CN115130967A - Method and system for arranging and optimizing distribution lists and paths of assembled and integrated home logistics with time windows - Google Patents
Method and system for arranging and optimizing distribution lists and paths of assembled and integrated home logistics with time windows Download PDFInfo
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Abstract
The invention relates to a method and a system for arranging logistics lists and optimizing paths of assembled integrated home with time windows, wherein the method comprises the following steps: s1: establishing an objective function and constraint conditions of a home logistics distribution model based on assembly integration: customer constraints, worker constraints, vehicle constraints, and skill and time window constraints; s2: solving the model by adopting an improved ant colony algorithm, and planning M optimal paths as alternative paths; substituting customer constraint, worker constraint, vehicle constraint, skill and time window constraint into alternative path planning to perform logistics ordering, taking path planning meeting all constraint conditions as path planning, and obtaining corresponding logistics ordering; and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained. The method provided by the invention adopts the ant colony algorithm to carry out the lowest-cost path planning and realizes the optimal intelligent order arrangement by establishing constraint conditions.
Description
Technical Field
The invention relates to the technical field of intelligent logistics, in particular to a method and a system for arranging and optimizing a distribution list and a path of a home furnishing with a time window.
Background
With the development of intelligent logistics, the intelligent logistics management level becomes a typical competitive advantage of enterprises. The comprehensive furniture service platform is used for further improving the intelligent service level, realizing refined operation and reducing the operation cost, and intelligent logistics distribution of the last kilometer needs to be realized. In fact, the intelligent delivery of the furniture industry needs to consider a plurality of factors such as customer requirements, product attributes, vehicle attributes, professional technical attributes, personnel combination arrangement, vehicle scheduling, path planning, service time windows, and technical noon break time windows, but in the current research and invention, the factors are mainly considered and single and partial influence factors, so that the intelligent management needs of the furniture comprehensive service platform cannot be met.
Disclosure of Invention
In order to solve the technical problems, the invention provides a method and a system for optimizing logistics list and path of a home integrated with time window.
The technical solution of the invention is as follows: a method for arranging lists and optimizing paths of assembled integrated home logistics with time windows comprises the following steps:
step S1: establishing an objective function and a constraint condition of a home logistics distribution model based on an assembly integration: customer constraints, worker constraints, vehicle constraints, and skill and time window constraints;
step S2: solving the model by adopting an improved ant colony algorithm, and planning M optimal paths as alternative paths; substituting the client constraint, the worker constraint, the vehicle constraint, the skill and the time window constraint into the alternative path planning to carry out logistics ordering, taking the path planning meeting all the constraint conditions as the path planning, and obtaining a corresponding logistics ordering; and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
Compared with the prior art, the invention has the following advantages:
the invention discloses a method for arranging and optimizing a path of a home furnishing logistics with a time window in an integrated manner, which aims at the problems of intelligent arranging and path planning of home furnishing logistics distribution with an integrated manner established by a furniture comprehensive service platform, adopts an ant colony algorithm to plan a path with the lowest cost, realizes the optimal intelligent arranging by establishing constraint conditions of customer constraint, worker constraint, vehicle constraint and skill and time window constraint, and can simultaneously meet the requirements of customer service time windows and noon break requirements of workers.
Drawings
FIG. 1 is a flow chart of a method for arranging and optimizing a path of a fitted integrated home logistics with a time window according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an optimal distribution path scheme according to an embodiment of the present invention;
FIG. 3 is a comparison graph of resource occupancy for algorithm and manual ordering in an embodiment of the present invention;
FIG. 4 is a comparison graph of the working hours of workers performing algorithm and manual ordering in an embodiment of the present invention;
FIG. 5 is a graph comparing stability of an algorithm and a manual ordering in an embodiment of the present invention;
FIG. 6 is a comparison graph of total cost of manual and algorithmic ordering in an embodiment of the present invention;
fig. 7 is a comparison diagram of total travel paths of manual and algorithm lists according to an embodiment of the invention.
Detailed Description
The invention provides a method for sorting and optimizing a path of a home logistics integrated with time window assembling, which adopts an ant colony algorithm to plan a path with the lowest cost, and realizes the optimal intelligent sorting by establishing constraint conditions of customer constraint, worker constraint, vehicle constraint and skill and time window constraint.
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings.
