CN112990528A - Logistics transportation stowage management method and device - Google Patents
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Abstract
The application discloses a logistics transportation stowage management method and device. Reading in order data, determining a loading sequence of goods according to goods attributes in the order data, and determining an optimal loading scheme according to the loading sequence and loading constraints by adopting a boxing algorithm; inputting the optimal loading scheme output by the packing algorithm into a path algorithm for path planning, outputting the optimal solution or the approximate optimal solution of the path algorithm, and taking the optimal solution as the optimal path planning; the path planning specifically comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; and at the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information. By adopting the logistics transportation stowage management method and device, the loading and delivery speed of logistics transportation can be improved by combining packing optimization and path optimization, the logistics transportation cost is greatly reduced, and the transportation efficiency is improved.
Description
Technical Field
The application relates to the technical field of logistics management, in particular to a logistics transportation stowage management method and device.
Background
Logistics management has been moving towards automation, high efficiency and low cost. Many logistics enterprises still use the sequence of containers coming first and coming last to place in the boxing process, so that although the boxing time is saved, a large amount of manpower, material resources and time are consumed to accurately obtain the required goods during transportation and boxing; similarly, most of goods are transported by random paths when being delivered from the warehouse, which wastes a lot of time cost in actual business.
In order to save time, some existing logistics enterprises begin to consider optimization on boxing and path selection, but generally only consider boxing or paths, and rarely combine paths and boxing, which mostly can only satisfy part of constraint conditions, and because both the path problem and the three-dimensional boxing problem are NP-Hard problems (all non-deterministic polynomial problems can be reduced within polynomial time complexity), the complexity of the problem is greatly improved by combining the path problem and the three-dimensional boxing problem, and the more constraint conditions, the smaller the solution space, the higher the performance requirement of the algorithm, and the less the actual business requirement can be satisfied.
Disclosure of Invention
The application provides a logistics transportation stowage management method, which comprises the following steps:
reading in order data, determining a loading sequence of goods according to goods attributes in the order data, and determining an order boxing scheme, a load utilization rate of a used vehicle and the space occupied by the whole goods by adopting a boxing algorithm according to the loading sequence and loading constraints;
performing path planning on an order boxing scheme output by a boxing algorithm, the load utilization rate of a used vehicle and the overall space occupied by the cargos by using an input path algorithm, and outputting an optimal solution or an approximately optimal solution of the path algorithm as an optimal path planning;
the optimal path planning comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; and at the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information.
The logistics transportation stowage management method as described above, wherein the cargo loading order is determined according to the volume and the friability degree of the cargo, including preferentially loading non-friable and bulky cargo to occupy a large feasible loading space, then loading small and non-friable cargo, and then loading small and friable cargo; in addition, for fragile or non-fragile products, the package is large.
The logistics transportation stowage management method as described above, wherein the loading constraints include space weight limit constraints, direction constraints, stability constraints and designated loading constraints;
space weight constraint, i.e. the total loading space and total cargo weight of each vehicle must not exceed the total volume and total capacity of the vehicle;
the direction constraint means that each cargo has fixed height and size, the edge of the cargo needs to be ensured to be parallel to the edge of the loading space, and the cargo must be completely and vertically loaded in the vehicle and can only horizontally rotate 90 degrees and can not be overturned;
the stability constraint is to ensure the safety of the vehicle in the driving process and ensure that the centers of the cargos are all concentrated in the range required by the center of the loading space;
the order for a given loading constraint, i.e., a given vehicle, must be loaded by the given vehicle.
The logistics transportation stowage management method comprises the following steps of performing path planning on an order stowage scheme output by a stowage algorithm, the load utilization rate of a used vehicle and the overall occupied space of the goods by using an input path algorithm, and outputting an optimal solution or an approximately optimal solution of the path algorithm as an optimal path plan:
setting a vehicle vector and an order sequence vector, and determining a vehicle path according to paired constraints, priority constraints and vehicle number constraint conditions of vehicle distribution;
checking whether the maximum cargo transportation quality on a vehicle path exceeds the rated load of a distribution vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an infeasible path, and deleting the path; if not, calling a larger first vehicle type until load constraint is met;
checking whether the occupied space of the goods on the path exceeds the volume constraint of the vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an unavailable path, and deleting the path; if not, calling a larger first vehicle type until the volume constraint is met;
and checking the distribution time constraint, if the time consumed by the vehicle for completing all distribution tasks exceeds the longest allowable use time of the vehicle, increasing the corresponding overtime penalty cost, reserving the path, and finally obtaining the optimal solution or the approximate optimal solution of the path planning through continuous iteration.
