CN116502975B - Store service duration prediction method based on cold chain transportation scene - Google Patents

Store service duration prediction method based on cold chain transportation scene Download PDF

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CN116502975B
CN116502975B CN202310758377.2A CN202310758377A CN116502975B CN 116502975 B CN116502975 B CN 116502975B CN 202310758377 A CN202310758377 A CN 202310758377A CN 116502975 B CN116502975 B CN 116502975B
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孙晓宇
黄博
刘方琦
刘昌盛
曾小松
杨茂茹
华强
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Chengdu Yunlitchi Technology Co ltd
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Abstract

The invention discloses a store service duration prediction method based on a cold chain transportation scene, which belongs to the technical field of logistics transportation and comprises the following steps: s1: acquiring a task to be distributed of a store, and generating a store waiting distribution time length according to distribution weight of the store in a picking area; s2: generating store dispatch distribution time length according to the running track; s3: generating store unloading time length; s4: correcting the waiting distribution time and the unloading time of the store to obtain the basic service time of the store; s5: and taking the sum of the store dispatch distribution time length and the store basic service time length as the store final service time length. The store service duration prediction method takes the sum of the store dispatching delivery duration and the store basic service duration as the final service duration of the store, so that the reasonable planning of delivery routes and dispatching vehicles in the transportation process is facilitated, the goods delivery efficiency is improved, and the delivery cost is reduced.

Description

Store service duration prediction method based on cold chain transportation scene
Technical Field
The invention belongs to the technical field of logistics transportation, and particularly relates to a store service duration prediction method based on a cold chain transportation scene.
Background
In the cold chain logistics city distribution process, a vehicle often needs to distribute a plurality of stores, in order to meet the receiving time requirement of each store, a distribution route needs to be planned in advance, and the service time of the vehicle in the store needs to be considered in an algorithm for planning the route, wherein the service time comprises all times of parking, waiting for unloading, carrying goods and the like. The estimated quality of service duration directly affects the time that the vehicle arrives at the next store. The service time is affected by factors such as the amount of store, the location of the store, and the proficiency in handling. Whereas traditional store service durations are often estimated by the dispatcher based on driver feedback, they are subject to large errors and subjective impacts.
Disclosure of Invention
The invention provides a store service duration estimation method based on a cold chain transportation scene in order to solve the problems.
The technical scheme of the invention is as follows: the store service duration estimation method based on the cold chain transportation scene comprises the following steps:
s1: acquiring a task to be distributed of a store, determining the distribution weight of the store in a picking area according to the storage information of each item to be picked in the task to be distributed, and generating the waiting distribution duration of the store according to the distribution weight of the store in the picking area;
s2: acquiring running tracks of stores and a picking area, and generating store dispatching and delivery time according to the running tracks;
s3: acquiring store unloading information and goods information of each goods to be picked in a task to be distributed, and generating store unloading time;
s4: correcting the waiting distribution time and the unloading time of the store to obtain the basic service time of the store;
s5: and taking the sum of the store dispatch distribution time length and the store basic service time length as the store final service time length.
The beneficial effects of the invention are as follows:
(1) The store service duration prediction method is based on a special scene of cold chain transportation, and accurately estimates store waiting delivery duration by carrying out parameter operation on storage information of goods to be picked and real-time positions of goods picking areas, so that the quality of the goods is prevented from being influenced by overlong waiting time;
(2) The store service duration prediction method constructs a store unloading duration generation model by fully considering the influence of factors such as different unloading modes, unloading amounts, cargo specifications and the like on the unloading duration, and accurately calculates the store unloading duration;
(3) According to the store service duration prediction method, matrix correction is carried out on the store waiting distribution duration and the store unloading duration, deviation caused by duration prediction is reduced, accurate basic service duration is obtained, the sum of the store dispatching distribution duration and the store basic service duration is used as the store final service duration, reasonable planning of distribution routes and dispatching vehicles in the transportation process is facilitated, the goods distribution efficiency is improved, and the distribution cost is reduced.
