scholarly journals Spatiotemporal-Dependent Vehicle Routing Problem Considering Carbon Emissions

2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
Ziqi Liu ◽  
Yeping Chen ◽  
Jian Li ◽  
Dongqing Zhang
Keyword(s):  
Vehicle Routing ◽  
Carbon Emissions ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
Travel Speed ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Adaptive Genetic Algorithm ◽  
Local Optimum ◽  
Routing Problem

Aiming to improve the timeliness of logistics distribution and render the optimized route scheme effective under the real traffic network, we study the green vehicle routing problem with dynamic travel speed from both dimensions of time and space. A discrete formulation is proposed to calculate the travel time based on periods and arcs, which allows a vehicle to travel across an arc in multiple periods. Then, we establish a mixed-integer nonlinear programming model with minimum distribution costs including transportation costs, carbon emissions costs, and penalty costs on earliness and tardiness. A hybrid adaptive genetic algorithm with elite neighborhood search is developed to solve the problem. In the algorithm, a neighborhood search operator is employed to optimize elite individuals so that the algorithm can stimulate the intensification and avoid falling into a local optimum. Experimental instances are constructed based on benchmark instances of vehicle routing problem. The numerical results indicate that the proposed algorithm is rather effective in global convergence. Compared with the routing schemes in which travel speed merely varies with time periods or locations, the vehicle route optimized on spatiotemporal-varying speed outperforms them in terms of carbon emissions and timeliness. The research can provide a scientific and reasonable method for logistics enterprises to plan the vehicle schedule focusing on spatiotemporal-dependent speed of the road network.

scholarly journals Vehicle Routing Problem with Transshipment: Mathematical Model and Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Thanapat Leelertkij ◽  
Parthana Parthanadee ◽  
Jirachai Buddhakulsomsiri
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Retail Stores ◽  
Routing Problem ◽  
Hybrid Heuristics ◽  
New Variant ◽  
Problem Instances

This paper presents a new variant of vehicle routing problem with paired transshipment demands (VRPT) between retail stores (customers) in addition to the regular demand from depot to retail stores. The problem originates in a real distribution network of high-end retail department stores in Thailand. Transshipment demands arise for one-order-per-season expensive items, whose inventories at the depot may become shortage after the middle of a season, while they remain available at some retail stores. A transshipment demand is a request for items that need to be picked up from a specific store that has the items and delivered to the store that requests the items. The objective of solving the VRPT is to find delivery routes that can satisfy both regular demands and transshipment demands in the same routes without incurring too much additional transportation distance. A mixed integer linear programming model is formulated to represent the VRPT. Six small problem instances are used to test the model. A hybrid threshold accepting and neighborhood search heuristic is also developed to solve large problem instances of VRPT. The heuristic is further extended to include a forbidden list of transshipment demands that should not be included in the same routes. The purpose is to prevent incurring too much additional distance from satisfying transshipment demands. With the forbidden list, the problem becomes vehicle routing problem with optional transshipment demands (VRPOT). Computational testing shows promising results that indicate effectiveness of the proposed hybrid heuristics as well as the forbidden list.

scholarly journals A Two-Echelon Electric Vehicle Routing Problem with Time Windows and Battery Swapping Stations

2021 ◽  
Vol 11 (22) ◽  
pp. 10779
Author(s):  
Dan Wang ◽  
Hong Zhou
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
High Efficiency ◽  
Programming Model ◽  
Time Windows ◽  
Transportation Cost ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Fixed Cost ◽  
Routing Problem

Driven by the new laws and regulations concerning the emission of greenhouse gases, it is becoming more and more popular for enterprises to adopt cleaner energy. This research proposes a novel two-echelon vehicle routing problem consisting of mixed vehicles considering battery swapping stations, which includes one depot, multiple satellites with unilateral time windows, and customers with given demands. The fossil fuel-based internal combustion vehicles are employed in the first echelon, while the electric vehicles are used in the second echelon. A mixed integer programming model for this proposed problem is established in which the total cost, including transportation cost, handling cost, fixed cost of two kinds of vehicles, and recharging cost, is minimized. Moreover, based on the variable neighborhood search, a metaheuristic procedure is developed to solve the problem. To validate its effectiveness, extensive numerical experiments are conducted over the randomly generated instances of different sizes. The computational results show that the proposed metaheuristic can produce a good logistics scheme with high efficiency.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

scholarly journals Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bochen Wang ◽  
Qiyuan Qian ◽  
Zheyi Tan ◽  
Peng Zhang ◽  
Aizhi Wu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Programming Model ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

scholarly journals Electric Vehicle Routing Problem with Battery Swapping Considering Energy Consumption and Carbon Emissions