The logistics transportation stowage management method comprises the following steps of solving an optimal solution or an approximately optimal solution of vehicle path planning by adopting an improved genetic algorithm:
selecting a fitness function, and outputting a fitness value according to the fitness function:
and selecting excellent individuals from the initial population by the selection operator according to the adaptive value to form an output population:
inputting the output population of the selection operator into a crossover operator for population crossover treatment:
inputting the population processed by the crossover operator into a mutation operator, and performing mutation operation on individual population by the mutation operator:
and combining the varied population with an elite retention strategy to form a new population, selecting the optimal individual in the current new population, and taking the optimal individual as the optimal solution or the approximately optimal solution for path planning.
The application still provides a commodity circulation transportation stowage management device, includes:
the container loading optimization unit is used for reading in order data, determining the loading sequence of the goods according to the goods attributes in the order data, and determining an order container loading scheme, the load utilization rate of the used vehicle and the space occupied by the whole goods by adopting a container loading algorithm according to the loading sequence and the loading constraint;
the path optimization unit is used for inputting the order boxing scheme output by the boxing algorithm, the load utilization rate of the used vehicle and the overall space occupied by the cargos into the path algorithm for path planning, and outputting the optimal solution or the approximate optimal solution of the path algorithm as the optimal path planning; the optimal path planning comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; and at the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information.
The logistics transportation stowage management device comprises a loading sequence optimizing module, a loading sequence optimizing module and a loading sequence optimizing module, wherein the loading sequence optimizing module is used for determining a loading sequence of cargos according to the volume and the friability degree of the cargos, and comprises the steps of preferentially loading non-friable cargos with large volume so as to occupy large feasible loading space, then loading small-volume non-friable cargos and then loading small-volume friable cargos; in addition, for fragile or non-fragile products, the package is large.
The logistics transportation stowage management device comprises a container loading optimization unit, a container loading optimization unit and a container loading optimization unit, wherein the container loading optimization unit comprises a loading constraint optimization module, and specifically comprises a space weight limit constraint, a direction constraint, a stability constraint and a specified loading constraint; space weight constraint, i.e. the total loading space and total cargo weight of each vehicle must not exceed the total volume and total capacity of the vehicle; the direction constraint means that each cargo has fixed height and size, the edge of the cargo needs to be ensured to be parallel to the edge of the loading space, and the cargo must be completely and vertically loaded in the vehicle and can only horizontally rotate 90 degrees and can not be overturned; the stability constraint is to ensure the safety of the vehicle in the driving process and ensure that the centers of the cargos are all concentrated in the range required by the center of the loading space; the order for a given loading constraint, i.e., a given vehicle, must be loaded by the given vehicle.
The logistics transportation stowage management device comprises a route optimization unit, a route calculation unit and a route calculation unit, wherein the route optimization unit is specifically used for setting a vehicle vector and an order sequence vector, and determining a vehicle route according to paired constraints, priority constraints and vehicle number constraint conditions of vehicle delivery; checking whether the maximum cargo transportation quality on a vehicle path exceeds the rated load of a distribution vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an infeasible path, and deleting the path; if not, calling a larger first vehicle type until load constraint is met; checking whether the occupied space of the goods on the path exceeds the volume constraint of the vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an unavailable path, and deleting the path; if not, calling a larger first vehicle type until the volume constraint is met; and checking the distribution time constraint, if the time consumed by the vehicle for completing all distribution tasks exceeds the longest allowable use time of the vehicle, increasing the corresponding overtime penalty cost, reserving the path, and finally obtaining the optimal solution or the approximate optimal solution of the path planning through continuous iteration.
The logistics transportation stowage management device as described above, wherein the path optimization unit is specifically configured to solve the optimal solution or the approximately optimal solution of the vehicle path planning by using an improved genetic algorithm, and specifically includes selecting a fitness function, and outputting a fitness value according to the fitness function: and selecting excellent individuals from the initial population by the selection operator according to the adaptive value to form an output population: inputting the output population of the selection operator into a crossover operator for population crossover treatment: inputting the population processed by the crossover operator into a mutation operator, and performing mutation operation on individual population by the mutation operator: and combining the varied population with an elite retention strategy to form a new population, selecting the optimal individual in the current new population, and taking the optimal individual as the optimal solution or the approximately optimal solution for path planning.
