CN112101638B - Cooperative optimization method for urban logistics distribution range - Google Patents

Cooperative optimization method for urban logistics distribution range Download PDF

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CN112101638B
CN112101638B CN202010875016.2A CN202010875016A CN112101638B CN 112101638 B CN112101638 B CN 112101638B CN 202010875016 A CN202010875016 A CN 202010875016A CN 112101638 B CN112101638 B CN 112101638B
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温惠英
蒋晗
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South China University of Technology SCUT
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Abstract

The invention discloses a city logistics distribution range collaborative optimization method, which comprises the following steps: 1) acquiring urban road network structure information, urban road network real-time traffic flow data, distribution center position and corresponding capacity information, distribution station position and corresponding demand information, and setting the minimum total distribution cost as an optimization principle; 2) calculating the cost C from each center to each station according to the acquired urban road information such as real-time traffic flow data and the like, and matching according to the principle of minimum total cost in the step 1); 3) after the step 2) is finished, the remaining sites which are not matched yet enter a second matching process: in the process, the rest sites are taken as objects, a distribution center with the minimum distribution cost is searched for matching, and the matching process is ended after all the sites are matched; 4) and outputting a distribution range division optimization result comprising distribution cost and a distribution range division scheme. The invention can improve the logistics distribution efficiency and reduce the distribution cost.

Description

Cooperative optimization method for urban logistics distribution range
Technical Field
The invention relates to the technical field of traffic logistics distribution, in particular to a city logistics distribution range collaborative optimization method.
Background
With the rapid development of the internet era and the steady promotion of national economy, the logistics industry plays an increasingly important role in the increasing material demand of people, and for logistics enterprises, the distribution cost is the most important consideration factor in the whole distribution process. In a complex and variable traffic network, how to be able to have a logistics distribution scheme that minimizes the total cost is a first problem to be solved by logistics enterprises nowadays. The distribution range division is particularly important in the whole distribution scheme. The distribution range division refers to a process of determining the distribution range of the center and matching the center and the stations with each other on the basis of the known positions of the center and the stations. By dividing the distribution range and effectively matching the center and the stations, the optimal distribution scheme can be obtained, so that the aim of saving the distribution cost is fulfilled. The disadvantages of the current urban logistics distribution method are probably the following: (1) due to the limitations of a solving algorithm and an optimization method, the division of the logistics distribution range is difficult to have global optimality and only feasible; (2) the weight calculation between the distribution center and the station is constant, is not influenced by factors such as time, traffic flow, climate and oil price, and is not in accordance with the reality. (3) When the distribution range of the stations is clustered and divided, the topological structure of a road network is often ignored, only the geometric position distance between two points is considered, the distance is not consistent with the actual distance, and the method is difficult to be directly applied. In view of this, a method for collaborative optimization of urban logistics distribution range is provided to achieve the purpose of minimizing the total distribution cost. The method can optimize the distribution range division process of the logistics enterprises, is more suitable for other traffic and transportation range division scenes, namely the distribution range division and other traffic and transportation range division optimization scenes of the logistics enterprises are all in the research range of the method, and can provide reference and support for the traffic and transportation range division.
Disclosure of Invention
The invention aims to overcome the defects and shortcomings of the prior art and provides a collaborative optimization method for an urban logistics distribution range, which is based on the traffic state of an urban road network and the capacities of a distribution center and a station to carry out collaborative optimization so as to reduce the logistics distribution cost and enable a distribution range division scheme to have higher economical efficiency, effectiveness and reliability.
In order to achieve the purpose, the technical scheme provided by the invention is as follows: a city logistics distribution range collaborative optimization method comprises the following steps:
1) acquiring urban road network structure information, urban road network real-time traffic flow data, distribution center position and corresponding capacity information, distribution station position and corresponding demand information, analyzing cost composition in the process of dividing logistics distribution ranges, selecting reasonable cost composition elements, and setting the minimum total distribution cost as an optimization principle;
2) calculating the cost C from each center to each station according to the acquired urban road network structure information and the real-time traffic flow data, and matching according to the minimum principle of the total distribution cost in the step 1): starting from a distribution center, selecting distribution stations for matching;
3) step 2) after the matching is finished, the remaining sites which are not matched yet enter a second matching process, the process takes the remaining sites as objects, a distribution center with the minimum distribution cost is searched for matching, and the matching process is finished after all the sites are matched;
4) and taking the matching mode obtained in the first two steps as an optimized logistics distribution range, calculating the final total distribution cost, and outputting a distribution range division optimization result which comprises the distribution cost and a distribution range division scheme.
