CN115618994A - Logistics park vehicle loading method based on simulated annealing algorithm - Google Patents
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Abstract
The invention discloses a logistics park vehicle loading method based on a simulated annealing algorithm, which comprises the following steps: the method comprises the following steps: acquiring information of a cargo owner order, wherein the information comprises cargo weight information, cargo distribution time information and distribution place information; the information of the trucks to be dispatched in the region comprises truck load information and vehicle distribution fee; step two: ordering the goods owner orders according to the goods distribution time information, and setting an order distribution priority; step three: and arranging and combining the information of the goods owner order and the information of the goods vehicle, obtaining an optimal subset through a simulated annealing algorithm, and carrying out goods delivery according to the optimal subset. The invention optimally combines the goods delivery based on the simulated annealing algorithm, reduces the delivery cost while delivering the goods without dividing the goods, and simultaneously intelligently delivers the goods, shortens the goods delivery time and improves the delivery efficiency.
Description
Technical Field
The invention relates to the technical field of cargo handling, in particular to a logistics park vehicle loading method based on a simulated annealing algorithm, which is suitable for a system for a logistics park as an intermediary to connect a car owner and a cargo owner.
Background
Logistics is part of supply chain activities, is a process of planning, implementing and controlling efficient and low-cost flow and storage of commodities, service consumption and related information from a production place to a consumption place in order to meet the needs of customers, and comprises links such as transportation, distribution, warehousing, packaging, handling, distribution and processing of the commodities, related logistics information and the like.
The rapid development of the e-commerce industry makes the loading, unloading and distribution modes of loading and unloading goods more and more frequently, the phenomena that the logistics park of a three-four-wire city which is not very popular for some intelligent technologies is not large in scale generally, and human resources are insufficient are avoided, and the operation of loading goods by experience is still used, so that the space utilization rate of vehicles is low, the transportation efficiency is low, and when a vehicle owner needs to load goods ordered by the cargo owner in the logistics park, the high requirement on how to quickly and reasonably select goods of different cargo owners which are transported to the same place to load the vehicles is provided.
Disclosure of Invention
The invention aims to provide a logistics park vehicle loading method based on a simulated annealing algorithm, which has the advantages of helping a vehicle owner to realize the maximum utilization rate of loading space, providing loading guidance, facilitating the completion of loading work of cargos, shortening the cargo allocation time and improving the distribution efficiency.
In order to achieve the purpose, the invention provides the following technical scheme: a logistics park vehicle loading method based on a simulated annealing algorithm comprises the following steps:
the method comprises the following steps: acquiring owner order information including cargo weight information, cargo distribution time information and distribution place information; the method comprises the steps that information of trucks to be dispatched in an area comprises truck load information and vehicle distribution fees;
step two: ordering the goods owner orders according to the goods distribution time information, and setting an order distribution priority;
step three: arranging and combining the information of the goods owner order and the information of the goods vehicle, obtaining an optimal subset through a simulated annealing algorithm, and carrying out goods delivery according to the optimal subset;
when goods are delivered, variable x =1 is defined; otherwise x =0, determining a loading combination variable x i (i＝1、2、……、n)，h i For delivery time priority, loading combined variable x i The following equation is satisfied:
solving the optimal configuration by using a simulated annealing algorithm, randomly generating an initial solution omega, calculating an objective function f (omega), disturbing the objective function f (omega), and generating a new solution omega i And calculating an objective function f (ω) i )：
Δf＝f(ω i )-f(ω)
When Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) (ii) a When Δ f>When 0, receiving a new solution according to Metropolis criterion, and judging whether the new solution reaches iteration for a plurality of times; if the iteration is not satisfied for a plurality of times, the iteration is disturbed again, if the iteration is satisfied for a plurality of times, whether the iteration satisfies the termination condition or not is judged, if the iteration does not satisfy the termination condition, the iteration is disturbed again, and if the iteration satisfies the termination condition, the optimal solution is returned.
