Cold chain logistics
Definition of cold chain logistics
Cold chain logistics refers to perishable and perishable products such as agricultural products, meat delicatessens, and dairy products that must remain in a low-temperature environment [10, 11].
Characteristics of cold chain logistics
The main characteristics of cold chain logistics are as follows.
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1)
Storage and transportation products are perishable and perishable
During the storage and transportation process, with the passage of time, the deterioration of storage and transportation products is accelerated. Once the product deteriorates and the customer is unwilling to purchase it, the enterprise experiences a large amount of waste. Different products have different temperature requirements for storage and transportation; some require refrigerated processing, and some require frozen processing.
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2)
Large investment in cold chain infrastructure
To ensure the quality of cold chain logistics production [12], storage and transportation products and to keep them in a low-temperature environment, the products need to be stored in cold storage, and a large amount of refrigeration equipment must be purchased to monitor the temperature in real time. The daily maintenance of cold chain equipment and the training of personnel using refrigeration equipment require significant investment. Perishable products require low temperature throughout the whole process from procurement, production, and processing to transportation and distribution, especially the connection of each link. Therefore, cold chain logistics requires more investment and management funds than room temperature logistics.
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3)
High level of cold chain logistics technology and information technology requirements
Cold chain products must be kept in a low-temperature environment due to their own particularities [13]; otherwise, the products will rot and deteriorate, which will affect sales and reduce customer satisfaction. The whole process of cold chain products from procurement, production, storage, and transportation involves many technologies, such as refrigeration technology, the selection of refrigerants, the management of refrigeration systems, and vehicle refrigeration technology. To maintain the coordination of the cold chain and the freshness of the product, it is necessary to monitor the real-time status of the cold chain throughout the process. This process requires the use of RFID, GPS, GIS, GPRS and other logistics technologies.
Cold chain logistics composition
Freezing
Cooling is the process of manually lowering the temperature using a refrigerant. Precooling refers to the process of quickly lowering the temperature of products that need to be refrigerated and transported to the final temperature in advance. This approach can extend the shelf life of fruits and vegetables and reduce their drying during circulation and transportation. Consumption and loss ensure that consumers can buy fresh green products.
Frozen storage
Frozen storage is a process after freezing processing. On the one hand, it is used to keep the processed products in a low-temperature environment, to keep the products fresh and to prevent decay and deterioration. On the other hand, it is used to transport the products processed in the first step to a distribution centre or to the merchant for storage, which can alleviate the imbalance in production and sales and ensure that goods are available to consumers during the off-peak season.
Refrigerated transportation and distribution
Refrigerated transportation and distribution refer to the low-temperature transportation and distribution of products from the entire logistics link of procurement, production processing, and distribution. This process involves the selection of transportation means according to the transportation products and distance. Temperature and humidity are the key factors affecting product quality. These factors must be controlled for during transportation and distribution, so the good performance of the vehicle and the real-time monitoring of temperature and humidity on the vehicle are important. In addition to the two main reasons—temperature and humidity—road conditions, wear and tear during loading and unloading, and the conversion between different modes of transportation will all affect the quality of refrigerated transportation and distribution products, chain equipment and cold chain technology.
Frozen sales
Frozen sales are the last link of cold chain logistics. After a series of links, the products are finally sent to retail stores or hypermarkets. They are sold in refrigerated storage and refrigerated display cabinets in these malls for a short period of time. Only when the product is sold to consumers can the whole process of this product be considered complete, and then, the producers and operators can obtain profits. After the product is sold, the market feedback information can provide producers and operators with subsequent decision-making information on how the product is produced, sold, and operated; then, a new round of operation can begin.
Cloud computing
Cloud computing is a resource service model that combines grid computing [14], distributed computing, parallel computing, and the Internet. Its objective is to virtualize soft and hard resources such as computing, storage, and services. Software and other services integrate computing models that integrate distributed computing resources such as scalable computing, storage, data, and applications for collaborative work [15, 16]. The cloud computing architecture consists of three parts: Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS). The architecture of cloud computing is shown in Fig. 1.
The principle of cloud computing is to use distributed computing resources to provide users with various services. Users can obtain service resources anytime and anywhere according to their actual needs. Cloud computing uses Internet and virtualization technology to connect a large number of PCs or servers together; they form large clusters, virtualize resource pools, allocate resources according to user needs, and obtain higher resource utilization rates at lower costs [17, 18]. Cloud computing, as a new application model, breaks down the resource constraints under the traditional model, stores data, applications, and services in the cloud, provides powerful computing and processing capabilities, and enables the self-adaptation of business systems [19]. As long as users can access the network, they can obtain the required service resources (computing, storage, and software).
The resource layer provides infrastructure cloud computing services [20], forming a variety of physical devices, such as servers, networks, and storage devices, through virtualization technology to form dynamic virtual resources and provide them to users as network services.
The platform layer provides users with the encapsulation of resource layer services, enabling users to build their own applications using more advanced services. This layer is mainly designed for developers and includes parallel programming and development environments, middleware services, and distributed data management.