The embodiment of the invention is based on the following scenes:
a distribution center, a distribution team, a plurality of customer points;
assembling workers: different functional roles including: pure drivers, drivers are installed simultaneously, only installed;
private car: the self vehicle of the assembly worker is assigned and can only be used for the assembly worker and the combination of the assembly worker, and the assembly worker returns to the self house (namely a virtual terminal) after the service is finished;
the bus: the vehicle team appointed for reservation by the distribution center can be used by all assembly workers, and the vehicle team returns to the distribution center after the service is finished;
a service unit: the system consists of a vehicle and a plurality of assembling workers, and a service unit is provided with a vehicle driving right and a certain installation skill; each service unit starts from the distribution center, returns home if the service is finished, and returns to the distribution center if the service is finished, and the service unit can carry out secondary departure.
Stipulating: each customer demand can only be serviced by the same service unit and only once. The service units have a working time limit, each worker has a fixed installation skill, and there are differences in skill levels. The goal is to achieve cost minimization while meeting customer constraints, worker constraints, vehicle constraints, and skill and time window constraints.
The model assumptions are as follows:
(1) the vehicle has the limitation of passenger carrying quantity, can carry three persons at most, allows secondary departure, and does not consider special factors such as regional restriction and the like;
(2) endowing a skill attribute to a worker, wherein the skill attribute comprises driving skill and installation skill, and the installation skill is different from unskilled workers in proficiency, namely skill level difference;
(3) not allowing for batch delivery, each customer can only be served by one service unit and only once;
(4) a hard time window strategy is adopted, namely, the service unit must provide service in the service time window of the client, and the service is not allowed to be provided when overtime occurs;
(5) defining the time from a distribution center after the service units are loaded as initial working time, and defining that the initial working time of all the service units is the same and has working time limit;
(6) defining the time from the arrival of the customer point to the completion of the installation as the service time of the customer, wherein the service time comprises the waiting time, the unloading time, the carrying time and the pure installation time, and neglecting the unloading time. The carrying time is mainly related to environmental factors such as whether cars are allowed to enter a community, whether elevators exist upstairs or not and the like, the weight, the volume and the quantity of goods, and the carrying time is simplified to be only related to the quantity; the pure installation duration is then related to the goods category, quantity and service capacity of the installation team.
Example one
As shown in fig. 1, the method for ordering and optimizing the logistics and path of the home with time window and integrated assembly provided by the embodiment of the invention comprises the following steps:
step S1: establishing an objective function and constraint conditions of a home logistics distribution model based on assembly integration: customer constraints, worker constraints, vehicle constraints, and skill and time window constraints;
step S2: solving the model by adopting an improved ant colony algorithm, and planning M optimal paths as alternative paths; substituting customer constraint, worker constraint, vehicle constraint, skill and time window constraint into alternative path planning to perform logistics ordering, taking path planning meeting all constraint conditions as path planning, and obtaining corresponding logistics ordering; and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
In one embodiment, the step S1: establishing an objective function and constraint conditions of a home logistics distribution model based on assembly integration: customer constraints, worker constraints, vehicle constraints and skill and time window constraints, specifically including:
step S11: an objective function for path optimization based on the home logistics distribution model integrated with assembly is established as shown in formula (1):
where C is the set of all customer points i, and C ═ 1,2, … n };
n is a set of all the customer point sets C, the distribution center O, and the virtual end point O', N ═ {0,1,2 …, N +1}, i ═ 0 represents the distribution center, and i ═ N +1 represents the virtual end point;
d ij the distance from the client point i to the client point j; when i is 0, the distance from the delivery center to the customer point j is indicated, and when j is n +1, the distance from the customer point j to the virtual end point is indicated;
k is a set K of vehicles K {1,2, … K };
