CN116468352B - Logistics departure time calculation method, device, equipment and storage medium - Google Patents

Logistics departure time calculation method, device, equipment and storage medium Download PDF

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CN116468352B
CN116468352B CN202310715450.8A CN202310715450A CN116468352B CN 116468352 B CN116468352 B CN 116468352B CN 202310715450 A CN202310715450 A CN 202310715450A CN 116468352 B CN116468352 B CN 116468352B
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程志刚
赵兴
胡一皓
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Kuayue Express Group Co ltd
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Abstract

The invention discloses a method, a device, equipment and a storage medium for calculating logistics departure time, wherein the method comprises the following steps: acquiring a history waybill corresponding to each departure line of a target site in a preset history time period, wherein the history waybill comprises a history departure batch and history departure time; aiming at any historical departure batch, taking the median of a plurality of historical departure times corresponding to the current historical departure batch as the initial departure time of the current historical departure batch; taking the initial departure time of each historical departure batch as the initial solution of the improved simulated annealing algorithm, updating and selecting the solution based on the initial solution iteration according to the loading rate and the aging of the solution, and taking the solution when the preset iteration stop condition is reached as the target departure time. According to the method and the device, the logistics departure time can be calculated according to the loading rate and the aging, and the loading rate and the aging of the logistics are improved.

Description

Logistics departure time calculation method, device, equipment and storage medium
Technical Field
The invention relates to the technical field of logistics, in particular to a method, a device, equipment and a storage medium for calculating logistics departure time.
Background
In the logistics industry, a large number of waybills arrive at each site every day, each site can send the waybills to the next site at fixed departure time, if departure is too early, site goods are not accumulated enough, so that the loading rate of trucks is too low, the transportation cost is increased, if departure is too late, the timeliness of the waybills can be influenced, and the customer satisfaction is reduced.
Therefore, how to reasonably arrange the logistics departure time to meet the aging requirement of customers and the cost requirement of companies, and improve the logistics efficiency and customer satisfaction is always a challenge for logistics enterprises.
Conventional logistic departure scheduling methods are usually manually operated based on the experience of a salesman, and departure time is determined by rewinding historical waybills, but the manual operation method is inefficient, requires a great deal of labor cost to maintain and rewind, and has certain subjectivity and uncertainty in the result.
Disclosure of Invention
The invention provides a method, a device, equipment and a storage medium for calculating logistics departure time, which are used for solving the problems of low efficiency, low accuracy and poor effect of the traditional logistics departure time determination method.
In order to solve the technical problem, in a first aspect, the present invention provides a method for calculating a logistic departure time, the method comprising:
Acquiring a history waybill corresponding to each departure line of a target site in a preset history time period, wherein the history waybill comprises a history departure batch and history departure time;
aiming at any historical departure batch, taking the median of a plurality of historical departure times corresponding to the current historical departure batch as the initial departure time of the current historical departure batch;
taking the initial departure time of each historical departure batch as the initial solution of the improved simulated annealing algorithm, updating and selecting the solution based on the initial solution iteration according to the loading rate and the aging of the solution, and taking the solution when the preset iteration stop condition is reached as the target departure time.
Optionally, the updating of the solution based on the initial solution iteration according to the loading rate and aging of the solution includes:
for any iteration, randomly selecting a target operator from a preset random removal operator and a worst removal operator, wherein the worst removal operator is used for removing loading rate and/or time-lapse worst departure time;
and disturbing the solution of the previous iteration by using the target operator to obtain an updated solution of the current iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
Optionally, the selecting a solution based on the initial solution iteration according to the loading rate and the aging of the solution includes:
for any iteration, calculating an on-site cargo quantity accumulated value of an updated solution of the current iteration, wherein the on-site cargo quantity accumulated value is used for measuring the loading rate and the time effect of the solution;
judging whether the updated solution of the current iteration accords with a screening condition or not according to a preset threshold value and an on-site cargo quantity accumulated value of the updated solution of the current iteration;
if not, updating the solution again;
and if so, selecting a solution of the current iteration from the updated solution of the current iteration and the solution of the previous iteration according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
Optionally, the present cargo quantity accumulated value，
wherein ,total number of departure lots for updated solutions of the current iteration +.>Is a positive integer>The +.f. of the updated solution for the current iteration>+.>Time present amount of goods->The +.f. of the updated solution for the current iteration>+.>Time of vehicle loading.
