scholarly journals Emergency logistics vehicle routing optimization based on insufficient supply

2019 ◽  
Vol 136 ◽  
pp. 04068
Author(s):  
Yina Yuan ◽  
Xiaoguang Zhou ◽  
Mengke Yang
Keyword(s):  
Vehicle Routing ◽  
Optimization Method ◽  
Decision Makers ◽  
Emergency Vehicle ◽  
Routing Problem ◽  
Routing Optimization ◽  
Theoretical Support ◽  
Emergency Logistics ◽  
The Face ◽  
Vehicle Routing Optimization

In the face of various emergencies, emergency logistics vehicles are required to meet the needs of the affected areas in a short enough time. However, due to the suddenness of the incident and the shortage of relief supplies, it is necessary to further consider how to optimize the route of emergency vehicles in case of insufficient supply. In this paper, when the supply point is insufficient, the emergency vehicle routing can be optimized in the shortest possible time and at the same time to meet the requirements of the disaster site. By establishing the corresponding mathematical model and using the genetic algorithm to solve the relevant examples, the new solution is provided for the emergency logistics vehicle routing problem when the relief materials are insufficient. According to the analysis results of the example, the effectiveness of the optimization method is further demonstrated, and theoretical support is provided for relevant decision makers.

Logistics Distribution Vehicle Routing Problem with Time Windows

2012 ◽  
Vol 468-471 ◽  
pp. 2047-2051 ◽  
Author(s):  
Ai Ling Chen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimization Method ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Routing Problem ◽  
Logistics Distribution ◽  
Routing Optimization ◽  
Hybrid Optimization Method

Vehicle routing optimization problem is one of the major research topics in logistics distribution field. Suitable vehicle routing selection is vital to reduce the logistics cost. The paper presents a hybrid optimization method to solve the vehicle routing problem with time windows. In the hybrid optimization method, discrete particle swarm optimization algorithm is used to assign the customers on routes and simulated annealing (SA) algorithm to avoid becoming trapped in local optimum. The simulation results have shown that the proposed method is feasible and effective for the vehicle routing problem with time windows.

scholarly journals Vehicle Routing Optimization System with Smart Geopositioning Updates

2021 ◽  
Vol 11 (22) ◽  
pp. 10933
Author(s):  
Radosław Belka ◽  
Mateusz Godlewski
Keyword(s):  
Vehicle Routing ◽  
Optimization Algorithms ◽  
Real Life ◽  
Distance Matrix ◽  
Gps Tracking ◽  
Routing Problem ◽  
Optimization System ◽  
Routing Optimization ◽  
Vehicle Routing Optimization ◽  
The Impact

Solving the vehicle routing problem (VRP) is one of the best-known optimization issues in the TLS (transport, logistic, spedition) branch market. Various variants of the VRP problem have been presented and discussed in the literature for many years. In most cases, batch versions of the problem are considered, wherein the complete data, including customers’ geographical distribution, is well known. In real-life situations, the data change dynamically, which influences the decisions made by optimization systems. The article focuses on the aspect of geopositioning updates and their impact on the effectiveness of optimization algorithms. Such updates affect the distance matrix, one of the critical datasets used to optimize the VRP problem. A demonstration version of the optimization system was developed, wherein updates are carried out in integration with both open source routing machine and GPS tracking services. In the case of a dynamically changing list of destinations, continuous and effective updates are required. Firstly, temporary values of the distance matrix based on the correction of the quasi-Euclidean distance were generated. Next, the impact of update progress on the proposed optimization algorithms was investigated. The simulation results were compared with the results obtained “manually” by experienced planners. It was found that the upload level of the distance matrix influences the optimization effectiveness in a non-deterministic way. It was concluded that updating data should start from the smallest values in the distance matrix.

scholarly journals Vehicle-Routing Optimization for Municipal Solid Waste Collection Using Genetic Algorithm: The Case of Southern Nablus City

2017 ◽  
Vol 26 (3) ◽  
pp. 43-57 ◽  
Author(s):  
Ramiz Assaf ◽  
Yahya Saleh
Keyword(s):  
Genetic Algorithm ◽  
Solid Waste ◽  
Vehicle Routing ◽  
Service Level ◽  
Current Situation ◽  
Waste Collection ◽  
Solid Waste Collection ◽  
Routing Problem ◽  
Routing Optimization ◽  
Vehicle Routing Optimization

Abstract Municipalities are responsible for solid waste collectiont for environmental, social and economic purposes. Practices of municipalities should be effective and efficient, with the objectives of reducing the total incurred costs in the solid waste collection network concurrently achieving the highest service level. This study aims at finding the best routes of solid waste collection network in Nablus city-Palestine. More specifically, the study seeks the optimal route that minimizes the total travelled distance by the trucks and hence the resulted costs. The current situation is evaluated and the problem is modelled as a Vehicle Routing Problem (VRP). The VRP is then optimized via a genetic algorithm. Specifically, compared to the current situation, the trucks total travelled distance was reduced by 66%, whereas the collection time was reduced from 7 hours per truck-trip to 2.3 hours. The findings of this study is useful for all municipality policy makers that are responsible for solid waste collection.