In the step 1), the urban road network structure information comprises urban road network intersection adjacency information and road section length information, and urban road network structure information data can be acquired from an urban planning design scheme; the urban road network real-time traffic flow data comprises real-time information of average driving speed of vehicles at each road section of the urban road network and average traffic delay at intersections, and can be obtained from an urban road traffic command center; the distribution center position and the corresponding capacity information can obtain data information of the planned distribution center from the logistics enterprise; the distribution station position and the corresponding demand information can acquire the cargo demand distribution amount of each station from a logistics network management background; the cost component comprises the oil consumption cost of the vehicle and the road toll cost when passing through the toll station; the optimization principle is as follows: considering the distance, the oil consumption cost generated by delay and the road toll cost in the route, and optimizing by taking the minimum total distribution cost C of logistics transportation as an objective function, namely:
MinC＝C1+C2 (1)
wherein, C1Cost of fuel consumption for distance and delay time, C2Is the road tolling cost in the path.
In the steps 2) and 3), constructing a collaborative optimization basic frame according to a matching process between a distribution center and a station in the distribution range division process, dividing the matching selection between the distribution center and the station into two sub-processes, starting from a distribution center i in the first process, considering the minimum distribution cost C, selecting the distribution station by the ripple diffusion algorithm principle until the maximum distribution range R of the distribution center i is reachediOr accumulating the maximum delivery amount SiWhen the matching of the distribution center i and the k stations is finished, matching of the next distribution center i +1 is carried out until all the distribution centers reach the maximum distribution range or the constraint condition of the accumulated maximum distribution amount; the remaining sites, which have not been matched after the first process, will enter the second matching process,the process is to find the distribution center which does not reach the maximum accumulated capacity with the minimum cost as the target by taking the rest sites as the target, and the found distribution center distributes the sites.
In the steps 2) and 3), according to the running characteristics of the vehicles on the urban road, introducing a ripple diffusion algorithm and improving the termination condition of the ripple relay race, and providing a boundary ripple diffusion algorithm, wherein the specific conditions are as follows:
the expansion phenomenon of the ripples in nature reflects an optimization principle that the ripples always reach the space points closest to the centers of the ripples firstly, because the speed of the ripple expansion is the same in all directions, and when the ripples reach a certain space point, new ripples are generated to continue to diffuse outwards.
In step 4), the distribution cost is divided into two parts, and C is set1The oil consumption cost is all oil consumption cost in the transportation process, the value of the oil consumption cost is related to the distance and the transportation time, the oil consumption cost is divided into oil consumption cost generated by the transportation distance and oil consumption cost generated by delay, and the unit price is p1And p2；C2Cost per unit of p for toll in the route3Thus, there are:
Figure BDA0002652372520000041
tik＝tr-tt (3)
wherein m is the number of distribution centers, n is the number of distribution sites, tikTo delay time, trFor the actual delivery time, ttTo theoretically deliver time, /)ikThe distribution distance from the distribution center i to the station k;
C2for road toll cost in the path:
Figure BDA0002652372520000042
wherein f isik∈Wi，WiThe collection of toll road sections is passed through in the distribution route.
In step 4), after the distribution range optimization is completed, the distribution range is updated for the distribution center, the distribution sites are matched, and the distribution cost and the distribution range division scheme are output.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. in the city logistics distribution range collaborative optimization method provided by the invention, a boundary ripple diffusion algorithm is used, the algorithm is an algorithm for searching an optimal path, the robustness and the efficiency are excellent, and an optimal matching scheme can be quickly searched by utilizing the boundary ripple diffusion algorithm.
2. In a static road network, the distribution time is greatly reduced, and the time cost can be greatly saved by using the invention; in a dynamic road network, although the ripple diffusion is influenced by congestion delay in the road network, the distribution time can be effectively reduced by using the invention.
3. The invention can effectively reduce the distribution cost. The invention can effectively reduce the distribution cost no matter what road network the road network is in. The traditional distribution range division mode mostly adopts a clustering method, only Euclidean distance factors are considered, and various actual conditions in a road network are rarely considered.