Further, S1 comprises the following sub-steps:
s11: acquiring storage information of each item to be picked in a task to be distributed, and generating a pick weight of each item to be picked;
s12: acquiring real-time positions of all cargoes to be picked in a picking area in a task to be distributed, and calculating Euclidean distances between the real-time positions of all cargoes to be picked and outlets of the picking area;
s13: calculating the distribution weight of the store in the picking area according to the picking weight of each item to be picked and the Euclidean distance between the real-time position of each item to be picked and the outlet of the picking area;
s14: and generating a store waiting delivery duration according to the delivery weight of the store in the pick-up area.
Further, in S11, the calculation formula of the pick weight σ of the to-be-picked goods is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein t is 2 Indicating the highest storage temperature, t 1 Indicating the minimum storage temperature of the goods to be picked, t 0 Indicating the current storage temperature, T, of the goods to be picked in the picking zone 2 Indicating the optimal storage time of the goods to be picked, T 1 Indicating the time period of the goods to be picked reaching the cold junction, T 0 Representing the stored time period of the goods to be picked in the picking area, C representing the cold junction point of the goods to be picked, C 1 Represents a first constant, c 2 Representing a second constant.
Further, in S13, the formula for calculating the distribution weight ω of the store in the pick-up area is:
the method comprises the steps of carrying out a first treatment on the surface of the In sigma m Representing the pick weight, θ, of the mth order to be picked m Represents the weight of the mth article to be picked, M represents the number of articles to be picked of the task to be distributed, D m Representing the Euclidean distance of the real-time location of the mth order to the order picking zone outlet.
Further, in S14, the calculation formula of the store waiting delivery duration T is: t=ω (T) 2 －t 1 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein ω represents the distribution weight of the store in the pick zone, t 2 Indicating the end delivery time of the pick zone, t 1 Indicating the time of start delivery of the pick zone.
Further, in S3, the specific method for generating the unloading duration of the store is as follows: constructing a store unloading time length generation model, inputting store unloading information and goods information of each to-be-picked goods in a task to be distributed into the store unloading time length generation model, and generating store unloading time length;
the store unloading information comprises an unloading mode, a carrying capacity per minute and an unloading convenience weight; the cargo information of the cargo to be picked includes cargo weight, cargo size, and cargo quantity.
Further, the expression of the store unloading time length generation model F is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein a represents a 0-1 decision variable of whether the unloading mode adopts manual unloading or not, b represents a 0-1 decision variable of whether the unloading mode adopts machine unloading or not, M represents the quantity of objects to be picked of a task to be delivered, and Q 1 Representing the amount of goods transported per minute by manual unloading, Q 2 Indicating the amount of load carried per minute with machine unloading, gamma 1 Indicating discharge convenience weight, gamma, using manual discharge 2 Weight, q, representing convenience of manual discharge m Representing the weight of the mth item to be picked, u m Representing the length, v, of the mth order of the items to be picked m Represents the length of the mth article to be picked, K represents the number of loading workers and delta by adopting manual unloading k Represents the proficiency of the kth handler, N represents the number of machines for unloading by the machine, φ n Indicating the degree of wear of the nth machine.
Further, S4 comprises the sub-steps of:
s41: calculating the average value of the waiting distribution time of the store and the unloading time of the store, and taking the average value as the average value of the time; calculating the standard deviation of the waiting delivery time of the store and the unloading time of the store, and taking the standard deviation as the standard deviation of the time;
s42: constructing a time matrix according to the waiting and distribution time of the store, the unloading time of the store, the time mean value and the time standard deviation;
s43: calculating a conjugate transpose matrix of the duration matrix;
s44: and calculating the store basic service time according to the time matrix and the conjugate transpose matrix of the time matrix.
Further, in S42, the expression of the duration matrix X is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein T represents a store waiting distribution time period, F T Indicates the unloading time of store, S T Represent the average value of time length, A T Representing the standard deviation of the duration.
Further, in S44, the calculation formula of the store basic service duration G is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein I represents an identity matrix, lambda 1 Eigenvalues, lambda, representing a time duration matrix 2 Characteristic values of the conjugate transpose matrix of the duration matrix are represented, X represents the duration matrix, and Y represents the conjugate transpose matrix of the duration matrix.
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FIG. 1 is a flow chart of a store service duration estimation method based on a cold chain transportation scenario.
Detailed Description
Embodiments of the present invention are further described below with reference to the accompanying drawings.