Specifically, the optimal subset z needs to satisfy that z is less than or equal to a, and the weight of the goods is not divided; when z > a, searching again for the truck meeting the condition, and satisfying the formula:
wherein, a is the load of the truck, a z Representing the current truck load satisfying the optimal subset z, and d is the vehicle delivery fee.
Compared with the prior art, the invention has the beneficial effects that:
the logistics park vehicle loading method based on the simulated annealing algorithm is matched with the simulated annealing algorithm, the optimized combination is carried out on the cargo delivery, the delivery cost is reduced when the cargo is not divided for delivery, the cargo delivery is carried out more intelligently, the problem that how to quickly and reasonably select the cargos of different owners who deliver the same place for loading is solved, the higher requirement is provided, the vehicle owner can be helped to maximize the utilization rate of loading space, the loading guidance is provided simultaneously, the loading work of the cargos is conveniently completed by the logistics park vehicle loading method, the cargo delivery time is shortened, and the delivery efficiency is improved.
Drawings
FIG. 1 is a flow chart of a logistics park vehicle loading method based on a simulated annealing algorithm according to an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in FIG. 1, the invention provides a logistics park vehicle loading method based on a simulated annealing algorithm, which comprises the following steps:
the method comprises the following steps: arranging goods owner order information in a logistics park, and extracting goods weight information, goods distribution time information and distribution place information in the order; extracting freight car load information and vehicle distribution fee from freight car information to be dispatched in the region;
step two: ordering the orders according to the goods distribution time, wherein the closer the time is, the higher the urgency degree is, and the higher the priority is;
step three: and arranging and combining the information of the goods owner order and the information of the goods vehicle, obtaining an optimal subset through a simulated annealing algorithm, and carrying out goods delivery according to the optimal subset.
In the first step of the present invention, preferably, the cargo weight information, the cargo delivery time information, the delivery location information, the truck load information, and the vehicle delivery fee are represented as w, q, c, a, and d, respectively.
N goods owners in the manufacturers butted in the logistics park all send goods to a Q place, namely a goods owner 1, a goods owner 2, a goods owner … and a goods owner n, marked as a goods owner i, wherein the weight of the goods is w 1 ，w 2 ，…，w n Is denoted by w i (i＝1,2，…，n)。
The time distance that the goods of the owner i must be delivered is t when the worker in the logistics park runs by train i (i =1,2, …, n), the daily business hours of the logistics park is T, and the following formula is calculated:
wherein h is i The larger and more urgent, representing higher priority of the goods, the earlier the logistics park arranges for distribution.
Preferably, the simulated annealing algorithm in step three includes: when there are several goods owners to send goods to some place, form goods source set s and find subset s i So that:
wherein h is i >0。
Record the weight of the cargo as w 1 ，w 2 ，…，w n Forming a set m of weights of the goods, finding a subset m i So that:
wherein W is the total weight of the cargo.
When the cargo is delivered, variable x =1 is defined; otherwise x =0, determining a loading combination variable x i (i =1,2, … …, n) to make the goods urgent degree of the distribution as large as possible, and the variable x of the loading combination i The following equation is satisfied:
further, the optimal configuration is obtained by using a simulated annealing algorithm, the urgency degree of the goods distributed at this time is made to be as large as possible, an initial solution omega is randomly generated, an objective function f (omega) is calculated and disturbed, and a new solution omega is generated i And calculating an objective function f (ω) i ) By the formula:
Δf＝f(ω i )-f(ω)
when Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) When Δ f is>And 0, receiving a new solution according to Metropolis criterion, judging whether the new solution reaches iteration times, if the new solution does not meet the iteration times, disturbing the new solution again, if the iteration times are met, judging whether the iteration conditions meet the termination conditions, if the iteration conditions do not meet the termination conditions, disturbing the new solution again, and if the iteration conditions meet the termination conditions, returning to the optimal solution.
Preferably, the optimal subset z needs to satisfy z ≦ a, the cargo weight is not divided, and when z > a, the truck satisfying the condition is searched again and satisfies the formula:
wherein, a z And the current truck load meeting the optimal subset z is represented, so that the cargo delivery cost is as low as possible.
