The cold chain logistics distribution industry not only demands all goods can be timely distribution but also requires to reduce the entire logistics transportation cost as far as possible, and distribution vehicle route optimization is the key problem of cold chain logistics transportation cost calculation. The traditional optimization method spends a lot of time to search so that it is tough to find the globally optimal path approach, which results in higher distribution costs and lower efficiency. To solve the abovementioned problems, a cold logistics distribution path optimization solution, ground on an improved ant colony optimization algorithm (IACO) is formulated. Specially, other constraints, e.g., the transport time factor, transport cooling factor, and mean road patency factor, can be added to the unified IACO. Meanwhile, the updating mode of traditional pheromone is improved to limit the maximum and minimum pheromone concentration on the road and change the path selection transfer probability. The simulation results and experiment make clear that the IACO algorithm is lower than the chaotic-simulated annealing ant colony algorithm (CSAACO) and the traditional ACO algorithm in terms of convergence speed, logistics transportation distance, and logistics delivery time. At the same time, we have successfully obtained the optimal logistics distribution path, which can provide valuable reference information for improving the economic benefits of cold chain logistics enterprises.

With the increasing exploitation of the modern economy, advanced science, and information technology, the existing online shopping paradigm has gradually become an indispensable way of life for people. This shopping mode not only greatly facilitates people’s life but also drives the mushroom growth of the emerging logistics industry paradigm. As the “third benefit source,” logistics has an increasingly obvious impact on economic development, and more and more enterprises have joined logistics, which has gradually become one of the industries with fierce competition [

As a result of the late start disadvantages of domestic logistics in the current situation, the research time of emerging logistics distribution path design is relatively short. At first, the driver mainly plans the optimal logistics distribution path with his own experience. Due to the lack of scientific guidance, the obtained logistics distribution path is not the optimal one, with low logistics distribution efficiency and high logistics distribution cost.

The key issue to the vehicle routing problem (VRP) lies in the optimization of the whole logistics system [

It can be seen from the existing studies, considering the ACO algorithm, that there are two problems in the cold chain logistics path scheduling and optimization. On the one hand, there are few studies on the specific application requirements of the ACO algorithm in the cold chain logistics path optimization, ignoring the influence of the algorithm itself on the optimization accuracy and convergence speed. On the other hand, the constraint conditions of path optimal scheduling are too simple to meet the requirements of optimization accuracy under the current complex road conditions. Aiming at the existing problems in the current research, this paper proposes an improved ant colony optimization (IACO) algorithm-based cold chain logistics distribution scheduling and optimization method to reduce cold chain logistics transportation cost and improve transportation efficiency from the perspective of the transport time factor, transport cooling factor, and mean road patency factor.

The ACO is a population intelligent optimization algorithm, which can acquire the typical shortest path between demand nest and food via using the parallel mechanism of positive feedback and the cooperation between ants. This algorithm not only has the advantages of good parallelism and fast solving speed but also has an excellent performance in path optimization and task assignment. Therefore, the ACO algorithm has attracted extensive attention in the field of optimization research and developed very fast in the recent years, from the perspective of medical logistics distribution, agricultural products logistics distribution, port logistics ship transportation path, and supermarket logistics distribution, as well as cold chain logistics distribution.

Based on analyzing the characteristics of medical logistics distribution, the literature [

Based on the existing researches, there are two problems in the intelligent logistics path scheduling optimization, considering ACO algorithm: (1) there are few types of research on the specific application requirements of ant colony algorithm taking into the intelligent logistics path optimization consideration, ignoring the clear influence of the scheduling algorithm itself on the optimization accuracy and convergence speed; (2) the constraint conditions for path optimization are too simple to meet the requirements of optimization accuracy under the current complex road conditions. Aiming at the existing problems in the present research, this paper proposes an intelligent logistics distribution path optimization method based on an improved ant colony algorithm.

Aiming at the problems of low solving efficiency and high solving error rate in the current cold chain logistics distribution path scheduling and design methods, to build up the success rate of logistics distribution path formulation as shown in Figure

Framework of the proposed action quality assessment.