4. The distribution range division is usually ignored in the logistics distribution, the invention fills the insufficient part in the distribution range division optimization method, and provides convenience and decision thinking for scholars or enterprises which relate to relevant problems in the logistics distribution process in the future.
5. After the steps are optimized according to the invention, the calculation method of the system is formed, compared with the existing distribution range optimization method, the method has stronger operability, improves the efficiency and the reliability of distribution range division, and has important theoretical significance and social value.
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FIG. 1 is a flow diagram of the method of the present invention.
FIG. 2 is a basic flow chart of the boundary ripple diffusion algorithm of the present invention.
FIG. 3 is a schematic diagram of the boundary ripple diffusion principle of the present invention.
FIG. 4 is a schematic diagram of the operation of the boundary ripple relay race of the present invention.
FIG. 5 is a basic framework diagram of the distribution range co-optimization of the present invention.
FIG. 6 is a diagram illustrating the result of the unoptimized distribution range partition according to the present invention.
Fig. 7 is a schematic diagram of a delivery range division cooperative optimization result output in the present invention.
FIG. 8 is a comparison of correlation results before and after optimization according to the present invention.
Detailed Description
The present invention will be further described with reference to the following specific examples.
As shown in fig. 1, the method for collaborative optimization of urban logistics distribution range provided by this embodiment includes the following steps:
1) the method comprises the steps of obtaining urban road network structure information, urban road network real-time traffic flow data, distribution center position and corresponding capacity information, distribution station position and corresponding demand information, analyzing cost composition in the process of dividing logistics distribution ranges, selecting reasonable cost composition elements, and setting the minimum total distribution cost as an optimization principle.
The urban road network structure information comprises urban road network intersection adjacency information and road section length information, and urban road network structure information data can be obtained from an urban planning design scheme; the urban road network real-time traffic flow data comprises real-time information of average driving speed of vehicles at each road section of the urban road network and average traffic delay at intersections, and can be obtained from an urban road traffic command center; the distribution center position and the corresponding capacity information can obtain data information of the planned distribution center from the logistics enterprises; the distribution station position and the corresponding demand information can acquire the cargo demand distribution amount of each station from a logistics network management background; the cost component includes a fuel consumption cost of the vehicle and a road toll cost when passing through the toll station.
The urban road network structure information is as follows: the scale of the nodes of the road network is N-400, each node in the road network is connected with other 2-4 nodes, a distribution center and stations are randomly generated on the nodes, the number m of the distribution centers is 5, the number N of the stations is 50, and a corresponding capacity parameter diIn the range of [1,3]In cubic meters; the simulated road network for simulating the urban road traffic condition to generate the dynamic evolution of the speed has different road section speeds among nodes, and the speed value range of a common road section is (15, 60)]km/h, the express road section is shown in a road network graph in a bold way, and the speed value is [60,80 ]]km/h; the distances between the central and the inter-site sections are different, and the distance between the nodes is [150,300 ]]The unit is meter; because the delivery vehicles arrive at the intersection randomly, the delay of the delivery vehicles passing through the intersection is assumed to be a random value, and the value range is [30,60 ]]The unit is seconds.
Setting the minimum total cost C in the distribution process as an optimization target, taking 2/3 of the center maximum distribution range value obtained by a clustering algorithm as the maximum range of each distribution center of the optimization method, and taking the accumulated capacity of the stations matched to the centers in the clustering algorithm result as the maximum capacity of each distribution center of the optimization method.
Considering the distance, the oil consumption cost generated by delay and the road toll cost in the route, and optimizing by taking the minimum total distribution cost C of logistics transportation as an objective function, namely:
MinC＝C1+C2 (1)
wherein, C1Cost of fuel consumption for distance and delay time, C2Is the road toll cost in the path.
2) Constructing a collaborative optimization basic framework according to a matching process between a distribution center and stations in a distribution range division process, dividing the matching selection between the distribution center and the stations into two sub-processes, starting from a distribution center i in the first process, considering the minimum distribution cost C, selecting the distribution stations by the ripple diffusion algorithm principle until the maximum distribution range R of the distribution center i is reachediOr accumulating the maximum delivery amount SiWhen the matching of the distribution center i and the k stations is finished, matching of the next distribution center i +1 is carried out until all the distribution centers reach the maximum distribution range or the constraint condition of the accumulated maximum distribution amount; and after the first process is finished, the remaining sites which are not matched yet enter a second matching process, the process takes the remaining sites as objects, a distribution center which does not reach the maximum accumulated capacity is searched for with the aim of minimum cost, and the searched distribution center distributes the sites.