x ijk indicating that vehicle k is traveling from customer point i to customer point j, satisfies
a is the cost per kilometer of oil consumption; b 1 The fixed cost of each vehicle per day, including the depreciation cost and the insurance cost;
step S12: the following constraints are established:
(1) customer constraints
a) For each customer point, only one vehicle can enter the point, as shown in equation (2):
b) for each customer point, only one vehicle can leave the point, as shown in equation (3):
(2) worker restraint
In the embodiment of the invention, each worker is numbered, the order is arranged according to the serial number sequence of the workers, and the worker p of each vehicle 1 p 2 p 3 Is fixed;
a) one worker cannot repeatedly appear on the same vehicle as shown in equation (4):
wherein, the first and the second end of the pipe are connected with each other,
p is a set of workers P, {0, P ═ P 1 ,p 2 … P, where 0 represents a dummy without any skill; p is a radical of 2 Denotes the position number of worker 2 in set P, and P 2 At position p 1 Then, at p 3 Before;
b) one worker cannot be present on multiple cars as shown in equation (5):
c) the worker has a fixed collocation as shown in equation (6):
(3) vehicle restraint
a) The vehicle does not start from the place after returning to the virtual address after completing the last customer's fitting task, as shown in equation (7):
b) path balancing constraints that ensure that a vehicle must leave a customer site after entering the customer site, as in equation (8)
Shown in the figure:
wherein the content of the first and second substances,
indicating the return of the vehicle to the distribution center;
c) the headroom limit for each vehicle, as shown in equation (9):
wherein V is the headroom of each vehicle k; v. of j Order volume for customer j;
d) there cannot be multiple people in a vehicle, as shown in equation (10):
(4) skill and time window constraints
a) Time balance constraints, driving skill and installation skill constraints, as shown in equation (11):
wherein M is a set of household articles M; t is t i A transport duration required for the customer i; t is the basic installation time corresponding to each type of the M;
for workers combining p 1 p 2 p 3 The installation time for installing the mth category is long, if the category cannot be installed in the combination, a large integer E is set, so that the constraint condition of the formula (11) cannot be met; q. q.s im The number of the mth variety to be installed for the client i; at (a) ik Is the time at which vehicle k arrives at customer point i; u is the average travel speed of each vehicle;
b) latency constraints, as shown in equation (12):
wherein, wt ik Is the waiting time of vehicle k at customer point i; e.g. of the type i Is the earliest delivery time reserved by the customer;
c) the client point time window constraint, as shown in equation (13):
wherein l i Is the latest delivery time reserved by the customer; [ e ] a i ，l i ]A time window for customer point i;
d) the arrival time and the number of the installation categories of the distribution center are specified, and the distribution center is expressed by the formula (14):
the method comprises the steps of firstly, solving a formula (1) by adopting an ant colony path ordering algorithm, ordering paths according to expectations for vehicle capacity, and ordering according to cost to obtain a plurality of paths with the minimum cost as alternative paths; the worker is then populated with the alternate paths according to the constraints such that all constraints are met and made available on the alternate paths.
In one embodiment, the step S2: solving the model by adopting an improved ant colony algorithm, and planning M optimal paths as alternative paths; substituting customer constraint, worker constraint, vehicle constraint, skill and time window constraint into alternative path planning to perform logistics ordering, taking path planning meeting all constraint conditions as path planning, and obtaining corresponding logistics ordering; if none of the M alternative path plans can satisfy the constraint condition, increasing workers or increasing working time until a feasible path plan and a feasible logistics list are obtained, specifically comprising:
step S21: solving a formula (1) by using an ant colony algorithm, presetting m ants, traversing all client point sets C from a distribution center O to obtain a path plan and corresponding pheromones thereof; obtaining the optimal path with the minimum total cost in all ants as the current iteration, and performing iteration and increment on the pheromone and then performing the next iteration; outputting the previous M optimal path plans as alternative path plans until reaching the preset iteration times;
the ant colony algorithm parameters in the embodiment of the invention are as follows:
m: the number of ants for optimizing;
n: the number of iterations;
TC: the total cost of one ant;
BTC: optimal total cost in one iteration;
stagnation: the number of stalls, the number of iterations that currently continue to find solutions beyond the optimal solution so far.