The application layer provides users with software services and interactive interfaces. Users can lease corresponding software services and customize these services according to their needs. The cloud provides the corresponding infrastructure, services, software and hardware resources. There is no need to pay for expensive hardware equipment, no need to bear frequent maintenance and upgrade costs, and no need to build their own server centre, data centre, or large computer room, thereby greatly reducing the cost of information system construction [21, 22].
The user access layer provides various support services for users to utilize the various services of cloud computing and provides various levels of access interfaces [23].
The management layer manages the cloud computing services provided at all levels.
Big data
The scope of the exploration of big data technology is wider than that of traditional data management technology. It includes not only structured data within a known range but also the degree of data correlation. It is specifically manifested in three points: the first is to store a large amount of structured and unstructured data; the second is to process a large amount of data in real time; and the third is to build an algorithm model and continuously optimize it based on real-time data [24, 25]. A typical big data technology architecture is shown in Fig. 2.
Big data processing
Mining the value contained in data is the core driving force behind the development of big data technology. Big data platforms face the challenge of processing massive amounts of data in a short period of time. When the system is busy, there will be more than 100,000 or even millions of online data processing requests per second, and thus, it is necessary to use big data real-time processing technology. Big data require different processing forms for three different types of data: the batch processing of static data, real-time processing of online data, and comprehensive processing of image data.
Static data are stored on storage media in a static form, and their accumulation process reflects the continuous precipitation of enterprise data assets. Due to the large volume of static data, ranging from the TB level to the PB level, the data accuracy is high, but the value density is low. It also takes considerable time to process static data and basically does not provide user-system interaction operations. The batch processing of static data is suitable for behaviour analysis in social networks, product recommendations in e-commerce, and cost and efficiency analysis in public service fields.
Technologies suitable for the batch processing of static data include the MapReduce computing framework, which was first proposed and promoted by Google. It has great advantages in processing large data in a highly parallel and scalable manner. The programming interface is simple. You can use cheap X86 servers to build a large-scale big data framework with strong input and output (I/O) capabilities, which is easy to understand and use. MapReduce uses the working principle of decomposition first (Map) and then merge processing (Reduce). It can divide large data files and distribute them to multiple computing nodes for parallel processing. After processing, they are summarized. This data processing technology greatly improves the processing speed and has great scalability and high availability.
Big data analysis
Big data analysis is used to conduct in-depth observations of data to discover the relationships, patterns and trends that are valuable for decision-making and then to use discovery to establish decision-making models and provide predictive support methods and processes. In the process of big data analysis, appropriate statistical analysis methods and tools are used to extract the most valuable information from the collected data and play a role in the status quo analysis, cause analysis, and quantitative predictive analysis. Before performing data analysis tasks, clear analysis goals and assumptions need to be set, and then, whether the assumptions are correct needs to be verified through comparative analysis, group analysis, cross analysis, regression analysis and other methods; next, the analysis results need to be interpreted, and the corresponding data analysis conclusions can be drawn.
Optimization model of cold chain logistics distribution vehicle routing based on big data cloud computing
Distribution time analysis
By using the real-time road condition of vehicle travel, the driving speed of relevant sections of distribution vehicles is obtained, and the travel time of distribution vehicles is calculated so that the vehicles can choose the route with the shortest time possible in the process of driving. In the construction of the model, real-time road condition information is acquired. Through the unified access interface of the established service architecture, the real-time traffic information of the road segment in the city where the vehicles are distributed is obtained in the traffic information cloud, and the average driving speed of the vehicles in this road segment, V1, lane width LRi and the tonnage of permitted vehicles are obtained. On the basis of the value of Vi, the road traffic congestion degree, when Vi = 0, the current state of congestion high; only when LRi lane width is greater than vehicle width L and allows for traffic tonnage QRi, meaning the gross tonnage of Q is greater than that of the car, can I select the path. Therefore, the time needed for the vehicle to complete the distribution of total travel time Ts is calculated by Formula (1):
$$ {T}_s=\sum \limits_{i=1}^N{t}_{i\left(i=1\right)} $$
(1)
where ti(i − 1) is the travel time of the refrigerated truck from customer i-1 to i.
Distribution vehicle cost analysis
According to the characteristics of cold chain logistics, this paper deeply discusses the cost of goods damage caused by low-temperature products due to their corrosivity, the energy cost consumed by refrigeration equipment during vehicle distribution, the penalty cost beyond the customer’s time window, the inherent cost of distribution vehicles and the transportation cost on each road section.
Fixed costs
There are fixed costs to be borne by transport vehicles, namely, the wages of drivers and escorts and the cost of vehicle wear and tear, and the total fixed cost C1 = f is constant.