According to the running characteristics of urban road vehicles, a ripple diffusion algorithm is introduced and the termination condition of the ripple relay race is improved, a boundary ripple diffusion algorithm is provided, the basic flow is shown in fig. 2, the diffusion principle is shown in fig. 3, the running form of the improved ripple relay race is shown in fig. 4, and the specific conditions are as follows:
the expansion phenomenon of the ripples in nature reflects an optimization principle that the ripples always reach the space points closest to the centers of the ripples firstly, because the speed of the ripple expansion is the same in all directions, and when the ripples reach a certain space point, new ripples are generated to continue to diffuse outwards.
As shown in fig. 5, the first process is to select a station for matching based on the ripple diffusion algorithm from the distribution center.
3) The remaining sites that have not been matched after the first process is finished enter a second matching process, as shown in fig. 6, in the process, the remaining sites are used as objects, a distribution center meeting the conditions is searched for matching by using a ripple diffusion algorithm, and the matching process is finished after all the sites are matched.
4) Dividing the distribution cost into two parts, C1The cost of all fuel consumption during transportation, the value of which is related to the distance and the transportation time, is divided into the cost of fuel consumption generated by the transportation distanceAnd the cost of oil consumption caused by delay, the unit price is p1And p2；C2Cost per unit of p for toll in the route3Thus, there are:
Figure BDA0002652372520000081
tik＝tr-tt (3)
wherein m is the number of distribution centers, n is the number of distribution sites, tikTo delay time, trFor the actual delivery time, ttTo theoretically deliver time, /)ikThe distribution distance from the distribution center i to the station k;
C2for road toll cost in the path:
Figure BDA0002652372520000082
wherein f isik∈Wi，WiThe collection of toll road sections is passed through in the distribution route.
And after the distribution range is optimized, updating the distribution range for the distribution center, matching distribution sites, and outputting distribution cost and a distribution range division scheme.
Then, the normal oil consumption of a large truck is 19.09 liters/100 kilometers, and the unit price of the distance oil consumption is p according to the average price of 6.67 liters/yuan of diesel oil in Guangdong province in 2 months in 202011.27 yuan/km, the idle fuel consumption is about 10% -30% of the normal fuel consumption, the idle fuel consumption is 3.818L/100 km, and the unit price of the obtained delay fuel consumption is p2The toll of taking the express way is 1.42 yuan/kilometer according to the standard of highway charging in Guangdong province in 2020. And calculating the distribution cost according to the formula.
In order to prevent accidental factors, 100 experiments are performed, an average value is taken as a result parameter, the distribution range division scheme before optimization is shown in fig. 6, the distribution range division scheme after optimization is shown in fig. 7, and the related result parameters are shown in the following table 1:
TABLE 1-100 average result parameters for random trials
Distribution cost Distribution distance Time of flight
Clustering method 431126.4 283138.4 53965.9
The method of the invention 330229.2 247127.8 23931.69
As can be seen from table 1, comparing the results before and after optimization, it can be seen from fig. 6 and 7 that the partial sites produce different matching schemes in the two methods, thereby also changing the distribution range division of the center. On the basis, the distribution cost is reduced by utilizing the invention to divide the range, the distribution cost is reduced by 23.4 percent on average, the distribution distance is reduced by 12.7 percent, the distribution time is reduced by 55.7 percent on average, and the correlation results before and after optimization are shown in a graph of fig. 8. Experimental results show that the performance of the urban logistics distribution range collaborative optimization method is superior to that of the traditional division method, and the urban logistics distribution range collaborative optimization method has high feasibility in practical application and is worthy of popularization.
The above-mentioned embodiments are merely preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, so that the changes in the shape and principle of the present invention should be covered within the protection scope of the present invention.