Optionally, the selecting a solution of the current iteration from the updated solution of the current iteration and the solution of the previous iteration according to the present cargo amount accumulated value of the updated solution of the current iteration and the present cargo amount accumulated value of the solution of the previous iteration includes:
If the present cargo quantity accumulated value of the updated solution of the current iteration is smaller than or equal to the present cargo quantity accumulated value of the solution of the previous iteration, selecting the updated solution of the current iteration as the solution of the current iteration;
otherwise, calculating an evaluation probability according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration, and generating a random number; if the evaluation probability is larger than the random number, selecting an updated solution of the current iteration as the solution of the current iteration, otherwise, selecting a solution of the previous iteration as the solution of the current iteration, and adding the newly increased departure time in the updated solution of the current iteration into a tabu table.
Optionally, the evaluating probability；
wherein ,presence inventory accumulated value for solution of last iteration,/->Presence inventory accumulated value for updated solution of current iteration, +.>The temperature parameter of the current iteration of the simulated annealing algorithm is improved.
Optionally, after updating the solution based on the initial solution iteration, the method further includes:
judging whether the newly increased departure time in the updated solution of any iteration is in the tabu list, and if so, updating the solution again.
In a second aspect, the invention provides a logistics departure time calculation device, which comprises an acquisition module, a median calculation module and a prediction module, wherein:
The acquisition module is used for acquiring a historical freight list corresponding to each departure line of the target site in a preset historical time period, wherein the historical freight list comprises historical departure batches and historical departure time;
the median calculating module is used for regarding the median of a plurality of historical departure times corresponding to the current historical departure batch as the initial departure time of the current historical departure batch;
the prediction module is used for taking the initial departure time of each historical departure batch as the initial solution of the improved simulated annealing algorithm, updating and selecting the solution based on the initial solution iteration according to the loading rate and the aging of the solution, and taking the solution when the preset iteration stop condition is reached as the target departure time.
In a third aspect, the present invention provides a logistic departure time calculation apparatus, comprising a memory and a processor, wherein:
the memory is used for storing a computer program;
the processor is configured to read the program in the memory and execute the steps of a method for calculating a logistic departure time provided in the first aspect.
In a fourth aspect, the present invention provides a computer readable storage medium having stored thereon a readable computer program which when executed by a processor performs the steps of a method for calculating a time to physical distribution departure as provided in the first aspect above.
Compared with the prior art, the logistics departure time calculation method, the logistics departure time calculation device, the logistics departure time calculation equipment and the storage medium have the following beneficial effects:
when the target departure time of the target site is predicted, firstly, a historical freight list corresponding to each line of the target site in a preset historical time period is obtained, then, the median of the historical departure time corresponding to the current historical departure batch is used as the initial departure time of the current historical departure batch, the initial departure time is used as the initial solution of an improved simulated annealing algorithm, iterative calculation and updating are carried out, and finally, the target departure time is obtained. Compared with the traditional method adopting the mean value as the initial solution, the method can avoid the interference of the abnormal extremum data in the historical freight bill, so that the initial solution is more excellent, the convergence speed of an improved simulated annealing algorithm is increased, and the problems of abnormal data and poor frequency determination are effectively solved; according to the method and the device, the logistics departure time can be calculated according to the loading rate and the aging, and the loading rate and the aging of the logistics are improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions of the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is apparent that the drawings in the following description are only some embodiments of the present application, but not all embodiments, and other drawings obtained according to these drawings without inventive effort are all within the scope of the present application.
Fig. 1 is a flowchart of a method for calculating a logistic departure time according to an embodiment of the present application.
Fig. 2 is a schematic diagram of a working process of a random removal operator according to an embodiment of the present application.
FIG. 3 is a schematic diagram of a worst removing operator according to an embodiment of the present application.
Fig. 4 is a schematic structural diagram of a logistic departure time calculating device according to an embodiment of the present application.
Fig. 5 is a schematic structural diagram of a logistic departure time calculating device according to an embodiment of the present application.
Fig. 6 is a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described in further detail with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the application.