The beneficial effect that this application realized is as follows: by adopting the logistics transportation stowage management method and device, the loading and delivery speed of logistics transportation can be improved by combining packing optimization and path optimization, the logistics transportation cost is greatly reduced, and the transportation efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments described in the present invention, and other drawings can be obtained by those skilled in the art according to the drawings.
Fig. 1 is a flowchart of a logistics transportation stowage management method according to an embodiment of the present application;
FIG. 2 is a flowchart illustrating the detailed operation of inputting the output of the binning algorithm into the path algorithm;
fig. 3 is a schematic view of a logistics transportation stowage management device according to a second embodiment of the present application.
Detailed Description
Example one
In the logistics transportation stowage, the three-dimensional packing problem and the path problem are NP-Hard problems, and in consideration of the existing order quantity and the actual demand of more goods, an embodiment of the present application provides a logistics transportation stowage management method, as shown in fig. 1, including the following steps:
the optimal loading scheme comprises an order boxing scheme, a vehicle loading scheme and a cargo loading scheme; the order packing scheme is the loading position of each cargo, including but not limited to that the same customer must be served by the same vehicle, and all the cargos of all the customers must be delivered; vehicle load scenarios include, but are not limited to, maximum load utilization of the vehicle, maximum space utilization of the vehicle, load and load volume per vehicle; cargo loading schemes include, but are not limited to, loading of cargo parallel to the car, cargo not being able to be inverted in the car, cargo weight range loaded in the car must be within the vehicle center of gravity requirement, cargo loaded on the vehicle cannot exceed the car size range, and cargo on the same travel path must meet down-first and up-last constraints.
In the embodiment of the application, the optimal loading scheme is determined by adopting a boxing algorithm according to the loading sequence and the loading constraint, and the method specifically comprises the following substeps:
step 111, determining the loading sequence of the goods according to the goods attributes in the order data;
specifically, the loading sequence of the cargos is preferably determined according to the volume and the friability degree of the cargos; for example, the cargo loading sequence is to load non-fragile and bulky cargo preferentially to occupy a large feasible loading space, then load less bulky and non-fragile cargo, and then load less bulky and fragile cargo; in addition, for fragile or non-fragile products, the product is firstly packed with large volume;
step 112, searching an optimal loading position from the remaining feasible loading positions in real time according to the loading sequence and the loading constraint, and updating the remaining feasible loading positions;
specifically, the initial feasible loading position is predefined to be at a (0,0,0) point, the filling is started from the deepest position by adopting a Back-Left-Low guided sorting method, namely, the filling is started from the position with the minimum y value, when the y values are the same, the position with the minimum z value is searched, and if the y values and the z values are the same, the position with the minimum x value is searched.
After loading the first piece of goods according to the loading sequence, updating the remaining feasible loading positions according to the length, the width and the height of the piece of goods; for the next goods to be loaded in the loading sequence, acquiring the sequence of all feasible loading positions, and scanning one by one until finding out an optimal loading position which meets all loading constraints;
wherein the loading constraints include space weight limit constraints, orientation constraints, stability constraints, and specified loading constraints; in particular, space weight constraints are the total loading space and total cargo weight per vehicleMust not exceed the total volume and capacity of the vehicle; the direction constraint means that each cargo has fixed height and size, the edge of the cargo needs to be ensured to be parallel to the edge of the loading space, and the cargo must be completely and vertically loaded in the vehicle and can only horizontally rotate 90 degrees and can not be overturned; the stability constraint is to ensure the safety of the vehicle in the driving process and ensure that the centers of the cargos are all concentrated in the range required by the center of the loading space; order for a given load constraint, i.e. a given vehicle, must then be loaded by the given vehicle, e.g. two orders i and j, i/j indicating that order number vehicle k visits i before j, letting ni，njDevices belonging to i and j, respectively, then niCannot be placed at njAnd the rear of the loading space or njUpper side;
in addition, for with follow-up route optimization mode matching, improve the car length utilization ratio, this application still carries out the sequencing to the goods size and handles, promptly: the short edge of the bottom surface of the goods is used as the length, the long edge of the bottom surface of the goods is used as the width, if the short edge of the goods is parallel to the long edge of the carriage, the placing mode of the goods is recorded to be 0, and if the long edge of the goods is parallel to the long edge of the carriage, the placing mode of the goods is recorded to be 1.