VRP is an important research object in the process of cold chain logistics distribution optimization. The vehicle path optimization problem is clearly defined as an NP-hard problem [

Logistics distribution VRP paradigm can be depicted as follows: considering that a certain number of customer points

Symbol definition.

Symbol | Description |
---|---|

The delivery of customer point | |

Distribution vehicle | |

All shipping costs from customer point | |

Customer point | |

Customer point actual volume of traffic |

The ACO algorithm is regarded as an AI optimization model that simulates the behavior of natural ant colonies in their search for food, which shows that the ant can choose the route according to the pheromone secreted by the preceding ant, and the probability of the route to the food source is proportional to the pheromone intensity secreted on the route. Therefore, a feedback phenomenon of information will be formed in the path of ants, i.e., the more the ants choose a certain path, the more the pheromones are left on the path, and the more likely the subsequent ants will choose this path to find the shortest path.

Through experts researching for years, the application of ACO algorithm has made great research advance and extensive role in various engineering fields. The algorithm with slower speed of convergence is tending to split up into local optimal solution, and other shortcomings can be resolved via the improvement of local pheromone updating rule, dynamic adjustment of related parameters and optimum combination, and global update strategy implementation algorithm optimization to improve the convergence speed of ant colony algorithm, enhance the global search randomness, and significantly inhibit the algorithm appear premature phenomenon.

Suppose in the moment of

The prior yet promising ACO paradigm has many advantages, e.g., strong robustness, positive feedback algorithm, and ease to combine with other algorithms. However, the traditional ACO approach also inevitably takes possession of some defects, e.g., it is easy to split up into the local optimal solution, which leads to the phenomenon of search stops, and it needs to search for a long time, when resolving the prior problem of NP-hard, traditional ACO approach for the optimal solution, considering faster speed and higher efficiency, which will be relatively low in the application of traditional ACO paradigm for an optimal path in the process of logistics transportation, and a common method is to solve the cold chain logistics transportation, the shortest distance between the starting point to finish path length is used to measure the merits of the solution. However, in the process of logistics scheduling, it will be an urge to consider the selected road of the average degree of free distribution, e.g., the cost of transportation and shipping time. Therefore, the choice of the road is more than one constraint conditions for the optimal solution of the problem, which considers the standard ACO algorithm, jointly considering multiple constraint conditions of the optimal path selection of IACO, the existing ACO based on heuristic function, and pheromone update methods while using the one node to the endpoint of the IACO inspired by the distance function.

The improved ant colony algorithm enhances the pheromone updating mode by using the constraint function model, which consisted of the material flow transport time factor, the logistics transport cost factor, and the road average mobility factor, so that the logistics transport time is shorter, the transport route is shorter, and the transport efficiency is higher. Heuristic function in traditional ACO algorithm is

The optimal solution obtained by the classical optimization algorithm is the local minimum near the given initial value, which is not the minimum from the global point of view. Naturally, it is easy to remind of finding the local minimum through multiple initial points and then finding the global minimum among multiple local minimum values. Based on the process proposed in [

