scholarly journals Optimization of Urban Cold Chain Transport Routes under Time-Varying Network Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Shouchen Liu ◽  
Cheng Zhang
Keyword(s):  
Time Window ◽  
Cold Chain ◽  
Service Reliability ◽  
Time Varying ◽  
Road Transportation ◽  
Artificial Immune ◽  
Urban Road ◽  
Road Speed ◽  
Fuzzy Time Window ◽  
Vehicle Path

Given the time-efficient characteristics of urban cold chain transportation and the time-varying characteristics of urban road speed, customers encounter the problem of limited vehicle path optimization due to a fuzzy time window. An optimization model of urban cold chain transportation with the objective function as the minimum total cost is constructed under the premise of service reliability, and an artificial immune particle swarm optimization algorithm is designed to solve the model. For an empirical analysis of Xiamen’s cold chain transportation, a two-stage solution involving “static optimization and dynamic optimization” is used to verify the effectiveness of the model and the practical value of this research. Results show that the time-varying model can effectively reflect the situation of urban road transportation and satisfy the timeliness requirement of urban cold chain transportation.

scholarly journals Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

2020 ◽  
Vol 2020 ◽  
pp. 1-17
Author(s):  
Zhixue Zhao ◽  
Xiamiao Li ◽  
Xiancheng Zhou
Keyword(s):  
Electric Vehicles ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Route Optimization ◽  
Cold Chain ◽  
Logistic Distribution ◽  
Time Varying ◽  
Traffic Conditions ◽  
Distribution Process ◽  
Fresh Products

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.

An optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window

2021 ◽  
pp. 1-15
Author(s):  
Jie Lian
Keyword(s):  
Vehicle Routing ◽  
Optimization Model ◽  
Time Window ◽  
Cold Chain ◽  
Low Carbon ◽  
Scheduling Optimization ◽  
Logistics Distribution ◽  
Cross Docking ◽  
Fuzzy Time Window ◽  
Distribution Cost

In order to improve the distribution efficiency of cold chain logistics and reduce the distribution cost, an optimization model of cross-docking scheduling of cold chain logistics based on fuzzy time window is constructed. According to the complexity of cold chain logistics network, a multi-objective optimization model of cross-docking scheduling of cold chain logistics vehicle routing with fuzzy time window is established. In order to ensure the lowest total cost of cold chain logistics distribution and improve the overall customer satisfaction with service time, the Drosophila optimization algorithm is used to solve the model to obtain the optimal vehicle routing of cross-docking scheduling optimization of cold chain logistics. The simulation test results show that: after the application of the model, the cold chain logistics distribution time is significantly shortened, the distribution cost is significantly reduced, the damage cost is reduced, the carbon emission of vehicles is reduced, and the economic and low-carbon benefits are significantly improved, which can be used as an effective tool to solve the cross-docking scheduling optimization problem of cold chain logistics.

scholarly journals Model and Algorithm of Two-Stage Distribution Location Routing with Hard Time Window for City Cold-Chain Logistics

2020 ◽  
Vol 10 (7) ◽  
pp. 2564
Author(s):  
Liying Yan ◽  
Manel Grifoll ◽  
Pengjun Zheng
Keyword(s):  
Time Window ◽  
Hybrid Genetic Algorithm ◽  
Distribution Networks ◽  
Cold Chain ◽  
Two Stage ◽  
Logistics Network ◽  
Stage Distribution ◽  
Hard Time ◽  
Location Routing ◽  
Logistics Enterprises

Taking cold-chain logistics as the research background and combining with the overall optimisation of logistics distribution networks, we develop two-stage distribution location-routing model with the minimum total cost as the objective function and varying vehicle capacity in different delivery stages. A hybrid genetic algorithm is designed based on coupling and collaboration of the two-stage routing and transfer stations. The validity and feasibility of the model and algorithm are verified by conducting a randomly generated test. The optimal solutions for different objective functions of two-stage distribution location-routing are compared and analysed. Results turn out that for different distribution objectives, different distribution schemes should be employed. Finally, we compare the two-stage distribution location-routing to single-stage vehicle routing problems. It is found that a two-stage distribution location-routing system is feasible and effective for the cold-chain logistics network, and can decrease distribution cost for cold-chain logistics enterprises.

scholarly journals Impact analysis of two kinds of failure strategies in Beijing road transportation network