For example, there are k different types of trucks, each with a rated load weight and maximum volume of GkAnd VkThe length, width and height of the truck are respectively indicated by Lk、Wk、HkDenotes, TiIndicates the quantity of goods, T, required for the ith orderi＝{1,2……tiH, the length, width, height and weight of the t-th goods of the ith order are respectively lit、wit、hit、gitDenotes xit、yit、zitRepresenting ordersiTo (1) atThe center of gravity of the piece of cargo in the vehicle;
respectively representing the coordinates of the right rear lower corner of the t piece goods of the order i in the carriage on the k vehicle;x itk、y itk、z itkt-th cargo items respectively representing orders i on k-th vehiclesCoordinates of the upper left front corner in the carriage; (a ', a'), (b ', b'), and (c ', c') indicate the gravity center range of the vehicle, and are determined according to the specification of the vehicle;
the optimal decision under each loading constraint is calculated separately using the following formula:
equation (1) for calculating the maximum load utilization f of a vehicle during distribution1；
Equation (2) for calculating the maximum space utilization f of a vehicle during distribution2；
The formula (3) represents that the load of each vehicle cannot exceed the rated load mass required by the vehicle;
equation (4) represents that the loading volume of each vehicle cannot exceed the maximum volume required by the vehicle;
equations (5) and (6) indicate that the cargo loaded on the vehicle must be loaded in parallel to the vehicle compartment;
the formula (7) shows that the goods cannot be inverted in the carriage;
equations (8) to (10) indicate that the weight range of the cargo loaded in the vehicle compartment must be within the range required by the center of gravity of the vehicle;
equations (11) to (13) indicate that the cargo loaded on the vehicle cannot exceed the compartment size range;
equation (14) indicates that the same customer must be serviced by the same vehicle;
equation (15) indicates that all the goods of all the customers must be delivered;
when r isijkWhen the number is equal to 1, the alloy is put into a container,
and i is not equal to j,
When r isijkWhen the content is equal to 0, the content,
and i is not equal to j,
Equations (16) and (17) indicate that the cargo on the same travel path must satisfy the down-first and up-last constraints;
equations (18) to (20) represent three decision variables; wherein, yik: a variable of 0-1, if the vehicle k serves the order i, it is 1, otherwise it is 0; z is a radical ofitk: a variable of 0-1, which is 1 if the t-th cargo of the order i is loaded on the vehicle k, otherwise, is 0; r isijk: a variable of 0 to 1, which is 1 if the order i is served before the order i is served on the vehicle k, otherwise, is 0;
the present application finds the optimal decision that satisfies all loading constraints as the optimal loading position according to the above formula, then updates the remaining feasible loading positions according to the size of the optimal loading position, and then continues to execute step 112 until the order is completed.
Referring back to fig. 1, step 130, inputting the optimal loading scheme output by the packing algorithm into the path algorithm for path planning, outputting the optimal solution or the approximately optimal solution of the path algorithm, and taking the optimal solution as the optimal path planning; the path planning specifically comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; in the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information;
in the embodiment of the present application, as shown in fig. 2, after the output result of the binning algorithm is input into the path algorithm, the following operations are specifically performed:
the method comprises the steps that a specific natural number coding mode is set, and the coding is represented by a vehicle vector and an order sequence vector; during vehicle distribution, firstly, pair constraints must be met, namely after a vehicle completes a goods taking task of an order, a discharging point of the order must be visited; priority constraint, namely, the vehicle must complete the order starting point goods taking task first and then complete the order end point goods delivery task; the number of the vehicles is restricted, namely the number of the calling vehicles cannot exceed the number of the vehicles which can be controlled currently by the vehicle type;
for example, suppose there are two vehicle types, vehicle type 1 can dominate 4 vehicles, and the number is {0,1,2,3 }; model 2 may contain 2 vehicles, numbered 4,5, and 8 orders, numbered 1,2, …,8, which are shown in table 1 below:
| order numbering | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Starting point | A | C | E | G | I | H | M | O |
| End point | B | D | F | H | J | L | N | N |
TABLE 1
The dispensing schedule is shown in table 2 below:
| order numbering | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | ||||||||
| Vehicle vector | 3 | 0 | 3 | 5 | 0 | 5 | 3 | 3 | ||||||||