Ti ij : the quantity of the pheromones on the paths is a fixed value when the pheromones on each path are initialized;
C ij (ii) a A heuristic function, otherwise known as visibility, has the meaning that the expectation degree of an ant moving from a client node i to a client node j is generally related to the distance between two client nodes, and for the problem, the oil cost, the transfer angle and the waiting time consumed from i to j are taken;
P ij : the ant transfers from the node i to the transfer rule of the node j, which is the most core step of the ant colony algorithm, and traverses the nodes in the network one by one through the transfer rule to finally solve the optimal solution of the problem;
D ij : representing from customer point i to customerThe distance of the user point j is usually a distance matrix which can be obtained or is known according to the coordinate point of each client point;
T ij : represents the time from customer point i to customer point j;
A ij : representing the angle formed by the route from the current customer point i to the next customer point j and the route from the last customer point to i;
Q i : represents the latency penalty cost of arriving at the inode at this moment;
J i : representing the penalty cost of the transfer angle of the arrival at the i node at the moment;
α: pheromone elicitors, which mean the sensitivity of ants to pheromone quantity on a path or the influence degree of ants on the information quantity on the path when the ants select a next node;
beta: the expected heuristic factor represents the judgment capability of the ants on the distance of the next node or the perception capability of the ants on the surrounding environment in the optimizing process, wherein the judgment capability is related to the moving cost;
γ: a time window punishment cost heuristic factor which represents the sensitivity of the ants to the time window punishment cost when selecting the next node;
δ: a transfer angle punishment cost heuristic factor which represents the sensitivity of the ants to the transfer angle punishment cost when selecting the next node;
ρ: a volatile coefficient representing a pheromone; the residual coefficient of the pheromone is represented by RHO ═ 1- ρ; where ρ ∈ (0, 1);
TB: a taboo list, the removed point is put into the taboo list, and the ant does not go to the point any more;
AL: a permission table, complementary to TB;
c＝D ij ×V (15)
Ti ij (t+1)＝(1-ρ)Ti ij (t)+ΔTi ij (t,t+1) (20)
equation (15) is the calculation of the next move cost by distance and variable cost.
Equation (16) is a normalization of the movement cost, and the selection coefficient of the optimal movement cost is 1, and the lowest value is close to 0 as the selection coefficient is lower than the optimal movement cost.
Equation (17) is a normalization of penalty costs, and the selection coefficient of the minimum penalty cost is 1, and the lowest penalty cost approaches 0 as the selection coefficient is lower than the minimum penalty cost.
Equation (18) is a calculation of penalty cost for rotation angle, too large rotation angle (over 120 degrees) may cause round-trip waste of path in the early calculation period, and a better initial solution can be found earlier by limiting the rotation angle.
Equation (19) is the probability of selecting the next point j at point i, and is related to pheromones, movement cost, latency penalty cost, rotation angle penalty cost, and their coefficients.
Equations (20) and (21) are the iterative rules for pheromones, including pheromone residuals and pheromone increments on the best cost path.
For traversal of all service points of one ant, the ant starts from a distribution center, selects the next previous customer point randomly according to a probability judgment function, puts the next customer point into a TB table after reaching the next customer point, and only considers the permission table AL where the points which are not passed are positioned in the next selection. Until all points in the AL table can not be reached because one of the time window constraint, the time constraint, the mileage constraint or the volume constraint is violated, at which time the current vehicle K returns to the distribution center, after which ants start from the distribution center and empty the volume, mileage and time and continue crawling until all the customer points have been reached, there are no points in the AL table that can be reached before, and the next ant starts to run.
For a route where an ant crawls, given a total of 10 customer points, the path for the ant may be recorded as a series, such as (0-6-4-2-9-11-0-1-3-5-12-0-7-8-10-13-0), for a total of 3 vehicles.
And then sending out the next ant until all ants complete crawling, selecting the best cost with the minimum total cost in all ants as the iteration, iterating and increasing the pheromone, then entering the next iteration, and outputting the previous M best cost paths as alternative path plans after all iterations are completed.
Step S22: performing worker filling on the alternative path plan to enable the alternative path plan to meet customer constraints, worker constraints, vehicle constraints and skill and time window constraints;
firstly, according to field investigation and data analysis, a combination with 2 installers on one vehicle is the most common combination with the highest efficiency in the actual situation, so that the center of gravity of a main body is placed on a two-person combination by the algorithm, and the three-person combination is dispatched only under the condition that the single-point order quantity is too large and the task can be completed only by the three-person combination; a one-person combination is sent with only one person left and the order not completed.
For each car, there is either a private car of the owner or a public car of the owner. If the owner of the vehicle belongs to the fixed combination, the skill of the vehicle is the skill of the fixed combination; if the owner of the vehicle belongs to the non-fixed combination, the technical capability of the vehicle is the combination of the average capabilities of the main vehicle and the non-fixed combination personnel without the vehicle; if the bus is a bus without owner, the capacity of the bus is the average of the capacities of the non-fixed combination personnel.
And thirdly, for the vehicle distribution sequence, a vehicle sequence table is formulated according to the condition of the current day, and the vehicle distribution sequence is carried out according to the vehicle sequence table. When three-person combined vehicle dispatching is carried out on a special point, the vehicle with the highest priority in the vehicles meeting the skill requirements of the special point is dispatched.