In the analysis of the basic ACO approach, it is found that there are two pheromone updating strategies, i.e., real-time updating and global updating. The former means that the ant renovates the pheromone of the scheduling path immediately after it gets from one node to another. The latter means that the ant updates pheromones along the path only after it has traversed all the nodes. Compared with these two methods, a global updating strategy can accelerate the convergence speed rapidly. At present, many studies have shown that global update has a good effect. Meanwhile, there are some defects. For example, global update in this method usually converges too early, and many ants will quickly converge on the same path so that a better solution cannot be found and obtained, i.e., it falls into the local optimal solution situation. During pheromone updating, the system usually only updates the pheromone of the ants that finds the optimal path. The ant pheromone update usually can use the following two ways, one is to find the best performing in the process of circulation of ants; this way is usually slow convergence speed, not too early lead to rapid convergence to a certain path. The ant will continue to find a new path, and it is easier to find a better path. The other way is to find the ants with the best performance in the whole operation. This updating method can rapidly improve the convergence speed and obtain a better solution, but it also prevents the ant colony from searching for a better solution, which makes the entire ant colony easily trapped in a relatively poor path. Therefore, this paper proposes a new hybrid update pheromone strategy, which is in the process of searching previous cycle, using the iterative optimal method of pheromone update in time. This pheromone update is to find out the best in the circle of ants; this method is usually easy to find many more optimal solutions, which can effectively avoid premature of an ant colony in the poor solution. After multiple cycles (in this case, ten cycles) are completed, the updates are then performed using the global optimal solution, i.e., pheromone updates are performed using the ants that have the best performance of the entire operation. After mixing pheromone update rule was adopted, the algorithm will converge to the optimal solution concentration, thus can also find a more feasible solution, and can continue to search for other, more optimal solution and keep the fast convergence speed and can be used effectively to overcome a single global update which is easy to appear prematurely into a locally optimal solution.

The penalty cost of delivery time is related to the paradigm that whether the delivery vehicle meets the time window of the distribution point. Part of the penalty cost is the loss cost of an early arrival and waiting when the delivery vehicle arrives before the time window required by the distribution point. The other part is when the delivery vehicle arrives after the required time window at the distribution point, resulting in the penalty cost of delay. Then, the time penalty cost is

The main purpose of refrigeration is to maintain a constant temperature to ensure the freshness of fresh products. Suppose that the refrigeration cost mainly includes the cost of keeping the carriage temperature constant in the transportation process of distribution vehicles, the cost of an early arrival and waiting of distribution vehicles, and the cost of the lateness of distribution vehicles. For simplicity, transportation cost is usually related to the price of the product and fuel consumption caused by the multivehicles. Importantly, the longer the vehicle travels, the higher the distribution cost will be. Let the cooling cost be formulated by the ratio of the actual cooling cost

Traffic performance index (TPD) is an index that comprehensively reflects the unblocked conditions of regional traffic roads [

To build up the convergence speed of this algorithm, the quality path-based worst path is weakened, and the penalty factor is added to reduce the probability of its being selected and increase the pheromone concentration on the stronger and better-quality path leads the ant to choose the better-quality path. The pheromone renovation in standard ant colony algorithm occurs locally. Substituting equations (

The steps to solve the optimal logistics distribution path of IACO algorithm are represented in Algorithm

Through the above introduction of algorithm improvement and detailed process, this experiment will use test data to simulate the IACO scheduling paradigm and analyze the test results to evaluate the quality of the improved algorithm for cold chain logistics.

The operating platform of the simulation system is characterized by a CPU capacity of 2.6GMHz, a memory size of 4 GB and OS mode of Windows 7. To analyze the performance of the optimal cold chain logistics distribution path design method based on the proposed IACO algorithm, considering transport time factor, transport cooling factor, and mean road patency factor, MATLAB software is used to realize the simulation test. The key parameter settings of IACO are interpreted in Table

Parameters setting of the IACO approach.

Symbol | Description | Value |
---|---|---|

1 | Pheromone factor | |

3 | Expected heuristic factor | |

0.4 | Pheromone volatility | |

0.33 | Weight coefficient of transport time | |

0.33 | Weight coefficient of transport cooling | |

0.33 | Weight coefficient of mean road patency | |

300 | Maximization iterations | |

21 | Total number of customer points | |

8 | Total number of distribution vehicles | |

125 | Total distribution vehicle capacity |

It is known that a logistics company, only one distribution center, has 8 distribution vehicles, the maximum load resource of each vehicle is 10 T, and the maximum driving distance of each vehicle is 500 km, and it needs to provide resource services for 30 customer points at the same time. The basic information and location of the logistics distribution center and customers are shown in Figure

Customer location information map in Guangzhou (brown points).