In order that the present disclosure may be more fully described and fully understood, the following description is provided by way of illustration of embodiments and specific examples of the present application; this is not the only form of practicing or implementing the application as embodied. The description covers the features of the embodiments and the method steps and sequences for constructing and operating the embodiments. However, other embodiments may be utilized to achieve the same or equivalent functions and sequences of steps. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the application described herein may be implemented in sequences other than those illustrated or otherwise described herein.
In the description of the embodiments of the present application, unless otherwise indicated, "/" means or, for example, a/B may represent a or B; the text "and/or" is merely an association relation describing the associated object, and indicates that three relations may exist, for example, a and/or B may indicate: in addition, in the description of the embodiments of the present application, "plural" means two or more, and other words and the like, it is to be understood that the preferred embodiments described herein are for illustration and explanation of the present application only, and are not intended to limit the present application, and embodiments of the present application and features in the embodiments may be combined with each other without conflict.
Example 1
For each field of the logistics industry, there is an ageing requirement after a typical waybill arrives at the field, which requires the goods to be collected and loaded within a specified time and then shipped from this field to the next field by a truck. Because the cargo volume of most of the site lines is basically stable, in the embodiment of the invention, the process of loading the freight bill into the freight car for departure by the site is simulated by collecting the history freight bill corresponding to the preset history time period of each site, and whether the target departure time of the site is reasonable is judged based on the statistical loading rate and the aging.
As shown in fig. 1, a flowchart of a method for calculating a logistic departure time according to an embodiment of the present invention includes the following steps.
Step S101, acquiring a history waybill corresponding to each departure line of a target site in a preset history time period, wherein the history waybill comprises a history departure batch and history departure time;
firstly, all historical waybills of the target site in a preset historical time period are acquired, the preset historical time period can be determined according to practical situations, for example, the past two weeks, the past one month and the like, and the embodiment of the invention is not limited in particular.
All the history waybills refer to all the logistics waybills passing through the target site, after all the history waybills are obtained, the history waybills are classified according to the departure routes, the same history waybills of the departure routes are used as the same class, and finally the history waybills corresponding to the departure routes in a preset history time period are obtained.
In the embodiment of the invention, the historical waybill comprises a historical departure batch and a historical departure time, wherein the historical departure batch refers to a number of batches each day, for example, for a certain day in a preset historical time period, the total historical departure batches in the day are 4 batches, and the historical departure batch refers to a number of batches in the day; the historical departure time refers to the historical departure time corresponding to the historical departure batch.
Additionally, the historical manifest may include information such as the number of the shipment, the weight of the shipment, the time the shipment arrives at the target site, the destination of the shipment, the time the shipment arrives at the next destination, and the like.
Step S102, regarding any historical departure batch, taking the median of a plurality of historical departure times corresponding to the current historical departure batch as the initial departure time of the current historical departure batch;
after the historical waybills corresponding to each departure line of the target site are obtained, each historical departure batch in the historical waybills of each departure line is described by taking the current historical departure batch as an example, and the median of all the historical departure times in the current historical departure batch is used as the initial departure time of the current historical departure batch.
In the specific implementation process, the historical departure time of each historical departure batch is counted, then time sequencing is carried out, and the initial departure time of each historical departure batch is obtained by adopting a counting median strategy.
There are n historical departure batches per day in a preset historical time period, i.e. the initial departure time of the historical departure batch is as follows:
, wherein ,/>Represents the historical departure times of the 1 st, 2 nd, 3 rd, … th, n th historical departure lots per day, k represents the total number of each historical departure lot within a preset historical period,represents the median of the 1 st, 2 nd, 3 rd, … th, n th historical departure lots.
For example, the predetermined history period is the past week, for FuYong to Daling mountain line, there are 4 history departure batches each day, 7 day departure for week, assuming that the history departure time of the first history departure batch from Monday to Monday is T respectively 1 = (15, 14, 13, 16, 14, 15, 14), where k=7, ordered T 1 = (13, 14, 14, 14, 15, 15, 16), then median t 1 =14, and so on, to give t 1 、t 2 、t 3 、…、t n When a history is launched as each history is launched batchAn initial solution between them.
It will be readily appreciated that included in the initial solution is the historical departure time for each historical departure lot.