Example one
When a certain logistics park operates continuously for 24 hours, and 8 morning, when a goods distributor in the logistics park handles orders, the system receives freight lists of 12 goods owners, and the goods of the goods are all waiting for goods distribution in the logistics park, meanwhile, 3 trucks to be distributed in the logistics park have loads of 6 tons, 12 tons and 16 tons respectively, and when the goods distribution work is carried out, the trucks with larger loads can be preferentially arranged to reduce the distribution times, and the information can show that: n =12; t =24 × 60min =1440min;
data collected and calculated are shown in table 1:
by passing
Calculate the degree of urgency by max ∑ i∈s h i ，
And
calculating related parameters, judging the optimal subset z, randomly generating an initial solution omega, calculating an objective function f (omega), disturbing the objective function f (omega), and generating a new solution omega i And calculating an objective function f (ω) i ) By the formula:
Δf＝f(ω i )-f(ω)
when Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) (ii) a When Δ f>And 0, receiving a new solution according to Metropolis criterion, judging whether the new solution reaches iteration times, if the new solution does not meet the iteration times, disturbing the new solution again, if the iteration times are met, judging whether the iteration conditions meet the termination conditions, if the iteration conditions do not meet the termination conditions, disturbing the new solution again, and if the iteration conditions meet the termination conditions, returning to the optimal solution.
From the above optimal subset, the optimal distribution combination is then composed of the following items: and = (= { cargo 1, cargo 2, cargo 3, cargo 5, cargo 6, cargo 8, cargo 9, cargo 12}, this time 8.
The undelivered goods at this time are: cargo 4, cargo 7, cargo 10, cargo 11, as shown in Table 2
| Goods source | Cargo weight (ton) | Delivery time | Delivery duration (min) | Degree of urgency |
| Source of goods 4 | 4.5 | 11：30 | 210 | 6.86 |
| Goods source 7 | 3.3 | 12：00 | 240 | 6.00 |
| Source of goods 10 | 5.6 | 13：00 | 300 | 4.80 |
| Goods source 11 | 3 | 10：30 | 150 | 9.60 |
Existing trucks weigh 6 tons and 12 tons, passing
Calculate the degree of urgency by max ∑ i∈s h i ，
And
calculating related parameters, judging the optimal subset z, randomly generating an initial solution omega, calculating an objective function f (omega), disturbing the objective function f (omega), and generating a new solution omega i And calculating an objective function f (ω) i ) By the formula:
Δf＝f(ω i )-f(ω)
when Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) When Δ f is>And 0, receiving a new solution according to Metropolis criterion, judging whether the new solution reaches iteration times, if the new solution does not meet the iteration times, re-disturbing the new solution, if the iteration times are met, judging whether the new solution meets a termination condition, if the iteration times are not met, re-disturbing the new solution, and if the iteration times are not met, returning to an optimal solution, wherein the optimal cargo allocation combination comprises the following cargos: the transport tonnage is 10.8 tons, and by the above calculation, the utilization efficiency of the truck can be maximized and the delivery time can be saved on the premise of fully considering the urgency degree of the cargo source, and meanwhile, since the cargo is not divided for delivery and the delivery cost can be maximally reduced by adopting a one-time delivery mode, the truck with the load of 12 tons is selected for delivery, and only the cargo 10 is not delivered, the total weight of the cargo is 5.6 tons, and the cargo can be delivered by the truck with the load of 6 tons.
Through the calculation of above-mentioned embodiment, can carry out the rational distribution back with used goods, carry out the work of delivery with the goods, guarantee simultaneously that the goods does not appear cutting apart when carrying out the delivery, reduce cost.