The proposed IACO algorithm, existing CSAACO algorithm, and traditional ACO algorithm are diffusely used to solve the optimal path of logistics vehicles. Five simulation experiments were conducted to calculate the optimal logistics distribution path length for each experiment, and the results are shown in Figure

The optimal logistics distribution path length comparison.

Compared with the ACO algorithm and the CSAACO scheduling paradigm, the average optimal path time of the IACO paradigm is shortened by 258000 s and 15000 s, respectively. When customer scale in 100∼200, task time of the algorithm for the conventional ACO algorithm, CSAACO algorithm, and the proposed IACO in this paper are not so obvious, with increasing customer scale, under the condition of the scale of performing the same task, the time of the IACO algorithm is far lower than the CSAACO and ACO algorithms. This algorithm can make up for the CSAACO algorithm and the ACO algorithm optimization time longer due to faster speed. Compared with ACO and CSAACO, the path distance of the IACO algorithm is reduced by 25000 m and 5800 m, respectively. When the customer task scale is 100, the distance of the proposed algorithm is about 2650 m shorter than that of the CSAACO algorithm. Compared with the prior ACO algorithm, the scheduling distance of the reformative algorithm is shortened by about 5 km. Moreover, with the increase in scale, the gap is gradually widening. The envisioned IACO algorithm shows more obvious advantages and has better path optimization ability.

The above experimental results make clear that the IACO scheduling paradigm has obvious improvement in optimization efficiency and quality compared with the CSAACO algorithm and ACO method. Especially as the service customer scale expands unceasingly, the proposed algorithm can more effectively deal with the scheduling problem of logistics distribution time-consuming and poor quality, the advantages are better reflected, the IACO scheduling paradigm is presented in this paper under the condition of the same customer scale task, shorten the time of the distribution tasks effectively, reduce the transportation path distance, accelerate the algorithm convergence speed, and maximize the enhanced efficiency of the logistics industry as a whole and distribution.

As can be seen from Figure

Comparison of iteration times of optimal logistics distribution path.

It can be seen in Figure

Besides, the total amount of iterations for the IACO scheduling paradigm to find the optimal logistics distribution path is significantly less than that for the CSAACO and ACO algorithm as seen in Figure

Through the analysis of Figure

Effect of

It can be seen from Figure

The problem of route choice of logistics transportation is crucial to the transportation cost, time, and efficiency of logistics enterprises, and it is a difficult problem faced by all logistics industries. It is of great practical value to analyze the optimal logistics distribution path. To solve some problems existing in the current logistics distribution path, design method is proposed in this paper based on the IACO algorithm of the optimal design method of logistics distribution path, at the same time, to join based on the transport time factor, transport cooling factor, and mean road patency factor, improved pheromone update methods, which changed the logistics path transition probability, and compared with the traditional ACO scheduling approach and CSAACO scheduling approach, the outstanding results show that the IACO algorithm can obtain the ideal logistics distribution path, and the search efficiency is high and has a very wide application prospect. Its superior performance is embodied in that it increases the global optimization ability, shortens the distribution path, reduces the cost of distribution for logistics enterprises, improves distribution efficiency, and promotes the rapid development of the logistics industry.

The data used to support the findings of this study are available from the corresponding authors upon request.

The author declares no conflicts of interest.

H. X. conceptualized the study, developed methodology, utilized software, validated the study, did formal analysis, investigated the study, provided resources, curated the data, wrote the original draft, reviewed and edited the article, visualized and supervised the study, did project administration, and was responsible for funding acquisition. The author read and agreed to the published version of the manuscript.

This work was supported Guangzhou Education Science Planning Project in 2019, “CDIO-Based Research on Innovative and Applied Talent Training Mode” (201912027); Guangzhou Maritime University Innovation and Strength Project in 2018, Logistics Engineering Key Major Construction (F410608); Guangzhou Maritime University Innovation and Strength Project in 2017, Logistics Engineering Key Major Construction (E320103); and Education and Scientific Research Project of Education Department of Guangdong Province in 2020 (2020GXJK403).