In the embodiment of the invention, the median of the historical departure time is used as the initial solution, so that the interference of abnormal extremum data in the historical freight bill can be avoided, the initial solution is more excellent, and the convergence speed of an improved simulated annealing algorithm is further accelerated.
The traditional method adopts a method for counting the average value of the actual departure time of one week or one month, is easy to be interfered by abnormal extremum data, so that the final time result is poor, and taking Fu Yong-Daling mountain lines as an example, the first batch time from Monday to Monday is respectively (13, 13, 14, 14, 15, 15 and 22), and the average value is late due to the fact that the Monday cargo amount is small, and 22 obviously belongs to abnormal values, so that the result is not good.
The conventional method also adopts a mode of counting common time periods, such as the departure frequency of 13, 14 and 15, and takes the time with the highest occurrence frequency as the initial departure time, but under the condition that the occurrence frequency is the same, the determination of which time to use as the initial departure time cannot be determined, such as the occurrence of 13, 14 and 15 respectively for two times, is difficult.
According to the embodiment of the invention, a mode of counting the median time is adopted, the historical departure times of each historical departure batch in a week or a month are firstly ordered, and then the median of each historical departure time is counted, so that the problems of abnormal data, same frequency, poor determination and the like can be effectively solved, the solution result is better, the initial solution is better, and the subsequent convergence speed of a further improved simulated annealing algorithm is also faster.
Step S103, taking the initial departure time of each historical departure batch as the initial solution of the improved simulated annealing algorithm, updating and selecting the solution based on the initial solution iteration according to the loading rate and the aging of the solution, and taking the solution when the preset iteration stop condition is reached as the target departure time.
In the embodiment of the invention, an improved simulated annealing algorithm is adopted to calculate the logistics departure time. The simulated annealing algorithm is an optimization algorithm based on probability, can simulate various conditions possibly occurring in the logistics process, and obtains an optimal solution on the premise of meeting various departure volume constraints and site constraints, so that the finally obtained target departure time is most reasonable.
In a specific implementation process, taking the initial departure time of each historical departure batch obtained in the last step as an initial solution of an improved simulated annealing algorithm, carrying out iterative updating on the basis of the initial solution to generate an updated solution, selecting a solution of the current iteration from the initial solution and the updated solution by taking the loading rate and aging of the solution as evaluation criteria, and continuing the iterative updating until a preset iteration stop condition is reached, and taking the final solution as a target departure time.
As an alternative embodiment, the updating the solution based on the initial solution iteration according to the loading rate and the aging of the solution includes:
for any iteration, randomly selecting a target operator from a preset random removal operator and a worst removal operator, wherein the worst removal operator is used for removing loading rate and/or time-lapse worst departure time;
and disturbing the solution of the previous iteration by using the target operator to obtain an updated solution of the current iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
Taking the initial departure time of each historical departure batch as the initial solution of the improved simulated annealing algorithm, iterating on the basis of the initial solution after the initial solution is obtained, and disturbing the initial solution by using the selected target operator to obtain an updated solution of the current iteration, namely, each iteration is an updated solution obtained on the basis of the solution of the last iteration.
In a specific implementation process, taking any iteration as an example, the embodiment of the invention selects a target operator from a preset random removal operator and a worst removal operator, and then utilizes the target operator to disturb the solution of the last iteration to obtain an updated solution of the current iteration.
The random removal operator is to randomly remove a certain time from the departure time included in the solution of the previous iteration, and then add the neighborhood departure time, as shown in fig. 2, where the departure time included in the solution of the previous iteration is (17:00, 19:00, 21:00, 24:00), and randomly remove a certain departure time, such as 21:00, then 21:00 neighborhood departure times, the resulting update solutions are (17:00, 19:00, 22:00, 24:00).
According to the embodiment of the invention, the rationality of the departure time is evaluated by the loading rate and/or the aging, and the departure time is unreasonable to be set by the low loading rate and the poor aging, so that the departure time with the lowest evaluation value is removed by the worst removal operator in the embodiment of the invention.
The worst removing operator is to remove the departure time with the lowest evaluation value from the departure time contained in the solution of the last iteration, increase the neighborhood departure time and finally obtain the updated solution. The evaluation value may be a loading rate, an aging rate, or a combination of both.