And fourthly, after the number of the vehicle and the vehicle personnel combination is determined, generating a path according to an ant colony algorithm, continuously iterating and optimizing, and finally outputting the currently obtained optimal solution. Meanwhile, aiming at the defects of the ant colony algorithm, a maximum minimum pheromone method, parameter dynamic adjustment and the like are used for preventing the ant colony from falling into local optimum, and the ant transfer probability function is further designed, and time window punishment cost factors and transfer angle factors are considered.
For the alternative path plans generated in step S21, the person filling is performed sequentially in the order of the sub paths. Different personnel combinations have different fitness for each sub-path, the iterative cycle is carried out in turn according to the personnel combination fitness table of each sub-path, if the solution cannot be found, the previous sub-path is returned, otherwise, the iterative cycle is continued until a feasible personnel filling solution is found.
Because the two-person combination can meet more than 90% of skill requirements, and the robustness of personnel filling is very strong, after the personnel filling of the algorithm is independent and the path ordering is performed, the feasible solution of the personnel filling is successfully found in the results of 20 experimental operations through subsequent verification on the algorithm, and the feasibility of the separate operation of the path ordering and the personnel filling is proved.
Step S23: and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
If none of the optimal M solutions obtained in the ant colony algorithm can obtain a feasible solution for population, an overtime solution for the personnel or a solution for calling additional staff is generated to make a feasible solution for the path ranking solution.
Based on the above model, the following example was used for verification.
1) Product database
1. Product classification and basic installation duration
The product database contains data such as the length of the base installation of the product, the length of the non-installation associated with the product, the volume, the sub-kit relationship, etc., as shown in table 1.
The product categories are divided into three types of installation types: simple (distribution only, representative of products such as household appliances), ordinary (simple installation, representative of products such as self-contained furniture), custom (complex installation). Taking the cabinet class as an example, all products can be divided into simple cabinets, ordinary cabinets and customized cabinets.
Table 1 database of installation time
| Product numbering | Product classification | Length of foundation erection (min) | Volume of | Number of |
| 0 | 0 | 0.000 | 0 | 1 |
| 1 | 7 | 5.000 | 0.27 | 1 |
| 2 | 7 | 5.000 | 0.27 | 1 |
| 3 | 11 | 15.000 | 0.192 | 1 |
| 4 | 1 | 5.000 | 0.85 | 1 |
| 5 | 3 | 15.000 | 0.85 | 3 |
| 6 | 3 | 15.000 | 1.08 | 3 |
| 7 | 3 | 15.000 | 1.08 | 3 |
| 8 | 6 | 5.000 | 1.08 | 1 |
| 9 | 4 | 30.000 | 0.04 | 4 |
| 10 | 4 | 30.000 | 0.9804 | 4 |
| 11 | 4 | 30.000 | 0.21 | 4 |
| 12 | 4 | 30.000 | 0.18 | 4 |
2. Length of non-installation
The non-installation time is defined as the product loading time, which means the time taken for the vehicle to arrive at the customer site and begin to stop installation, and is mainly influenced by the service environment and the order attributes, including whether the vehicle can enter a cell, whether the cell has an elevator, the number of orders, the weight and volume of the orders and other factors. The non-installation time is defined as a piecewise integer function, when the number of the order pieces is within 3, the non-installation time is 15min, when the number of the order pieces is in a [4,7] section, the non-installation time is 30min, and when the number of the order pieces exceeds 7, the non-installation time is 50 min.
3. Product volume
The product volume and the vehicle clearance determine the amount of cargo that can be loaded by the vehicle.
4. Sub-piece and external member
Due to the particularities of furniture items, there are situations where there are multiple packages for a product. The volume information of the components can be measured independently, the installation time length information is determined according to the total number of the components of the kit, and all the components of the kit share the installation time of the kit. For example, a three-person sofa consisting of three pieces would have a total installation time of 15 minutes, and then each sofa piece would have an installation time of 5 minutes.
2) Vehicle and worker database
1. Vehicle data
The vehicle data comprise the clearance of the vehicle, the daily depreciation cost, the kilometer fuel consumption cost and the average driving speed, and the vehicle calculation data of the experiment are shown in the table 2.