| Order sequence vector | 1 | 2 | -2 | -1 | 3 | -3 | 4 | -4 | 5 | -5 | 8 | 7 | -8 | -7 | 6 | -6 |
TABLE 2
As can be seen from table 2, the vehicle vector value corresponding to the order number represents the vehicle code to service the customer, e.g., orders 1, 3, 7, and 8 are delivered by vehicle type 1 vehicle 3, orders 2 and 5 are delivered by vehicle type 1 vehicle 0, and orders 4 and 6 are serviced by vehicle type 2 vehicle 5; the order vector represents the order of the vehicle to deliver the order, for example, the order sequence vector corresponding to the vehicle 3 is (1-13-387-8-7), which represents that the vehicle 3 first loads the order 1 from the warehouse a and transports to the warehouse B to unload the order 1, and completes the delivery of the order 1; then, the warehouse E is carried to load the order 3, and the order is conveyed to the warehouse F to unload the order 3; finally, go to warehouse M, warehouse O to load order 8 and order 7, and transfer to warehouse N to unload orders 7 and 8; by this time, the vehicle 3 has completed all the order deliveries. Thus, after decoding the above chromosomes, three vehicle paths are obtained as shown in table 3 below:
| 1# vehicle 3 | A(1)→B(-1)→E(3)→F(-3)→M(8)→O(7)→N(-7/-8) |
| 1# vehicle 0 | C(2)→D(-2)→I(5)→J(-5) |
| 2# vehicle 5 | G(4)→H(-4/6)→L(-6) |
TABLE 3
in the embodiment of the application, an improved genetic algorithm is adopted to solve the optimal solution or the approximate optimal solution of the path planning, and the method specifically comprises the following operations:
the embodiment of the application preferably adopts an improved genetic algorithm to solve the optimal solution or the approximately optimal solution of the vehicle path planning, and specifically comprises the following operations:
selecting a fitness function, and outputting a fitness value according to the fitness function:
according to the method, the load allocation is carried out in a genetic algorithm of path planning according to an order, the actual conditions of multi-vehicle type constraint, vehicle number constraint, paired constraint, priority constraint, vehicle load constraint, vehicle volume constraint, first-in last-out constraint, total vehicle transportation duration and the like in actual service are considered, the total transportation cost is calculated according to the gradient prices of different vehicle types and serves as a target function, a path distribution scheme is finally output, and the return trip mileage and the cost can be selected not to be calculated according to the actual service condition; taking the reciprocal of the objective function as chromosome fitness, wherein the fitness function is used for processing the loading load, the volume constraint and the delivery duration constraint of the vehicle;
specifically, according to the relationship between the vehicle route and the load and volume constraints of the vehicle type and the vehicle distribution duration, different fitness functions are selected, specifically:
if the vehicle path does not meet the load and volume constraints of the current vehicle type, calling a larger first vehicle type on the basis of meeting the constraints of the vehicle, and when the vehicle path meets the capacity constraints of the vehicle and a distribution center, selecting a fitness function as shown in the following formula:
wherein d iskIs the mileage of vehicle k, p (d)k) Expressing the corresponding gradient price, K expressing the total number of the called vehicles, and f expressing the calculated fitness;
if the vehicle delivery duration exceeds a set duration (e.g., each vehicle is set to complete delivery within 12.5 hours), a corresponding penalty cost is added, and the fitness function is selected as follows:
wherein, tkTime to complete all delivery tasks for the vehicle, tsFor the preset allowable delivery time length, P is a penalty factor which is a sufficiently large constant, and f is the calculated fitness.
Selecting excellent individuals from the initial population to form an output population according to the adaptive value by a selection operator:
the selection operator determines which individuals are selected from the population to be used for reproducing new individuals in the next generation population, and selects excellent individuals from the initial population according to the individual fitness to form an output population, which specifically comprises the following steps: adopting a roulette selection strategy in a genetic algorithm, calculating the probability of each individual being inherited to a next generation group according to the fitness of each individual in the calculated group according to the proportional relation between the selected probability and the fitness of the individual, and then calculating the cumulative probability of each individual; for example, a uniformly distributed pseudo random number r is generated in the [0,1] interval; if r < q [ k ], selecting an individual k, otherwise, judging the next individual and guiding to select the individual of q > r; repeating the steps until a new population is formed.