Example two
When a certain logistics park works for 12 hours, from 8 am to 8 am, in 8 am: n =10; t =12 × 60min =720min;
data collected and calculated are shown in table 3:
| goods source | Cargo weight (ton) | Delivery time | Delivery duration (min) | Degree of urgency |
| Source of goods 1 | 1.2 | 11：00 | 180 | 4.00 |
| Goods source 2 | 0.7 | 9：30 | 90 | 8.00 |
| Goods source3 | 3.5 | 11：15 | 195 | 3.69 |
| Source of goods 4 | 4.7 | 11：40 | 220 | 3.27 |
| Goods source 5 | 2.3 | 9：50 | 110 | 6.55 |
| Source of goods 6 | 3.1 | 10：30 | 150 | 4.80 |
| Goods source 7 | 2.5 | 10：10 | 130 | 5.54 |
| Goods source 8 | 1.7 | 9：40 | 100 | 7.20 |
| Goods source 9 | 1.9 | 10：50 | 170 | 4.24 |
| Source of goods 10 | 4.1 | 11：30 | 210 | 3.43 |
TABLE 3
By passing
Calculate the degree of urgency by max ∑ i∈s h i ，
And
calculating related parameters, judging the optimal subset z, randomly generating an initial solution omega, calculating an objective function f (omega), disturbing the objective function f (omega), and generating a new solution omega i And calculating an objective function f (ω) i ) By the formula:
Δf＝f(ω i )-f(ω)
when Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) When Δ f is>And 0, receiving a new solution according to Metropolis criterion, judging whether the new solution reaches iteration times, if the new solution does not meet the iteration times, re-disturbing the new solution, if the iteration times are met, judging whether the new solution meets a termination condition, if the iteration times are met, re-disturbing the new solution, and if the iteration times are not met, returning to the optimal solution.
From the above optimal subset, the optimal distribution combination is then composed of the following items: and = (= { cargo 1, cargo 2, cargo 3, cargo 4, cargo 5, cargo 6}, this time 8.
The undelivered goods at this time are: cargo 7, cargo 8, cargo 9, cargo 10, as shown in table 4
| Goods source | Cargo weight (ton) | Delivery time | Distribution time (min) | Degree of urgency |
| Goods source 7 | 2.5 | 10：10 | 130 | 5.54 |
| Goods source 8 | 1.7 | 9：40 | 100 | 7.20 |
| Source of goods 9 | 1.9 | 10：50 | 170 | 4.24 |
| Source of goods 10 | 4.1 | 11：30 | 210 | 3.43 |
TABLE 4
Existing trucks weigh 6 tons and 10 tons, passing
Calculate the degree of urgency by max ∑ i∈s h i ，
And
calculating related parameters, judging the optimal subset z, randomly generating an initial solution omega, calculating an objective function f (omega), disturbing the objective function f (omega), and generating a new solution omega i And calculating an objective function f (ω) i ) By the formula:
Δf＝f(ω i )-f(ω)
when Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) When Δ f is>When 0, receiving a new solution according to Metropolis criterion, judging whether the new solution reaches iteration times, if not, re-disturbing the new solution, if so, judging whether the new solution meets the termination condition, if not, re-disturbing the new solution, and if so, returning to the optimal solutionThe distribution combination consists of the following goods: the transport tonnage is 8.5 tons, and by the above operation, the utilization efficiency of the truck can be maximized on the premise of fully considering the urgency degree of the cargo source, the cargo allocation time is saved, and meanwhile, the delivery is not divided, and the delivery cost can be maximally reduced by adopting a one-time delivery mode, so that the delivery work is performed by comprehensively selecting the truck with the load of 10 tons, and meanwhile, only the residual cargo 7 is not delivered, the total weight of the cargo is 1.7 tons, and the delivery can be performed by the truck with the load of 6 tons.
Through the calculation of above-mentioned embodiment, can carry out the rational distribution back with used goods, carry out the work of delivery with the goods, guarantee simultaneously that the goods does not appear cutting apart when carrying out the delivery, reduce cost.