As shown in fig. 1, the invention provides a store service duration estimation method based on a cold chain transportation scene, which comprises the following steps:
s1: acquiring a task to be distributed of a store, determining the distribution weight of the store in a picking area according to the storage information of each item to be picked in the task to be distributed, and generating the waiting distribution duration of the store according to the distribution weight of the store in the picking area;
s2: acquiring running tracks of stores and a picking area, and generating store dispatching and delivery time according to the running tracks;
s3: acquiring store unloading information and goods information of each goods to be picked in a task to be distributed, and generating store unloading time;
s4: correcting the waiting distribution time and the unloading time of the store to obtain the basic service time of the store;
s5: and taking the sum of the store dispatch distribution time length and the store basic service time length as the store final service time length.
In an embodiment of the present invention, S1 comprises the following sub-steps:
s11: acquiring storage information of each item to be picked in a task to be distributed, and generating a pick weight of each item to be picked;
s12: acquiring real-time positions of all cargoes to be picked in a picking area in a task to be distributed, and calculating Euclidean distances between the real-time positions of all cargoes to be picked and outlets of the picking area;
s13: calculating the distribution weight of the store in the picking area according to the picking weight of each item to be picked and the Euclidean distance between the real-time position of each item to be picked and the outlet of the picking area;
s14: and generating a store waiting delivery duration according to the delivery weight of the store in the pick-up area.
In the embodiment of the present invention, in S11, the calculation formula of the picking weight σ of the to-be-picked goods is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein t is 2 Indicating the highest storage temperature, t 1 Indicating the minimum storage temperature of the goods to be picked, t 0 Indicating that the goods to be picked are pickingThe current storage temperature of the zone, T 2 Indicating the optimal storage time of the goods to be picked, T 1 Indicating the time period of the goods to be picked reaching the cold junction, T 0 Representing the stored time period of the goods to be picked in the picking area, C representing the cold junction point of the goods to be picked, C 1 Represents a first constant, c 2 Representing a second constant.
The waiting and delivery time of the store in the picking area largely determines the progress of the total service time of the store and the quality of the goods, so that the waiting and delivery time of the store needs to be estimated accurately.
The storage information includes a maximum storage temperature of the goods, a minimum storage temperature, a current storage temperature at the pick zone, an optimal storage time period of the goods, a time for the goods to reach the cold junction, a stored time of the goods at the pick zone, and the cold junction. The transportation of goods in a cold chain scene is different from the transportation of normal temperature goods, and the key information such as the storage temperature and the cold junction of the goods needs to be fully considered, so that the quality of the goods can be greatly influenced. The storage temperatures of different cold chain cargos are different, and the quality of the cargos can be influenced by the waiting time of the cargos, so that the distribution weight of each cargo is obtained by carrying out parameter processing on storage information. And calculating the distribution weight of each cargo and the Euclidean distance to obtain the final distribution weight of the store in the whole picking area.
In the embodiment of the present invention, in S13, the calculation formula of the distribution weight ω of the store in the pick-up area is:the method comprises the steps of carrying out a first treatment on the surface of the In sigma m Representing the pick weight, θ, of the mth order to be picked m Represents the weight of the mth article to be picked, M represents the number of articles to be picked of the task to be distributed, D m Representing the Euclidean distance of the real-time location of the mth order to the order picking zone outlet.
In the embodiment of the present invention, in S14, the calculation formula of the waiting delivery duration T of the store is: t=ω (T) 2 －t 1 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein ω represents the distribution weight of the store in the pick zone, t 2 Indicating the end delivery time of the pick zone, t 1 Indicating the time of start delivery of the pick zone.
In the embodiment of the present invention, in S3, a specific method for generating a store unloading duration is as follows: constructing a store unloading time length generation model, inputting store unloading information and goods information of each to-be-picked goods in a task to be distributed into the store unloading time length generation model, and generating store unloading time length;
the store unloading information comprises an unloading mode, a carrying capacity per minute and an unloading convenience weight; the cargo information of the cargo to be picked includes cargo weight, cargo size, and cargo quantity.