Thirdly, inputting the output population of the selection operator into a crossover operator for population crossover treatment:
in order to improve the effect of the genetic algorithm, the self-adaptive cross probability is set, so that the cross probability can be automatically changed along with the fitness value; when the group fitness values tend to be consistent or tend to be locally optimal, the cross probability is increased, and when the group fitness values are dispersed, the cross probability is reduced;
further, corresponding cross operations are respectively designed according to the attribute characteristics of the two gene segments:
using the vehicle vector as a first part gene segment, wherein the part allows repeated genes to exist, so a PMX crossing method is adopted, a pair of chromosomes (parents) are selected according to crossing probability, two demarcation points c1 and c2 are randomly selected as starting and stopping points, and gene segments between the two demarcation points c1 and c2 are exchanged to form two new filial generation gene segments;
taking the order sequence vector as a second part of gene segment, selecting an improved crossover strategy to avoid the condition that two parent chromosomes are the same and increase the diversity of the population, specifically selecting a pair of chromosomes (parents) according to crossover probability, and randomly selecting two demarcation points c1 and c2, wherein the two demarcation points are crossed gene segments; transferring the gene segment of the first parent chromosome to the tail part of the second parent chromosome, and transferring the second parent chromosome to the head part of the first parent chromosome; and correcting the error part to delete the repeated conflicting genes in the chromosome, wherein when the parent individuals are the same, the crossover operator can also generate two different offspring gene segments.
Fourthly, inputting the population processed by the crossover operator into a mutation operator, and performing mutation operation on individual population by the mutation operator:
wherein, the mutation operation of the population individuals by the mutation operator is specifically to randomly generate two variation points and exchange genes at the positions of the variation points to obtain new individuals.
Combining the varied population with an elite retention strategy to form a new population, selecting the optimal individual in the current new population as the optimal solution or the approximate optimal solution for path planning:
in order to prevent the optimal individual of the current group from losing in the next generation, which causes the genetic algorithm to be incapable of converging to the global optimal solution, an elite reservation strategy is introduced into the genetic algorithm, the best individual of the group which appears so far in the evolution process is directly copied into the next generation, the worst individual in the next generation is replaced by the optimal individual, the global convergence capability of the algorithm is improved, continuous iteration is carried out, and the output result when the maximum iteration times is reached is used as the optimal solution or the approximate optimal solution of the path planning;
in the embodiment of the application, the path algorithm aims at the lowest total transportation cost based on the gradient price, and obtains an optimal solution or an approximately optimal solution (when the problem scale is large) under the constraints of the number of vehicles, the types of the vehicles, the load, the volume, the goods taking and delivery, the visiting sequence, the total time and the like, wherein the optimal solution or the approximately optimal solution output by the path algorithm comprises the total mileage, the total transportation cost, the delivery route of the vehicles and the corresponding vehicle types, the vehicle index, the delivery cost, the vehicle driving mileage, the time consumption, the utilization rate change of the loads of the vehicles and the vehicle length, the visiting sequence of each warehouse and the order loading and unloading sequence and the sequence of multiple operations in the.
Example two
An embodiment of the present application provides a logistics transportation stowage management device, as shown in fig. 3, including:
the boxing optimization unit 31 is configured to read in order data, determine a loading sequence of the goods according to the goods attributes in the order data, and determine an optimal loading scheme according to the loading sequence and loading constraints by using a boxing algorithm;
the path optimization unit 32 is configured to input the optimal loading scheme output by the packing algorithm into a path algorithm for path planning, output an optimal solution or an approximately optimal solution of the path algorithm, and plan the optimal path; the path planning specifically comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; and at the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information.
The boxing optimization unit 31 includes a loading sequence optimization module 311, which is specifically configured to determine a loading sequence of the cargo according to the volume and the friability of the cargo, including preferentially loading non-fragile and bulky cargo to occupy a large feasible loading space, then loading small-volume and non-fragile cargo, and then loading small-volume and fragile cargo; in addition, for fragile or non-fragile products, the package is large.
In addition, the packing optimization unit 31 further includes a loading constraint optimization module 312, which specifically includes a space weight limit constraint, a direction constraint, a stability constraint, and a specified loading constraint; space weight constraint, i.e. the total loading space and total cargo weight of each vehicle must not exceed the total volume and total capacity of the vehicle; the direction constraint means that each cargo has fixed height and size, the edge of the cargo needs to be ensured to be parallel to the edge of the loading space, and the cargo must be completely and vertically loaded in the vehicle and can only horizontally rotate 90 degrees and can not be overturned; the stability constraint is to ensure the safety of the vehicle in the driving process and ensure that the centers of the cargos are all concentrated in the range required by the center of the loading space; the order for a given loading constraint, i.e., a given vehicle, must be loaded by the given vehicle.