Synthesize two embodiments, can help the commodity circulation garden to carry out the combination of optimization to the goods delivery, can be when not dividing the goods and delivering, reduce the cost of delivery, the more intelligent delivery of joining in marriage goods, it provides higher requirement to solve how to select fast and rationally to transport the goods of the different owners of goods of same place to load, help the car owner can maximize the utilization ratio of loading space, provide the loading simultaneously and guide, make things convenient for its loading work of accomplishing the goods, shorten the time of joining in marriage goods, improve the efficiency of delivery.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (5)
1. A logistics park vehicle loading method based on a simulated annealing algorithm is characterized by comprising the following steps:
the method comprises the following steps: acquiring information of a cargo owner order, wherein the information comprises cargo weight information, cargo distribution time information and distribution place information; the information of the trucks to be dispatched in the region comprises truck load information and vehicle distribution fee;
step two: ordering the goods owner orders according to the goods distribution time information, and setting an order distribution priority;
step three: arranging and combining goods owner order information and goods vehicle information, obtaining an optimal subset through a simulated annealing algorithm, and carrying out goods delivery according to the optimal subset;
when goods are delivered, the variable x =1 is defined; otherwise x =0, determining a loading combination variable x i (i＝1、2、……、n)，h i For delivery time priority, loading combined variable x i The following equation is satisfied:
solving the optimal configuration by using a simulated annealing algorithm, randomly generating an initial solution omega, calculating an objective function f (omega), disturbing the objective function f (omega), and generating a new solution omega i And calculates an objective function f (ω) i )：
Δf＝f(ω i )-f(ω)
When Δ f ≦ 0, a new solution is accepted, i.e., ω = ω i ，f(ω)＝f(ω i ) (ii) a When Δ f>When 0, receiving a new solution according to Metropolis criterion, and judging whether the new solution reaches iteration for a plurality of times; if the iteration is not satisfied for a plurality of times, the iteration is disturbed again, if the iteration is satisfied for a plurality of times, whether the iteration satisfies the termination condition or not is judged, if the iteration does not satisfy the termination condition, the iteration is disturbed again, and if the iteration satisfies the termination condition, the optimal solution is returned.
2. The logistics park vehicle loading method based on simulated annealing algorithm as claimed in claim 1, characterized in that: the sorting method in the second step comprises the following steps: the time distance that the goods of the owner i must be delivered is t when the worker in the logistics park is in train distribution i (i =1,2, …, n), the daily business hours of the logistics park is T, and the following formula is calculated:
wherein h is i The larger the goods are, the higher the priority of the goods is represented, and the logistics park prioritizes the distribution.
3. The logistics park vehicle loading method based on simulated annealing algorithm as claimed in claim 1, characterized in that: the simulated annealing algorithm in the third step comprises the following steps: when there are several goods owners to send goods to some place, form goods source set s and find subset s i So that:
wherein h is i >0。
4. The logistics park vehicle loading method based on simulated annealing algorithm of claim 1 or 3, characterized in that: the simulated annealing algorithm in the third step further comprises: record the weight of the cargo as w 1 ，w 2 ，…，w n Forming a set m of weights of the goods, finding a subset m i So that:
wherein W is the total weight of the cargo.
5. The logistics park vehicle loading method based on simulated annealing algorithm as claimed in claim 1, wherein: the optimal subset z needs to satisfy that z is less than or equal to a, and the weight of the goods is not divided; when z > a, searching again for the truck meeting the condition, and satisfying the formula:
wherein, a is the load of the truck, a z Representing the current satisfaction of the optimal subsetz, and d is the vehicle delivery fee.
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| CN116739482A (en) * | 2023-08-15 | 2023-09-12 | 宁波安得智联科技有限公司 | Order packing method, order packing equipment and computer readable storage medium |
| CN117557077A (en) * | 2024-01-12 | 2024-02-13 | 宁波安得智联科技有限公司 | Method for distributing capacity, capacity distribution device, and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116739482A (en) * | 2023-08-15 | 2023-09-12 | 宁波安得智联科技有限公司 | Order packing method, order packing equipment and computer readable storage medium |
| CN117557077A (en) * | 2024-01-12 | 2024-02-13 | 宁波安得智联科技有限公司 | Method for distributing capacity, capacity distribution device, and storage medium |
| CN117557077B (en) * | 2024-01-12 | 2024-04-26 | 宁波安得智联科技有限公司 | Method for distributing capacity, capacity distribution device, and storage medium |
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