As shown in fig. 3, taking the loading rate as an evaluation value, the departure time included in the solution of the last iteration is (17:00, 19:00, 21:00, 24:00), the loading rate of each departure time is calculated, and the departure time 17 with the lowest loading rate is calculated: 00 removal, add 17: neighborhood departure time of 00 17:30, the updated solution for the current iteration is finally obtained as (17:30, 19:00, 21:00, 24:00).
It should be noted that, in the first iteration, the solution of the previous iteration refers to the initial solution.
The traditional simulated annealing algorithm adopts a single random removal operator, but the single operator is not flexible enough, so that a plurality of invalid searches can be caused, and the waste of search time is caused. According to the embodiment of the invention, the worst removing operator is added to specially search and process the departure time with low timeliness and low loading rate, so that the departure time with poor processing effect can be well processed, the searching speed is greatly improved, the solution obtained by each iteration is optimized, and the effect of the final output target departure time is improved.
As an optional implementation manner, after the updating of the solution based on the initial solution iteration, the method further includes:
judging whether the newly increased departure time in the updated solution of any iteration is in the tabu list, and if so, updating the solution again.
In each iteration process, after an updated solution is obtained, judging whether the newly increased departure time in the updated solution is in a tabu table, if so, selecting a target operator from a random removal operator and a worst removal operator again, perturbing the solution of the previous iteration by using the target operator again, and obtaining the updated solution again until the obtained updated solution is not in the tabu table, and finally obtaining the updated solution of the current iteration.
For example, the solution of the previous iteration includes a departure time t 1 =15，t 2 =16，t 3 18, where the tabu table is empty, the selected target operator is a random removal operator, and t is determined by using the random removal operator 3 Modified to 17, i.e. the update solution obtained after perturbation is t 1 =15，t 2 =16，t 3 For the newly added departure time in the update solution, determine whether the newly added departure time is in the tabu table, i.e., determine 17:00 is in the tabu list, and if the newly added departure time is not in the tabu list, the updated solution is taken as the updated solution of the current iteration.
For example, in the above example, the tabu table is not empty, and the data contained in the tabu table includesThe newly added departure time 17 is judged as follows: 00 in the tabu list, if the newly increased departure time is in the tabu list, selecting a target operator again according to the process, and regenerating an update solution until the newly increased departure time in the update solution is not in the tabu list.
When determining, it is only determined whether the newly increased departure time in the updated solution of any one iteration is in the tabu table, instead of determining the entire departure time in the updated solution.
In the embodiment of the invention, the departure time contained in the tabu list is the time with poor loading rate or aging, the time with poor performance can be screened out through the tabu list, the repeated calculation of the same departure time is avoided, and the convergence rate of the improved simulated annealing algorithm is improved.
As an alternative embodiment, the selecting a solution based on the initial solution iteration according to the loading rate and the aging of the solution includes:
for any iteration, calculating an on-site cargo quantity accumulated value of an updated solution of the current iteration, wherein the on-site cargo quantity accumulated value is used for measuring the loading rate and the time effect of the solution;
judging whether the updated solution of the current iteration accords with a screening condition or not according to a preset threshold value and an on-site cargo quantity accumulated value of the updated solution of the current iteration;
if not, updating the solution again;
and if so, selecting a solution of the current iteration from the updated solution of the current iteration and the solution of the previous iteration according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
For the solutions of the previous iteration and the updated solutions of the current iteration, the solutions are evaluated by using the present cargo quantity accumulated value, and the solutions with better evaluation values are reserved.
After the initial solution is obtained, judging whether the initial solution meets the screening conditions or not according to a preset threshold value and an on-site cargo quantity accumulated value of the initial solution, and if not, updating the solution.
As an alternative embodiment, the present cargo quantity accumulated value，
wherein ,total number of departure lots for updated solutions of the current iteration +.>Is a positive integer>The +.f. of the updated solution for the current iteration>+.>Time present amount of goods->The +.f. of the updated solution for the current iteration>+.>Time of vehicle loading.