scholarly journals Vehicle Routing Problem for Collaborative Multidepot Petrol Replenishment under Emergency Conditions

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Guangcan Xu ◽  
Qiguang Lyu
Keyword(s):  
Vehicle Routing ◽  
Time Window ◽  
Programming Model ◽  
Distribution Network ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Routing Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Vehicle Routing Optimization

In recent years, emergency events have affected urban distribution with increasing frequency. For example, the 2019 novel coronavirus has caused a considerable impact on the supply guarantee of important urban production and living materials, such as petrol and daily necessities. On this basis, this study establishes a dual-objective mixed-integer linear programming model to formulate and solve the cooperative multidepot petrol emergency distribution vehicle routing optimization problem with multicompartment vehicle sharing and time window coordination. As a method to solve the model, genetic variation of multiobjective particle swarm optimization algorithm is considered. The effectiveness of the proposed method is analyzed and verified by first using a small-scale example and then investigating a regional multidepot petrol distribution network in Chongqing, China. Cooperation between petrol depots in the distribution network, customer clustering, multicompartment vehicle sharing, time window coordination, and vehicle routing optimization under partial road blocking conditions can significantly reduce the total operation cost and shorten the total delivery time. Meanwhile, usage of distribution trucks is optimized in the distribution network, that is, usage of single- and double-compartment trucks is reduced while that of three-compartment trucks is increased. This approach provides theoretical support for relevant government departments to improve the guarantee capability of important materials in emergencies and for relevant enterprises to improve the efficiency of emergency distribution.

scholarly journals On the Use of Learnheuristics in Vehicle Routing Optimization Problems with Dynamic Inputs

Algorithms ◽  
2018 ◽  
Vol 11 (12) ◽  
pp. 208 ◽  
Author(s):  
Quim Arnau ◽  
Angel Juan ◽  
Isabel Serra
Keyword(s):  
Vehicle Routing ◽  
Optimization Problems ◽  
Time Windows ◽  
Real Life ◽  
Routing Problem ◽  
Routing Optimization ◽  
Statistical Learning Techniques ◽  
Critical Activity ◽  
The Cost ◽  
Vehicle Routing Optimization

Freight transportation is becoming an increasingly critical activity for enterprises in a global world. Moreover, the distribution activities have a non-negligible impact on the environment, as well as on the citizens’ welfare. The classical vehicle routing problem (VRP) aims at designing routes that minimize the cost of serving customers using a given set of capacitated vehicles. Some VRP variants consider traveling times, either in the objective function (e.g., including the goal of minimizing total traveling time or designing balanced routes) or as constraints (e.g., the setting of time windows or a maximum time per route). Typically, the traveling time between two customers or between one customer and the depot is assumed to be both known in advance and static. However, in real life, there are plenty of factors (predictable or not) that may affect these traveling times, e.g., traffic jams, accidents, road works, or even the weather. In this work, we analyze the VRP with dynamic traveling times. Our work assumes not only that these inputs are dynamic in nature, but also that they are a function of the structure of the emerging routing plan. In other words, these traveling times need to be dynamically re-evaluated as the solution is being constructed. In order to solve this dynamic optimization problem, a learnheuristic-based approach is proposed. Our approach integrates statistical learning techniques within a metaheuristic framework. A number of computational experiments are carried out in order to illustrate our approach and discuss its effectiveness.

scholarly journals Green Vehicle Routing Optimization Based on Carbon Emission and Multiobjective Hybrid Quantum Immune Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-Hong Liu ◽  
Mi-Yuan Shan ◽  
Ren-Long Zhang ◽  
Li-Hong Zhang
Keyword(s):  
Vehicle Routing ◽  
Carbon Emission ◽  
Optimal Allocation ◽  
Cloud Model ◽  
Research Problem ◽  
Optimization Method ◽  
Immune Algorithm ◽  
Practical Significance ◽  
Routing Optimization ◽  
Vehicle Routing Optimization

Green Vehicle Routing Optimization Problem (GVROP) is currently a scientific research problem that takes into account the environmental impact and resource efficiency. Therefore, the optimal allocation of resources and the carbon emission in GVROP are becoming more and more important. In order to improve the delivery efficiency and reduce the cost of distribution requirements through intelligent optimization method, a novel multiobjective hybrid quantum immune algorithm based on cloud model (C-HQIA) is put forward. Simultaneously, the computational results have proved that the C-HQIA is an efficient algorithm for the GVROP. We also found that the parameter optimization of the C-HQIA is related to the types of artificial intelligence algorithms. Consequently, the GVROP and the C-HQIA have important theoretical and practical significance.

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