In the embodiment of the present application, the path optimizing unit 32 is specifically configured to set a vehicle vector and an order sequence vector, and determine a vehicle path according to paired constraints, priority constraints, and vehicle number constraints of vehicle distribution; checking whether the maximum cargo transportation quality on a vehicle path exceeds the rated load of a distribution vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an infeasible path, and deleting the path; if not, calling a larger first vehicle type until load constraint is met; checking whether the occupied space of the goods on the path exceeds the volume constraint of the vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an unavailable path, and deleting the path; if not, calling a larger first vehicle type until the volume constraint is met; and checking the distribution time constraint, if the time consumed by the vehicle for completing all distribution tasks exceeds the longest allowable use time of the vehicle, increasing the corresponding overtime penalty cost, reserving the path, and finally obtaining the optimal solution or the approximate optimal solution of the path planning through continuous iteration.
Further, the path optimizing unit 32 is specifically configured to solve the optimal solution or the approximately optimal solution of the vehicle path planning by using an improved genetic algorithm, and specifically includes selecting a fitness function, and outputting an adaptation value according to the fitness function: and selecting excellent individuals from the initial population by the selection operator according to the adaptive value to form an output population: inputting the output population of the selection operator into a crossover operator for population crossover treatment: inputting the population processed by the crossover operator into a mutation operator, and performing mutation operation on individual population by the mutation operator: and combining the varied population with an elite retention strategy to form a new population, selecting the optimal individual in the current new population, and taking the optimal individual as the optimal solution or the approximately optimal solution for path planning.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application. Although the identification method and system of the order are disclosed in the present application, other logistics documents with different formats can be identified by the identification method of the present application, and it is obvious that various changes and modifications can be made to the present application by those skilled in the art without departing from the spirit and scope of the present application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (10)
1. A logistics transportation stowage management method is characterized by comprising the following steps:
reading in order data, determining the loading sequence of goods according to the goods attributes in the order data, and determining an optimal loading scheme according to the loading sequence and loading constraints by adopting a boxing algorithm;
inputting the optimal loading scheme output by the packing algorithm into a path algorithm for path planning, outputting the optimal solution or the approximate optimal solution of the path algorithm, and taking the optimal solution as the optimal path planning;
the path planning specifically comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; and at the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information.
2. The logistics transportation stowage management method of claim 1 wherein determining the order of loading the cargo based on the volume and fragility of the cargo comprises preferentially loading non-fragile and bulky cargo to occupy a large feasible loading space, then loading less bulky and non-fragile cargo, and then loading less bulky and fragile cargo; in addition, for fragile or non-fragile products, the package is large.
3. The logistics transportation stowage management method of claim 1 wherein the loading constraints include space weight limit constraints, orientation constraints, stability constraints, and designated loading constraints;
space weight constraint, i.e. the total loading space and total cargo weight of each vehicle must not exceed the total volume and total capacity of the vehicle;
the direction constraint means that each cargo has fixed height and size, the edge of the cargo needs to be ensured to be parallel to the edge of the loading space, and the cargo must be completely and vertically loaded in the vehicle and can only horizontally rotate 90 degrees and can not be overturned;
the stability constraint is to ensure the safety of the vehicle in the driving process and ensure that the centers of the cargos are all concentrated in the range required by the center of the loading space;
the order for a given loading constraint, i.e., a given vehicle, must be loaded by the given vehicle.
4. The logistics transportation stowage management method according to claim 1, wherein the path planning is performed by using an order stowage scheme output by a stowage algorithm, a load utilization rate of a used vehicle and an input path algorithm of a space occupied by the whole cargo, and an optimal solution or an approximately optimal solution of the output path algorithm is used as an optimal path planning, and the method specifically comprises the following substeps:
setting a vehicle vector and an order sequence vector, and determining a vehicle path according to paired constraints, priority constraints and vehicle number constraint conditions of vehicle distribution;
checking whether the maximum cargo transportation quality on a vehicle path exceeds the rated load of a distribution vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an infeasible path, and deleting the path; if not, calling a larger first vehicle type until load constraint is met;
checking whether the occupied space of the goods on the path exceeds the volume constraint of the vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an unavailable path, and deleting the path; if not, calling a larger first vehicle type until the volume constraint is met;
and checking the distribution time constraint, if the time consumed by the vehicle for completing all distribution tasks exceeds the longest allowable use time of the vehicle, increasing the corresponding overtime penalty cost, reserving the path, and finally obtaining the optimal solution or the approximate optimal solution of the path planning through continuous iteration.