Calculating the present cargo quantity accumulation value of the updated solution of the current iteration and the present cargo quantity accumulation value of the solution of the last iteration according to the method, comparing the present cargo quantity accumulation value of the updated solution of each historical departure batch with a preset threshold, if the present cargo quantity accumulation value of the historical departure batch is larger than the preset threshold for a certain historical departure batch, indicating that the updated solution of the historical departure batch does not accord with the screening condition, reselecting a target operator from a random removal operator and a worst removal operator, disturbing departure time contained in the solution of the last iteration by using the target operator, obtaining the updated solution again, and repeating the process until the obtained updated solution accords with the screening condition.
If the present cargo quantity accumulated value of the updated solution is smaller than or equal to a preset threshold value, the updated solution meets the screening condition, and in this case, the solution of the current iteration is selected from the updated solution and the solution of the last iteration.
As an optional implementation manner, the selecting the solution of the current iteration from the updated solution of the current iteration and the solution of the previous iteration according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration includes:
if the present cargo quantity accumulated value of the updated solution of the current iteration is smaller than or equal to the present cargo quantity accumulated value of the solution of the previous iteration, selecting the updated solution of the current iteration as the solution of the current iteration;
otherwise, calculating an evaluation probability according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration, and generating a random number; if the evaluation probability is larger than the random number, selecting an updated solution of the current iteration as the solution of the current iteration, otherwise, selecting a solution of the previous iteration as the solution of the current iteration, and adding the newly increased departure time in the updated solution of the current iteration into a tabu table.
In the case where the updated solution satisfies the filtering condition, if the present cargo amount cumulative value of the updated solution is less than or equal to the present cargo amount cumulative value of the solution of the previous iteration, the updated solution is taken as the solution of the current iteration. If the presence volume cumulative value of the updated solution is greater than the presence volume cumulative value of the previous iteration, an evaluation probability is calculated.
As an alternative embodiment, the probability of evaluation；
wherein ,presence inventory accumulated value for solution of last iteration,/->Presence inventory accumulated value for updated solution of current iteration, +.>The temperature parameter of the current iteration of the simulated annealing algorithm is improved.
After calculating the evaluation probability according to the above formula, if the evaluation probability is larger than a random number, the random number is randomly generated and is between 0 and 1, including 0 and 1, the updated solution of the current iteration is taken as the solution of the current iteration, otherwise, the solution of the previous iteration is taken as the solution of the current iteration.
In addition, since the newly increased departure time in the updated solution performs worse, the newly increased departure time is added to the tabu table in order to avoid repeated calculations.
It should also be noted that, in the modified simulated annealing algorithm, the preset iteration stop conditions are as follows:
when going from the current iteration to the next iteration, the relevant iteration parameters are updated:
Iter=Iter-1；
Iter>0；
num≤L；
wherein Iter represents the current iteration times, num represents the current no-improvement times, namely when the current iteration solution is selected from the previous iteration solution and the current iteration update solution, the previous iteration solution is taken as the current iteration solution times, L is a preset no-improvement times threshold, and if the conditions of num less than or equal to L and Iter less than or equal to 0 are met, the next iteration is entered; otherwise, the iteration is jumped out, the cooling operation is carried out, and the cooling operation is specifically as follows:
，
，
，
，
wherein ,for the temperature of the current iteration, +.>For the cooling coefficient of the current iteration, +.>Is the maximum iteration number of the current temperature, +.>For the current cooling times, < >>Maximum number of times>Is a positive integer.
After each iteration, multiplying the iteration by a related coefficient, reducing the temperature, resetting the number Iter of the current temperature iteration, and reducing the temperature at the same timeAdd 1 cumulatively, then continue the next iteration until it is not satisfiedUntil the preset iteration stop condition is reached at this time.
After the iteration is finished, according to the final t 1 、t 2 、…、t n Determining the logistics batch departure time and obtaining the final target departure time. Such as t 1 ，t 2 ，t 3 = (15, 16, 17), then the final target departure time is 15: 00. 16:00 and 17:00.
In order to verify the effectiveness of the initial value of the median, the initial solution is obtained by counting the median time, and compared with the traditional method of using the mean value as the initial solution and the departure time with the highest frequency as the initial solution, table 1 is a schematic table comparing the results of the three initial solutions in the embodiment of the invention, as shown in table 1, it can be known from the table that the quasi-point rate of the scheme disclosed in the implementation is the highest.
