, simultaneously can ensure that the transportation cost is reduced to the maximum extent without delaying the arrival time of the goods by selecting the optimal path for distribution, ensures that the transportation profit of one truck is maximum, the number of stored tickets is minimum, and the truck is not overloaded, combines the attributes of various goods, the vehicle load and the like by analyzing the early configuration data and relying on a reasonable loading algorithm to improve the loading rate and average kilometer of the single truck benefit, through data analysis, the intelligent loading algorithm is adopted to combine the single phase and the manual combination list, and the average dispersion of the intelligent loading is 8% higher than the average loading rate of the manual combination list; compared with manual combination, the intelligent loading enables the vehicle loading efficiency to be improved by 15%.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A logistics intelligent allocation scheduling method is characterized by comprising the following steps:
s1: grouping the goods, namely grouping the goods to be loaded at the current station according to the goods direction;
s2: preferentially extracting goods with express signs, and preferentially extracting a loading list;
s3: calculating to form a list, comprehensively calculating the volume, the weight and the value of the goods in one direction to form a list M to be distributed, and arranging and combining the list M to be distributed to form N goods distribution sets;
s4: calculating an optimal list, calculating the loading rate and the cargo value of each cargo stowage set, calculating the optimal cargo stowage set by using vehicle data volume, and calculating the vehicle requirements through the optimal cargo stowage set to form the optimal cargo stowage list;
s5: calculating the vehicle distance and the vehicle volume, acquiring available vehicle information based on the vehicle arrival information and the vehicle GPS distance, and acquiring all available vehicle information through the estimated arrival time and the GPS distance;
s6: forming a vehicle list, carrying out loading sequencing on the vehicles, estimating the vehicle type, the estimated arrival time and the GPS distance, carrying out loading sequencing on the vehicles comprehensively, and arranging the vehicles with large loading space and short distance in front;
s7: acquiring vehicles, matching nearby allocable vehicles in real time through a station and a vehicle GPS, acquiring vehicle type loading information, screening out the vehicle type capacity with the volume or weight larger than the maximum vehicle type capacity, and rejecting and handing over to manual processing through a combined procedure;
s8: loading the vehicle, and picking up the goods from the goods stowage list until the weight and volume limit of the current vehicle is reached;
s9: selecting the next vehicle, selecting the next vehicle from the vehicle list, and repeating the cargo loading action until the cargo loading is completed;
s10: and recording the vehicle loading, and outputting the loading order of the vehicle after the vehicle is loaded.
2. The intelligent logistics stowage scheduling method of claim 1, wherein: in S3, a list is calculated and a delivery area information a (a ═ 1, 2, 3 … m) and all outstanding order information D (D ═ 1, 2, 3 … n) are input, and the order information includes a volume Vd, a weight qd, and a destination da.
3. The intelligent logistics stowage scheduling method of claim 2, wherein: dividing the orders into M types according to order destination coordinates by regions, sorting the M types of orders respectively according to the volume in a descending order, and outputting M order sequences d, wherein the sequence d is da (z) < … da (2) <da (1), z represents the order number of each region, and z may be different in each region.
4. The intelligent logistics stowage scheduling method of claim 1, wherein: the step S6 forms a vehicle list, sorts the vehicle types in descending order of the vehicle type sorting volume, and outputs a vehicle type sorting sequence T (T ═ {1, 2, 3 … T }) and a corresponding upper limit number nl of vehicles (l ═ 1, 2, 3 … T).
5. The intelligent logistics stowage scheduling method of claim 1, wherein: in S10, the vehicle stowage is recorded, the vehicle stowage rate (actual stowage weight/vehicle load + actual stowage party number/vehicle loadable volume)/2 × 100% is calculated, the vehicle stowage rate is recorded, and the cargo weight, volume, and stowage rate are calculated for the next stowage calculation.
6. The intelligent logistics stowage scheduling method of claim 1, wherein: and S8, loading the vehicles, selecting the order sequence from the goods loading list after arrangement and combination to take out the first sequence, and loading the orders into the top vehicle in the vehicle type sequence T.
7. The intelligent logistics stowage scheduling method of claim 1, wherein: and step S8, if the vehicle is loaded, selecting the next order in the sorting of the stowage list if the current vehicle loading does not place the order, if the order with the minimum volume in the stowage list cannot meet the residual volume of the vehicle, and if none of the orders in the cargo stowage list meets the residual volume of the vehicle, selecting the next vehicle for stowage until all the cargos are loaded completely.
8. The intelligent logistics stowage scheduling method of claim 1, wherein: the available vehicle information in S5 includes vehicle types, upper limit of vehicle quantity corresponding to each vehicle type, and optimal vehicle rank preferred by the loading space of each vehicle type.
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| CN111401821A (en) * | 2020-04-24 | 2020-07-10 | 中储南京智慧物流科技有限公司 | Intelligent pairing system and method for vehicle and goods online loading |
| CN111598341A (en) * | 2020-05-18 | 2020-08-28 | 广东电网有限责任公司 | Electric power material distribution method and system based on material allocation and path optimization |
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