In the embodiment of the invention, the expression of the store unloading time length generation model F is as follows:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein a represents a 0-1 decision variable of whether the unloading mode adopts manual unloading or not, b represents a 0-1 decision variable of whether the unloading mode adopts machine unloading or not, M represents the quantity of objects to be picked of a task to be delivered, and Q 1 Representing the amount of goods transported per minute by manual unloading, Q 2 Indicating the amount of load carried per minute with machine unloading, gamma 1 Indicating discharge convenience weight, gamma, using manual discharge 2 Weight, q, representing convenience of manual discharge m Representing the weight of the mth item to be picked, u m Representing the length, v, of the mth order of the items to be picked m Represents the length of the mth article to be picked, K represents the number of loading workers and delta by adopting manual unloading k Represents the proficiency of the kth handler, N represents the number of machines for unloading by the machine, φ n Indicating the degree of wear of the nth machine. If the unloading mode adopts manual unloading, a=1 and b=0; if the discharge mode adopts the machine discharge, a=0 and b=1.
Store unloading may take two forms, one is manual unloading and the other is unloading using existing handling machinery. When manual unloading is adopted, the proficiency of workers can influence the unloading efficiency; when the carrying machine is used for unloading, the wear degree of the machine also affects the unloading efficiency.
Different unloading convenience grades can generate different service time lengths, and the unloading convenience grades are determined according to the position relation between the vehicle stop points and the store; if the vehicle is parked at the door of the store, the unloading convenience level is a first level, and the corresponding unloading convenience weight value is 1; if the vehicle is parked at a certain position away from the store gate, the unloading convenience level is a second level, and the corresponding unloading convenience weight value is 2; if the vehicle is parked in the underground garage of the store, the unloading convenience level is three-level, and the corresponding unloading convenience weight value is 3.
In an embodiment of the present invention, S4 comprises the following sub-steps:
s41: calculating the average value of the waiting distribution time of the store and the unloading time of the store, and taking the average value as the average value of the time; calculating the standard deviation of the waiting delivery time of the store and the unloading time of the store, and taking the standard deviation as the standard deviation of the time;
s42: constructing a time matrix according to the waiting and distribution time of the store, the unloading time of the store, the time mean value and the time standard deviation;
s43: calculating a conjugate transpose matrix of the duration matrix;
s44: and calculating the store basic service time according to the time matrix and the conjugate transpose matrix of the time matrix.
In the embodiment of the present invention, in S42, the expression of the duration matrix X is:the method comprises the steps of carrying out a first treatment on the surface of the Wherein T represents a store waiting distribution time period, F T Indicates the unloading time of store, S T Represent the average value of time length, A T Representing the standard deviation of the duration.
In the embodiment of the present invention, in S44, the calculation formula of the store basic service duration G is:
the method comprises the steps of carrying out a first treatment on the surface of the Wherein I represents an identity matrix, lambda 1 Eigenvalues, lambda, representing a time duration matrix 2 Characteristic values of the conjugate transpose matrix of the duration matrix are represented, X represents the duration matrix, and Y represents the conjugate transpose matrix of the duration matrix.
Those of ordinary skill in the art will recognize that the embodiments described herein are for the purpose of aiding the reader in understanding the principles of the present invention and should be understood that the scope of the invention is not limited to such specific statements and embodiments. Those of ordinary skill in the art can make various other specific modifications and combinations from the teachings of the present disclosure without departing from the spirit thereof, and such modifications and combinations remain within the scope of the present disclosure.