In order to verify the effectiveness of two operators, namely a random removal operator and a worst removal operator, the scheme in the embodiment of the invention is compared with the traditional scheme which only adopts the random removal operator, and table 2 is a comparison table of the two schemes in the embodiment of the invention, and as shown in table 2, the time effect of the embodiment of the invention is higher and the search time is shorter as can be seen from table 2.
Example 2
Based on the above-mentioned logistic departure time calculation method, an embodiment of the present invention provides a logistic departure time calculation device, as shown in fig. 4, which includes an acquisition module 401, a median calculation module 402, and a prediction module 403, where:
the collection module 401 is configured to obtain a historical waybill corresponding to each departure line of the target site in a preset historical time period, where the historical waybill includes a historical departure batch and a historical departure time;
the median calculating module 402 is configured to use, for any historical departure lot, a median of a plurality of historical departure times corresponding to a current historical departure lot as an initial departure time of the current historical departure lot;
the prediction module 403 is configured to take an initial departure time of each historical departure lot as an initial solution of the improved simulated annealing algorithm, update and select a solution based on the initial solution iteration according to a loading rate and aging of the solution, and take the solution when a preset iteration stop condition is reached as a target departure time.
For other details of implementing the above technical solution by each module in the above device for calculating a logistic departure time, reference may be made to the description in the method for calculating a logistic departure time provided in the above embodiment of the present invention, which is not repeated here.
Based on the above-mentioned method for calculating the time of logistics departure, as shown in fig. 5, the embodiment of the invention further provides a schematic structural diagram of a device for calculating the time of logistics departure, where the identifying device includes a processor 51 and a memory 52 coupled to the processor 51. The memory 52 stores a computer program which, when executed by the processor 51, causes the processor 51 to perform the steps of one of the logistic departure time calculation methods in the above embodiments.
For further details of the implementation of the foregoing technical solution by the processor 51 in the foregoing logistics departure time calculating device, reference may be made to the description of the logistics departure time calculating method provided in the foregoing embodiment of the present invention, which is not repeated herein.
The processor 51 may also be called a CPU (Central Processing Unit ), and the processor 51 may be an integrated circuit chip with signal processing capability; the processor 51 may also be a general purpose processor, such as a microprocessor or the processor 51 may be any conventional processor, a DSP (Digital Signal Process, digital signal processor), ASIC (Application Specific Integrated Circuit ), FPGA (Field Programmable Gata Array, field programmable gate array) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components.
As shown in fig. 6, an embodiment of the present application further provides a schematic structural diagram of a computer-readable storage medium, on which a readable computer program 61 is stored; the computer program 61 may be stored in the storage medium in the form of a software product, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) or a processor (processor) to execute all or part of the steps of the methods according to the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a magnetic or optical disk, a ROM (Read-Only Memory), a RAM (Random Access Memory), or a terminal device such as a computer, a server, a mobile phone, or a tablet.
In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, and for example, the division of the modules is merely a logical function division, and there may be additional divisions when actually implemented, for example, multiple modules or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or modules, which may be in electrical, mechanical, or other forms.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present application, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by a wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be stored by a computer or a data storage device such as a server, data center, etc. that contains an integration of one or more available media. The usable medium may be a magnetic medium (e.g., a floppy Disk, a hard Disk, a magnetic tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a Solid State Disk (SSD)), or the like.
The above description has been made in detail for the technical solutions provided by the present application, and specific examples are applied in the present application to illustrate the principles and embodiments of the present application, and the above examples are only used to help understand the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.