2018 ◽  
Vol 29 (01) ◽  
pp. 1850005
Author(s):  
Zundong Zhang ◽  
Xiaoyang Xu ◽  
Zhaoran Zhang ◽  
Huijuan Zhou
Keyword(s):  
Network Model ◽  
Transportation Network ◽  
Dynamic Network ◽  
Network Models ◽  
Global Network ◽  
Statistical Characteristics ◽  
Road Transportation ◽  
Road Infrastructure ◽  
Urban Road ◽  
Infrastructure Network

The Beijing road transportation network (BRTN), as a large-scale technological network, exhibits very complex and complicate features during daily periods. And it has been widely highlighted that how statistical characteristics (i.e. average path length and global network efficiency) change while the network evolves. In this paper, by using different modeling concepts, three kinds of network models of BRTN namely the abstract network model, the static network model with road mileage as weights and the dynamic network model with travel time as weights — are constructed, respectively, according to the topological data and the real detected flow data. The degree distribution of the three kinds of network models are analyzed, which proves that the urban road infrastructure network and the dynamic network behavior like scale-free networks. By analyzing and comparing the important statistical characteristics of three models under random attacks and intentional attacks, it shows that the urban road infrastructure network and the dynamic network of BRTN are both robust and vulnerable.

scholarly journals Oil Price Forecasting Using a Time-Varying Approach

Energies ◽  
2020 ◽  
Vol 13 (6) ◽  
pp. 1403
Author(s):  
Lu-Tao Zhao ◽  
Shun-Gang Wang ◽  
Zhi-Gang Zhang
Keyword(s):  
Crude Oil ◽  
Time Window ◽  
Oil Prices ◽  
Oil Price ◽  
Price Forecasting ◽  
Time Varying ◽  
Crude Oil Price ◽  
Trend Prediction ◽  
Oil Market ◽  
Polynomial Decomposition

The international crude oil market plays an important role in the global economy. This paper uses a variable time window and the polynomial decomposition method to define the trend term of time series and proposes a crude oil price forecasting method based on time-varying trend decomposition to describe the changes in trends over time and forecast crude oil prices. First, to characterize the time-varying characteristics of crude oil price trends, the basic concepts of post-position intervals, pre-position intervals and time-varying windows are defined. Second, a crude oil price series is decomposed with a time-varying window to determine the best fitting results. The parameter vector is used as a time-varying trend. Then, to quantitatively describe the continuation of the time-varying trend, the concept of the trend threshold is defined, and a corresponding algorithm for selecting the trend threshold is given. Finally, through the predicted trend thresholds, the historical reference data are selected, and the time-varying trend is combined to complete the crude oil price forecast. Through empirical research, it is found that the time-varying trend prediction model proposed in this paper achieves a better prediction than several common models. These results can provide suggestions and references for investors in the international crude oil market to understand the trends of oil prices and improve their investment decisions.

A COMPARISON OF FORECASTING THE RESULTS OF ROAD TRANSPORTATION NEEDS

Transport ◽  
2012 ◽  
Vol 27 (1) ◽  
pp. 73-78 ◽  
Author(s):  
Bogna Mrówczyńska ◽  
Karolina Łachacz ◽  
Tomasz Haniszewski ◽  
Aleksander Sładkowski
Keyword(s):  
Harmonic Analysis ◽  
Percentage Error ◽  
Road Transportation ◽  
Artificial Immune ◽  
Mathematical Methods ◽  
Average Percentage ◽  
The Past ◽  
Forecasting Methods ◽  
Average Percentage Error

Determining the size and quality of transport needs would not be possible without adequate forecasting based on the sales volume or demand for this service from the past periods. Traditional forecasting methods use econometric models that may be subject to serious errors. The use of the methods taking into account the variability of the studied phenomena or more advanced mathematical methods enables to minimize the error. Various methods of artificial intelligence such as a neural network, fuzzy sets, genetic algorithms, etc., have been recently successfully applied. The aim of this paper is to compare three forecasting methods that can be used for predicting the volume of road freight. The article deals with the effectiveness of three prediction methods, namely Winter's method for seasonal problems – a multiplicative version, harmonic analysis and harmonic analysis aided by the artificial immune system. The effectiveness of prediction was counted using MAPE errors (main average percentage error). The results of calculations were compared and the best example was presented.

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