5. The logistics transportation stowage management method of claim 1, wherein the improved genetic algorithm is adopted to solve the optimal solution or the approximately optimal solution of the vehicle path planning, and specifically comprises the following operations:
selecting a fitness function, and outputting a fitness value according to the fitness function:
and selecting excellent individuals from the initial population by the selection operator according to the adaptive value to form an output population:
inputting the output population of the selection operator into a crossover operator for population crossover treatment:
inputting the population processed by the crossover operator into a mutation operator, and performing mutation operation on individual population by the mutation operator:
and combining the varied population with an elite retention strategy to form a new population, selecting the optimal individual in the current new population, and taking the optimal individual as the optimal solution or the approximately optimal solution for path planning.
6. A logistics transportation stowage management device is characterized by comprising:
the boxing optimization unit is used for reading in order data, determining the loading sequence of the goods according to the goods attributes in the order data, and determining an optimal loading scheme according to the loading sequence and loading constraints by adopting a boxing algorithm;
the path optimization unit is used for inputting the optimal loading scheme output by the boxing algorithm into the path algorithm for path planning, outputting the optimal solution or the approximate optimal solution of the path algorithm and taking the optimal solution as the optimal path planning; the path planning specifically comprises the steps of ensuring that constraint conditions of the path are met in an encoding stage and an initial solution generation stage of a path algorithm; and at the fitness function stage of the path algorithm, vehicle time window constraint, load constraint and volume constraint judgment are carried out according to the order goods information.
7. The logistics transportation stowage management device of claim 6 wherein the bin optimization unit comprises a stowage order optimization module, and is specifically configured to determine a stowage order of the cargos according to the volume and the friability of the cargos, wherein the stowage order comprises preferentially stowing non-friable and bulky cargos to occupy a large feasible stowage space, then stowing less bulky and non-friable cargos, and then stowing less bulky and friable cargos; in addition, for fragile or non-fragile products, the package is large.
8. The logistics transportation stowage management device of claim 6 wherein the bin packing optimization unit comprises a stowage constraint optimization module, specifically comprising a space weight limit constraint, an orientation constraint, a stability constraint and a designated stowage constraint; space weight constraint, i.e. the total loading space and total cargo weight of each vehicle must not exceed the total volume and total capacity of the vehicle; the direction constraint means that each cargo has fixed height and size, the edge of the cargo needs to be ensured to be parallel to the edge of the loading space, and the cargo must be completely and vertically loaded in the vehicle and can only horizontally rotate 90 degrees and can not be overturned; the stability constraint is to ensure the safety of the vehicle in the driving process and ensure that the centers of the cargos are all concentrated in the range required by the center of the loading space; the order for a given loading constraint, i.e., a given vehicle, must be loaded by the given vehicle.
9. The logistics transportation stowage management device of claim 6 wherein the path optimization unit is specifically configured to set a vehicle vector and an order sequence vector, and determine the vehicle path according to paired constraints, priority constraints and vehicle number constraints of vehicle delivery; checking whether the maximum cargo transportation quality on a vehicle path exceeds the rated load of a distribution vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an infeasible path, and deleting the path; if not, calling a larger first vehicle type until load constraint is met; checking whether the occupied space of the goods on the path exceeds the volume constraint of the vehicle, if so, further judging whether the vehicle type is the maximum vehicle type, if so, determining that the path is an unavailable path, and deleting the path; if not, calling a larger first vehicle type until the volume constraint is met; and checking the distribution time constraint, if the time consumed by the vehicle for completing all distribution tasks exceeds the longest allowable use time of the vehicle, increasing the corresponding overtime penalty cost, reserving the path, and finally obtaining the optimal solution or the approximate optimal solution of the path planning through continuous iteration.
10. The logistics transportation stowage management device of claim 6, wherein the path optimization unit is specifically configured to solve the vehicle path planning optimal solution or the approximate optimal solution by using an improved genetic algorithm, and specifically includes selecting a fitness function, and outputting an adaptive value according to the fitness function: and selecting excellent individuals from the initial population by the selection operator according to the adaptive value to form an output population: inputting the output population of the selection operator into a crossover operator for population crossover treatment: inputting the population processed by the crossover operator into a mutation operator, and performing mutation operation on individual population by the mutation operator: and combining the varied population with an elite retention strategy to form a new population, selecting the optimal individual in the current new population, and taking the optimal individual as the optimal solution or the approximately optimal solution for path planning.
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