Claims (9)

1. The method for calculating the logistics departure time is characterized by comprising the following steps of:
acquiring a history waybill corresponding to each departure line of a target site in a preset history time period, wherein the history waybill comprises a history departure batch and history departure time;
aiming at any historical departure batch, taking the median of a plurality of historical departure times corresponding to the current historical departure batch as the initial departure time of the current historical departure batch;
taking the initial departure time of each historical departure batch as an initial solution of an improved simulated annealing algorithm, updating and selecting solutions based on the initial solution iteration according to the loading rate and aging of the solutions, and taking the solutions when the preset iteration stop condition is reached as target departure time;
the updating of the solution based on the initial solution iteration according to the loading rate and the aging of the solution comprises the following steps:
for any iteration, randomly selecting a target operator from a preset random removal operator and a worst removal operator, wherein the worst removal operator is used for removing loading rate and/or time-lapse worst departure time;
and disturbing the solution of the previous iteration by using the target operator to obtain an updated solution of the current iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
2. The method of calculating a logistic departure time according to claim 1, wherein the selecting a solution based on the initial solution iteration according to a loading rate and an aging of the solution comprises:
for any iteration, calculating an on-site cargo quantity accumulated value of an updated solution of the current iteration, wherein the on-site cargo quantity accumulated value is used for measuring the loading rate and the time effect of the solution;
judging whether the updated solution of the current iteration accords with a screening condition or not according to a preset threshold value and an on-site cargo quantity accumulated value of the updated solution of the current iteration;
if not, updating the solution again;
and if so, selecting a solution of the current iteration from the updated solution of the current iteration and the solution of the previous iteration according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
3. The method for calculating a physical distribution departure time as recited in claim 2, wherein the present cargo quantity is a cumulative value，
wherein ,total number of departure lots for updated solutions of the current iteration +.>Is a positive integer>The +.f. of the updated solution for the current iteration>+. >Time present amount of goods->The +.f. of the updated solution for the current iteration>+.>Time of vehicle loading.
4. The method for calculating the time for logistics in departure according to claim 2, wherein the selecting the solution of the current iteration from the updated solution of the current iteration and the solution of the previous iteration according to the present inventory accumulation value of the updated solution of the current iteration and the present inventory accumulation value of the solution of the previous iteration comprises:
if the present cargo quantity accumulated value of the updated solution of the current iteration is smaller than or equal to the present cargo quantity accumulated value of the solution of the previous iteration, selecting the updated solution of the current iteration as the solution of the current iteration;
otherwise, calculating an evaluation probability according to the present cargo quantity accumulated value of the updated solution of the current iteration and the present cargo quantity accumulated value of the solution of the previous iteration, and generating a random number; if the evaluation probability is larger than the random number, selecting an updated solution of the current iteration as the solution of the current iteration, otherwise, selecting a solution of the previous iteration as the solution of the current iteration, and adding the newly increased departure time in the updated solution of the current iteration into a tabu table.
5. The method for calculating a physical distribution departure time according to claim 4, wherein the probability of evaluation ；
wherein ,presence inventory accumulated value for solution of last iteration,/->Presence inventory accumulated value for updated solution of current iteration, +.>The temperature parameter of the current iteration of the simulated annealing algorithm is improved.
6. The method for calculating a time to issue a logistics according to claim 1, further comprising, after the updating of the solution based on the initial solution iteration:
judging whether the newly increased departure time in the updated solution of any iteration is in the tabu list, and if so, updating the solution again.
7. The utility model provides a commodity circulation departure time calculation device which characterized in that, includes collection module, median calculation module and prediction module, wherein:
the acquisition module is used for acquiring a historical freight list corresponding to each departure line of the target site in a preset historical time period, wherein the historical freight list comprises historical departure batches and historical departure time;
the median calculating module is used for regarding the median of a plurality of historical departure times corresponding to the current historical departure batch as the initial departure time of the current historical departure batch;
the prediction module is used for taking the initial departure time of each historical departure batch as the initial solution of the improved simulated annealing algorithm, updating and selecting the solution based on the initial solution iteration according to the loading rate and the aging of the solution, and taking the solution when the preset iteration stop condition is reached as the target departure time;
The updating of the solution based on the initial solution iteration according to the loading rate and the aging of the solution comprises the following steps:
for any iteration, randomly selecting a target operator from a preset random removal operator and a worst removal operator, wherein the worst removal operator is used for removing loading rate and/or time-lapse worst departure time;
and disturbing the solution of the previous iteration by using the target operator to obtain an updated solution of the current iteration, wherein if the current iteration is the first iteration, the solution of the previous iteration is the initial solution.
8. A logistic departure time calculation device, comprising a memory and a processor, wherein:
the memory is used for storing a computer program;
the processor is configured to read the computer program in the memory, and execute the steps of the method for calculating a logistic departure time according to any one of claims 1 to 6.
9. A computer readable storage medium, characterized in that a readable computer program is stored thereon, which program, when being executed by a processor, implements the steps of the method for calculating a logistic departure time according to any one of claims 1 to 6.
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