Transportation cost
In the distribution process of low-temperature products, there is a large difference in vehicle driving speed in different time periods, so the fuel consumption of vehicles also has a large difference. Especially during rush hour when traffic congestion occurs, vehicle fuel consumption will be significantly higher. The transportation costs of the vehicle includes the fuel consumption and repair and maintenance costs of the vehicle, which are dynamic and proportional to the mileage of the vehicle. For transport cost expressed by C2, Formula (2) can be obtained.
$$ {C}_2=\sum \limits_{i=0}^n\sum \limits_{j=0}^n{c}_{ij}{x}_{ij} $$
(2)
cij is the transport cost of refrigerated vehicles on section (vi,vj), and cij = cji, where xij is represented by 0,1. xij = 1 means refrigerated vehicles have passed section (vi, vj); otherwise, xij = 0.
Damage cost of cold storage
The damage cost of the cold collection is analysed in the following three cases. First, cargo damage is caused by the length of the transportation time during transportation. Second, when serving customers, the air flow caused by the opening of the compartment door, through which the cold air in the compartment and the outside air flow alternately, causes the temperature in the compartment to rise and damages the cold storage. Third, during transportation, the vehicle can be bumped due to the quality of the road, and the product may be damaged. The cost of cold storage damage is expressed by C3, which can be calculated by Formula (3):
$$ {C}_3=r\sum \limits_{j=0}^n{\lambda}_j\left({\alpha}_1{t}_{ij}+{\alpha}_2{\beta}_1+{\alpha}_3{S}_{ij}\right) $$
(3)
where r represents the unit price of the product; λj is a 0,1 variable, with λj = 1 representing the refrigerated truck serving customer j, and otherwise, λj = 0; α1 is the percentage of damage during the product delivery process; tij represents the time from customer i to customer j; α2 represents the percentage of product damage during the door opening and loading/unloading process; βj is the quantity of customer j’s cargo; α3 is the percentage of product damage during vehicle transportation; and sij is the mileage between customer i and customer j.
Energy costs of refrigerated vehicles
The cost of energy is mainly the cost of consuming refrigerant. The consumption of refrigerant is mainly related to the heat transfer coefficient of the cabin, the temperature inside the cabin, the surface area inside and outside the cabin, and the outside temperature. The amount of refrigerant consumption G can be calculated by Formula (4):
$$ G=a\times b\times S\times \Delta t $$
(4)
where G represents the consumption of refrigerant, a is a constant, b represents the heat transfer coefficient, S is the average surface area inside and outside the vehicle, and Δt is the temperature difference between the inside and outside of the vehicle. Then, the cooling cost of the vehicle during driving is represented by C4, which can be calculated by Formula (5):
$$ {C}_4=\sum \limits_{i=0}^n\sum \limits_{j=0}^n{r}_1\times G\times {t}_{ij}\times {x}_{ij} $$
(5)
where r1 is the price of the refrigerant, tij is the time from customer i to customer j, and xij is a variable of 0,1. In addition, when the door is opened, the outside air directly convects with the air in the refrigerated car to alternate between cold and heat. Therefore, when calculating the cooling cost C5 of the door, only the cost of the refrigerant consumed by the heat exchange of the door is calculated, such as in Formula (6):
$$ {C}_5=\sum \limits_{i=1}^n{r}_1\times a\times S\times \Delta t\times {t}_i $$
(6)
where ti is the time the vehicle stays and waits at customer i, and S is the area of the door at this time.
Penalty costs beyond customer delivery time
The delivery of goods by logistics companies to customers is limited by time. Here, we use the limitation method of soft time windows for optimization; that is, the goods required by customers must arrive within a certain time range. If they fail to arrive on time, then customers will impose a fine on the delivery company. To prevent the refrigerated truck from unloading the goods earlier than the time window, we adopt the early arrival waiting principle; that is, if the time of arrival of the goods is earlier than the start time of the customer’s time window, then the logistics vehicle should wait at the customer until the start of the customer’s time window and then unload. Then, the penalty cost incurred by the customer because the delivery task is not within the time window range is represented by C6, which can be calculated by Formula (7):
$$ {C}_6={\omega}_1\sum \limits_{j=1}^n\max \left[\left({a}_j-{s}_j-{t}_j\right),0\right]+{\omega}_2\sum \limits_{j=1}^n\max \left[\left({s}_j-{b}_j\right),0\right] $$
(7)
where ω1, ω2 represent the loss cost caused by the refrigerated vehicle unloading the goods earlier and later than the time window, respectively. sj is the time when the vehicle arrives at customer j, and tj is the time when the vehicle waits at customer j.
Other costs
In the process of the distribution of goods, under time constraints, vehicles will sometimes travel on expressways, which will generate high-speed charging costs. At the same time, some cities will levy different traffic congestion charges when vehicles drive during peak periods. These costs are expressed as C7 and can be obtained by Formula (8).
$$ {C}_7=\sum \limits_{j=1}^m{g}_i $$
(8)
Establish an optimized path model for cold chain logistics distribution vehicles
The path decision model of vehicle transport in cold chain logistics is constructed as follows:
$$ \mathit{\operatorname{Min}}\left\{{W}_1B+{W}_2C\right\} $$
(9)
Among them,
$$ B={T}_s+\sum \limits_{i=1}^N{t}_j $$
(10)
$$ C={C}_1+{C}_2+{C}_3+{C}_4+{C}_5+{C}_6+{C}_7 $$
(11)