Claims (5)

1. A city logistics distribution range collaborative optimization method is characterized by comprising the following steps: the method comprises the following steps:
1) acquiring urban road network structure information, urban road network real-time traffic flow data, distribution center position and corresponding capacity information, distribution station position and corresponding demand information, analyzing cost composition in the process of dividing logistics distribution ranges, selecting reasonable cost composition elements, and setting the minimum total distribution cost as an optimization principle;
2) calculating the cost C from each center to each station according to the acquired urban road network structure information and the real-time traffic flow data, and matching according to the minimum principle of the total distribution cost in the step 1): starting from a distribution center, selecting distribution stations for matching;
3) step 2) after the matching is finished, the remaining sites which are not matched yet enter a second matching process, the process takes the remaining sites as objects, a distribution center with the minimum distribution cost is searched for matching, and the matching process is finished after all the sites are matched;
4) taking the matching mode obtained in the first two steps as an optimized logistics distribution range, calculating the final total distribution cost, and outputting a distribution range division optimization result which comprises a distribution cost and a distribution range division scheme;
in the steps 2) and 3), constructing a collaborative optimization basic frame according to a matching process between a distribution center and a station in a distribution range division process, dividing the matching selection between the distribution center and the station into two sub-processes, starting from a distribution center i, considering the minimum distribution cost C, and selecting the distribution station by a ripple diffusion algorithm principle until the maximum distribution range R of the distribution center i is reachediOr accumulating the maximum delivery amount SiWhen the distribution center i is matched with the k stations, the next distribution is carried outMatching the delivery centers i +1 until all the delivery centers reach the maximum delivery range or the constraint condition of accumulating the maximum delivery amount; and after the first process is finished, the remaining sites which are not matched yet enter a second matching process, the process takes the remaining sites as objects, a distribution center which does not reach the maximum accumulated capacity is searched for with the aim of minimum cost, and the searched distribution center distributes the sites.
2. The city logistics distribution range collaborative optimization method according to claim 1, characterized in that: in the step 1), the urban road network structure information comprises urban road network intersection adjacency information and road section length information, and urban road network structure information data can be acquired from an urban planning design scheme; the urban road network real-time traffic flow data comprises real-time information of average driving speed of vehicles at each road section of the urban road network and average traffic delay at intersections, and can be obtained from an urban road traffic command center; the distribution center position and the corresponding capacity information can acquire data information of the planned distribution center from the logistics enterprises; the distribution station position and the corresponding demand information can acquire the cargo demand distribution amount of each station from a logistics network management background; the cost component comprises the oil consumption cost of the vehicle and the road toll cost when passing through the toll station; the optimization principle is as follows: considering the distance, the oil consumption cost generated by delay and the road toll cost in the route, and optimizing by taking the minimum total distribution cost C of logistics transportation as an objective function, namely:
MinC＝C1+C2 (1)
wherein, C1Cost of fuel consumption for distance and delay time, C2Is the road toll cost in the path.
3. The city logistics distribution range collaborative optimization method according to claim 1, characterized in that: in the steps 2) and 3), according to the running characteristics of the vehicles on the urban road, introducing a ripple diffusion algorithm and improving the termination condition of the ripple relay race, and providing a boundary ripple diffusion algorithm, wherein the specific conditions are as follows:
the expansion phenomenon of the ripples in nature reflects an optimization principle that the ripples always reach the space points closest to the centers of the ripples firstly, because the speed of the ripple expansion is the same in all directions, and when the ripples reach a certain space point, new ripples are generated to continue to diffuse outwards.
4. The city logistics distribution range collaborative optimization method according to claim 1, characterized in that: in step 4), the distribution cost is divided into two parts, and C is set1The oil consumption cost is all oil consumption cost in the transportation process, the value of the oil consumption cost is related to the distance and the transportation time, the oil consumption cost is divided into oil consumption cost generated by the transportation distance and oil consumption cost generated by delay, and the unit price is p1And p2；C2For the cost of road tolls in the path, unit price is p3Thus, there are:
Figure FDA0003535070870000031
tik＝tr-tt (3)
wherein m is the number of distribution centers, n is the number of distribution stations, tikTo delay time, trFor the actual delivery time, ttTo calculate the theoretical delivery time,/ikThe distribution distance from the distribution center i to the station k;
C2for road toll cost in the path:
Figure FDA0003535070870000032
wherein f isik∈Wi，WiThe collection of toll road sections is passed through in the distribution route.
5. The city logistics distribution range collaborative optimization method according to claim 1, characterized in that: in step 4), after the distribution range is optimized, the distribution range is updated for the distribution center, the distribution sites are matched, and the distribution cost and the distribution range division scheme are output.
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