TABLE 2 vehicle Attribute initialization values
2. Worker data
The worker data includes binding workers, driving skills, installation skills, and various rate synergies, and the time spent by each (or group of) worker installing a certain type of product can be obtained through the rate synergies and the basic installation duration. Rate synergies include individual primary rate synergies, combined rate synergies, and individual secondary rate synergies. The personal main speed efficiency enhancement means the efficiency enhancement of a worker who installs a certain class relative to a person whose reference efficiency is 1, wherein the efficiency enhancement is higher than the reference efficiency, and conversely, the efficiency enhancement is lower than the reference efficiency, and the reference efficiency of the default worker is not different in product class, namely, the personal main speed efficiency enhancement of the worker who installs the class A product is the same as that of the worker who installs the class B product. The combined speed increase is defined as the increase of the efficiency of a person by a plurality of persons relative to a person, and the auxiliary speed increase refers to the increase of the overall efficiency by providing simple assistance for workers who cannot install, and can be regarded as the substitute of the main speed increase of the person when a certain skill is not available. In order to prevent the promotion of the combination efficiency by the people who can not install from being higher than the promotion of the combination efficiency by the people who can install, the calculation formula of the personal auxiliary efficiency is set in the experiment as follows:
two person installation situation, C v ＝0.65*tan -1 (3x)
Three person installation situation, C v ＝0.5*tan -1 (3x)
Wherein x is the average of the personal main rate synergies of the workers capable of being installed, the function ensures that the personal auxiliary efficiency is always lower than the lowest personal main rate synergy in the combination, and the time length of the final combined installation can be obtained by the following formula.
The worker information collection table is shown in table 3:
table 3 worker information table
The combination synergies are shown in table 4:
TABLE 4 Combined synergy table
| Of the combined type | Simple | General | Customization |
| |
1 | / | / |
| |
1 | 1.3 | 1.2 |
| Three |
1 | 1.2 | 1.1 |
3. Relationship between man and vehicle
During the actual transportation and assembly, matching relations between vehicles and workers and between workers exist, namely, a certain vehicle can only be driven by a specific person, and some persons also have combined vehicle attendance situations, which restrict the available workers (or the combination of workers) and the vehicle attendance situations.
3) Customer point database
The customer site data mainly includes the geographic location, time window and product requirements of the customer site. The customer point calculation for this experiment is shown in table 5.
TABLE 5 customer requirements Table
4) Distance matrix
The distance matrix refers to the distance between the distribution center, the customer site and the return address, and the customer site data mainly comprises the geographic position, the time window and the product demand of the customer site. The latitude and longitude information of the client point, the distribution center and the vehicle return address can be obtained through the system, and in order to ensure the accuracy of the distance, the embodiment of the invention returns the actual driving distance between any two points by using the latitude and longitude information through a Gade map API.
5) Other fixed parameters
1. Distribution center
The distribution center address is uniquely determined and is the warehouse address.
2. Rest time
The worker needs a certain rest time in the process of transporting and assembling in one day, the worker is specified to take a rest for one hour when the end time of a certain task is after 12:30 for the first time, and the subsequent work is started after the rest.
The embodiment of the invention adopts matlab programming practice on a PC, adopts an ant colony algorithm to solve the model based on the data to obtain an optimal solution, the specific result is shown in Table 6, the obtained optimal distribution path scheme is shown in figure 2, the horizontal and vertical coordinates in figure 2 respectively represent longitude and latitude, a five-pointed star represents a distribution center 0, the distribution paths of 4 vehicles are shown, and the total cost is 520.62.
TABLE 6 optimal intelligent dispatching scheme
| |
1 | ||||||
| The 1 st vehicle visits the customer points as follows: | 0 | 2 | |||||
| the 1 st vehicle occupant is: | 1 | 2 | |||||
| the client points for the 2 nd vehicle visit are: | 0 | 8 | 12 | 13 | 7 | ||
| the 2 nd vehicle occupant is: | 3 | 8 | |||||
| the client points for the 3 rd vehicle visit are: | 0 | 9 | 11 | 6 | |||
| the 3 rd vehicle personnel are: | 5 | 7 | |||||
| the client points visited by the 4 th vehicle are: | 0 | 5 | 4 | 3 | 14 | 15 | 10 |
| the 4 th vehicle occupant is: | 4 | 6 |
according to the embodiment of the invention, based on the data samples of 40 client points in the table 5, through comparison, the algorithm ranking of the embodiment of the invention has the following advantages compared with manual ranking:
1. resource occupation
And comparing the number of workers and the number of vehicles for manual ordering and algorithm ordering. The manual ranking requires 5 vehicles and 8 workers, and the algorithm ranking requires 4 vehicles and 7 workers, and the algorithm ranking uses fewer resources to complete the service tasks of all customers regardless of the number of vehicles or workers. The resource occupation comparison graph is shown in fig. 3.