Claims (6)

1. The store service duration estimation method based on the cold chain transportation scene is characterized by comprising the following steps of:
s1: acquiring a task to be distributed of a store, determining the distribution weight of the store in a picking area according to the storage information of each item to be picked in the task to be distributed, and generating the waiting distribution duration of the store according to the distribution weight of the store in the picking area;
s2: acquiring running tracks of stores and a picking area, and generating store dispatching and delivery time according to the running tracks;
s3: acquiring store unloading information and goods information of each goods to be picked in a task to be distributed, and generating store unloading time;
s4: correcting the waiting distribution time and the unloading time of the store to obtain the basic service time of the store;
s5: taking the sum of the store dispatching distribution time length and the store basic service time length as the store final service time length;
the step S1 comprises the following substeps:
s11: acquiring storage information of each item to be picked in a task to be distributed, and generating a pick weight of each item to be picked;
s12: acquiring real-time positions of all cargoes to be picked in a picking area in a task to be distributed, and calculating Euclidean distances between the real-time positions of all cargoes to be picked and outlets of the picking area;
s13: calculating the distribution weight of the store in the picking area according to the picking weight of each item to be picked and the Euclidean distance between the real-time position of each item to be picked and the outlet of the picking area;
s14: generating a store waiting delivery duration according to the delivery weight of the store in the pick-up area;
in S11, the calculation formula of the picking weight σ of the object to be picked is:
；
wherein t is 2 Indicating the highest storage temperature, t 1 Indicating the minimum storage temperature of the goods to be picked, t 0 Indicating the current storage temperature, T, of the goods to be picked in the picking zone 2 Indicating the optimal storage time of the goods to be picked, T 1 Indicating the time period of the goods to be picked reaching the cold junction, T 0 Representing the stored time period of the goods to be picked in the picking area, C representing the cold junction point of the goods to be picked, C 1 Represents a first constant, c 2 Representing a second constant;
in S13, the formula for calculating the distribution weight ω of the store in the pick-up area is:
；
in sigma m Representing the pick weight, θ, of the mth order to be picked m Represents the weight of the mth article to be picked, M represents the number of articles to be picked of the task to be distributed, D m Representing the Euclidean distance between the real-time position of the mth article to be picked and the outlet of the picking area;
in S14, the calculation formula of the store waiting delivery duration T is as follows: t=ω (T) 2 －t 1 ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein ω represents the distribution weight of the store in the pick zone, t 2 Indicating the end delivery time of the pick zone, t 1 Indicating the time of start delivery of the pick zone.
2. The method for estimating a store service time based on a cold chain transportation scenario according to claim 1, wherein in S3, the specific method for generating a store unloading time is as follows: constructing a store unloading time length generation model, inputting store unloading information and goods information of each to-be-picked goods in a task to be distributed into the store unloading time length generation model, and generating store unloading time length;
the store unloading information comprises an unloading mode, a carrying capacity per minute and an unloading convenience weight; the cargo information of the cargo to be picked includes cargo weight, cargo size, and cargo quantity.
3. The method for estimating a store service time based on a cold chain transportation scenario according to claim 2, wherein the expression of the store unloading time generation model F is:
；
wherein a represents a 0-1 decision variable of whether the unloading mode adopts manual unloading or not, b represents a 0-1 decision variable of whether the unloading mode adopts machine unloading or not, M represents the quantity of objects to be picked of a task to be delivered, and Q 1 Representing the amount of goods transported per minute by manual unloading, Q 2 Indicating the amount of load carried per minute with machine unloading, gamma 1 Indicating discharge convenience weight, gamma, using manual discharge 2 Weight, q, representing convenience of manual discharge m Representing the weight of the mth item to be picked, u m Representing the length, v, of the mth order of the items to be picked m Represents the length of the mth article to be picked, K represents the number of loading workers and delta by adopting manual unloading k Represents the proficiency of the kth handler, N represents the number of machines for unloading by the machine, φ n Indicating the degree of wear of the nth machine.
4. The method for estimating a length of a store service based on a cold chain transportation scenario according to claim 1, wherein S4 comprises the substeps of:
s41: calculating the average value of the waiting distribution time of the store and the unloading time of the store, and taking the average value as the average value of the time; calculating the standard deviation of the waiting delivery time of the store and the unloading time of the store, and taking the standard deviation as the standard deviation of the time;
s42: constructing a time matrix according to the waiting and distribution time of the store, the unloading time of the store, the time mean value and the time standard deviation;
s43: calculating a conjugate transpose matrix of the duration matrix;
s44: and calculating the store basic service time according to the time matrix and the conjugate transpose matrix of the time matrix.
5. The method for estimating a length of a store service based on a cold chain transportation scenario according to claim 4, wherein in S42, the length matrix X has the expression:
；
wherein T represents a store waiting distribution time period, F T Indicates the unloading time of store, S T Represent the average value of time length, A T Representing the standard deviation of the duration.
6. The method for estimating a store service duration based on a cold chain transportation scenario according to claim 4, wherein in S44, a calculation formula of the store basic service duration G is:
；
wherein I represents an identity matrix, lambda 1 Eigenvalues, lambda, representing a time duration matrix 2 Characteristic values of the conjugate transpose matrix of the duration matrix are represented, X represents the duration matrix, and Y represents the conjugate transpose matrix of the duration matrix.
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