2. Length of time of worker
For the worker's work duration, the 8-person work duration for manual ordering and the 7-person work duration for algorithmic ordering are shown in FIG. 4. The average work time of workers who are manually arranged is 7.73 hours, the average work time of workers who are arranged in an algorithm is 9.32 hours, and the average work time of workers who are arranged in an algorithm is longer, because the worker arrangement is strictly filled according to the off-duty time of the workers, the work completion time of the workers is close to 7 pm, but due to the scale limitation, the last customer point is not enough, and the work can be completed only by one person working for 6 hours.
From the stability point of view, as shown in fig. 5, the algorithm is more stable in order, the standard deviation is 1.19 hours, and if only the conditions of the first 6 workers are considered, the labeling difference is only 0.11 hour; the manual ordering had large fluctuations with a standard deviation of 1.37 hours.
3. Cost and path length
According to the cost calculation rule, the daily depreciation cost of each vehicle is 100 yuan, the oil consumption of each vehicle per kilometer is 1.2 yuan, and a comparison graph of the total cost of the manual ranking list and the algorithm ranking list is obtained through calculation and shown in fig. 6:
as shown in fig. 7, the total travel path of 5 vehicles arranged manually is 315.3 km, and the cost of the branch is 878.4 yuan; the algorithm ranks 4 vehicles in a total travel distance of 254 kilometers, and the expenditure cost is 704.8 yuan. Compared with the real manual scheduling, the algorithm scheduling saves the route length by 19.4% and saves the cost by 19.7% in the aspect of cost. Overall, the algorithmic ordering is significantly improved over the manual ordering of the dispatcher himself.
The invention discloses a method for arranging and optimizing a path of assembled and integrated home logistics, which aims at the problems of intelligent arranging and path planning of assembled and integrated home logistics distribution constructed by a furniture comprehensive service platform, adopts an ant colony algorithm to plan a lowest-cost path, realizes optimal intelligent arranging by establishing constraint conditions of customer constraint, worker constraint, vehicle constraint and skill and time window constraint, and can meet the customer service time window requirement and the worker noon break requirement at the same time.
Example two
The embodiment of the invention provides a system for arranging lists and optimizing paths of assembled integrated home logistics with a time window, which comprises the following modules:
the model modeling module 31 is configured to establish an objective function and constraint conditions of a home logistics distribution model based on an integrated assembly: customer constraints, worker constraints, vehicle constraints, and skill and time window constraints;
a path planning and logistic order-ranking module 32, configured to solve the model by using an improved ant colony algorithm, and plan M optimal paths as alternative paths; substituting the client constraint, the worker constraint, the vehicle constraint, the skill and the time window constraint into the alternative path planning to carry out logistics ordering, taking the path planning meeting all the constraint conditions as the path planning, and obtaining a corresponding logistics ordering; and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
The above examples are provided only for the purpose of describing the present invention, and are not intended to limit the scope of the present invention. The scope of the invention is defined by the appended claims. Various equivalent substitutions and modifications can be made without departing from the spirit and principles of the invention, and are intended to be within the scope of the invention.
Claims (4)
1. A method for arranging lists and optimizing paths of assembled home logistics with time windows is characterized by comprising the following steps:
step S1: establishing an objective function and constraint conditions of a home logistics distribution model based on assembly integration: customer constraints, worker constraints, vehicle constraints, and skill and time window constraints;
step S2: solving the model by adopting an improved ant colony algorithm, and planning M optimal paths as alternative paths; substituting the client constraint, the worker constraint, the vehicle constraint, the skill and the time window constraint into the alternative path planning to carry out logistics ordering, taking the path planning meeting all the constraint conditions as the path planning, and obtaining a corresponding logistics ordering; and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
2. The fitted integrated home logistics list and path optimization method with time window as claimed in claim 1, wherein the step S1: establishing an objective function and constraint conditions of a home logistics distribution model based on assembly integration: customer constraints, worker constraints, vehicle constraints and skill and time window constraints specifically include:
step S11: the objective function for establishing the path optimization based on the home logistics distribution model integrated with the assembly is as shown in formula (1):
where C is the set of all customer points i, and C ═ 1,2, … n };
n is a set of all the customer point sets C, the distribution center O, and the virtual end point O', where N ═ 0,1,2 …, N +1}, 0 represents the distribution center, and N +1 represents the virtual end point;
d ij the distance from the client point i to the client point j;
k is a set K of vehicles K {1,2, … K };
x ijk indicating that vehicle k is traveling from customer point i to customer point j, satisfies
a is the cost per kilometer of oil consumption; b 1 A fixed cost per vehicle per day;
step S12: the following constraints are established:
(1) customer constraints
a) For each customer point, only one vehicle can enter the point, as shown in equation (2):
b) for each customer point, only one vehicle can leave the point, as shown in equation (3):
(2) worker restraint
a) A worker cannot repeatedly appear on the same vehicle as shown in equation (4):
wherein, the first and the second end of the pipe are connected with each other,
p is a set of workers P, {0, P ═ P 1 ,p 2 … P, where 0 represents a dummy without any skill; p is a radical of 2 Denotes the position number of worker 2 in set P, and P 2 At position p 1 Then, at p 3 Before;
b) one worker cannot be present on multiple cars as shown in equation (5):
c) workers have fixed matches as shown in equation (6):
(3) vehicle restraint
a) The vehicle does not start from the place after returning to the virtual address after completing the last customer's fitting task, as shown in equation (7):
b) a path balancing constraint that ensures that a vehicle must leave a customer site after entering the customer site, as shown in equation (8):
wherein the content of the first and second substances,
indicating that a vehicle is returning to the distribution center;
c) the headroom limit of each vehicle, as shown in equation (9):
wherein V is the headroom of each vehicle k; v. of j Order volume for customer j;
d) there cannot be multiple people in a vehicle, as shown in equation (10):
(4) skill and time window constraints
a) Time balance constraints, driving skill and installation skill constraints, as shown in equation (11):
wherein M is a set of household articles M; t is t i A transport duration required for the customer i; t is the basic installation time corresponding to each type of the M;
for workers combining p 1 p 2 p 3 The installation time for installing the mth product is long, and if the product is not installed in the combination, a large integer E is set; q. q.s im The number of the mth variety to be installed for the client i; at is a ik Is the time at which vehicle k arrives at customer point i; u is the average travel speed of each vehicle;
b) latency constraints, as shown in equation (12):
wherein, wt ik Is the waiting time of vehicle k at customer point i; e.g. of a cylinder i Is the earliest delivery time reserved by the customer;
c) the client point time window constraint, as shown in equation (13):
wherein l i Is the latest delivery time reserved by the customer; [ e ] i ，l i ]Time of point i for clientA window;
d) the arrival time and the number of the installation categories of the distribution center are specified, and the distribution center is expressed by the formula (14):
3. the fitted integrated home logistics list and path optimization method with the time window according to claim 2, wherein the step S2: solving the model by adopting an improved ant colony algorithm, and planning M optimal paths as alternative paths; substituting the client constraint, the worker constraint, the vehicle constraint, the skill and the time window constraint into the alternative path planning to carry out logistics ordering, taking the path planning meeting all the constraint conditions as the path planning, and obtaining a corresponding logistics ordering; if none of the M alternative path plans satisfies the constraint condition, increasing workers or increasing working time until a feasible path plan and a feasible logistics list are obtained, specifically comprising:
step S21: solving a formula (1) by using an ant colony algorithm, presetting m ants, traversing all the customer point sets C from the distribution center O to obtain a path plan and corresponding pheromones thereof; obtaining the optimal path with the minimum total cost in all ants as the current iteration, and performing iteration and increment on the pheromone for the next iteration; outputting the previous M optimal path plans as alternative path plans until reaching the preset iteration times;
step S22: performing worker population on the alternative path plan to meet the customer constraints, worker constraints, vehicle constraints, and skill and time window constraints;
step S23: and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
4. The utility model provides a take integrative house commodity circulation of dress list of time window and route optimization system which characterized in that includes following module:
the model modeling module is used for establishing an objective function and a constraint condition of the home logistics distribution model based on the assembly integration: customer constraints, worker constraints, vehicle constraints, and skill and time window constraints;
the path planning and logistics scheduling module is used for solving the model by adopting an improved ant colony algorithm and planning M optimal paths as alternative paths; substituting the client constraint, the worker constraint, the vehicle constraint, the skill and the time window constraint into the alternative path planning to carry out logistics ordering, taking the path planning meeting all the constraint conditions as the path planning, and obtaining a corresponding logistics ordering; and if the M alternative path plans can not meet the constraint condition, increasing workers or increasing the working time until a feasible path plan and a logistics list are obtained.
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