2020 ◽  
Vol 12 (24) ◽  
pp. 10537
Author(s):  
Jin Li ◽  
Feng Wang ◽  
Yu He
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Carbon Emissions ◽  
Vehicle Routing Problem ◽  
Hill Climbing ◽  
Neighborhood Search ◽  
Battery Life ◽  
Routing Problem ◽  
Crossover And Mutation

In this paper, we study an electric vehicle routing problem while considering the constraints on battery life and battery swapping stations. We first introduce a comprehensive model consisting of speed, load and distance to measure the energy consumption and carbon emissions of electric vehicles. Second, we propose a mixed integer programming model to minimize the total costs related to electric vehicle energy consumption and travel time. To solve this model efficiently, we develop an adaptive genetic algorithm based on hill climbing optimization and neighborhood search. The crossover and mutation probabilities are designed to adaptively adjust with the change of population fitness. The hill climbing search is used to enhance the local search ability of the algorithm. In order to satisfy the constraints of battery life and battery swapping stations, the neighborhood search strategy is applied to obtain the final optimal feasible solution. Finally, we conduct numerical experiments to test the performance of the algorithm. Computational results illustrate that a routing arrangement that accounts for power consumption and travel time can reduce carbon emissions and total logistics delivery costs. Moreover, we demonstrate the effect of adaptive crossover and mutation probabilities on the optimal solution.

scholarly journals Time-Dependent Electric Vehicle Routing Problem with Time Windows and Path Flexibility

2020 ◽  
Vol 2020 ◽  
pp. 1-19
Author(s):  
Li Wang ◽  
Shuai Gao ◽  
Kai Wang ◽  
Tong Li ◽  
Lin Li ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Optimal Path ◽  
Travel Speed ◽  
Path Selection ◽  
Time Dependent ◽  
Mixed Integer ◽  
Routing Problem

With energy and environmental issues becoming increasingly prominent, electric vehicles (EVs) have become the important transportation means in the logistics distribution. In the real-world urban road network, there often exist multiple paths between any two locations (depot, customer, and charging station) since the time-dependent travel times. That is, the travel speed of an EV on each path may be different during different time periods, and thus, this paper explicitly considers path selection between two locations in the time-dependent electric vehicle routing problem with time windows, denoted as path flexibility. Therefore, the integrated decision-making should include not only the routing plan but also the path selection, and the interested problem of this paper is a time-dependent electric vehicle routing problem with time windows and path flexibility (TDEVRP-PF). In order to determine the optimal path between any two locations, an optimization model is established with the goal of minimizing the distance and the battery energy consumption associated with travel speed and cargo load. On the basis of the optimal path model, a 0-1 mixed-integer programming model is then formulated to minimize the total travel distance. Hereinafter, an improved version of the variable neighborhood search (VNS) algorithm is utilized to solve the proposed models, in which multithreading technique is adopted to improve the solution efficiency significantly. Ultimately, several numerical experiments are carried out to test the performance of VNS with a view to the conclusion that the improved VNS is effective in finding high-quality distribution schemes consisted of the distribution routes, traveling paths, and charging plans, which are of practical significance to select and arrange EVs for logistics enterprises.

scholarly journals Collaborative Vehicle Routing Problem in the Urban Ring Logistics Network under the COVID-19 Epidemic

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wenjia Zheng ◽  
Zhongyu Wang ◽  
Liucheng Sun
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Multicommodity Flow ◽  
Integer Programming Problem ◽  
Distribution Centers ◽  
Routing Problem ◽  
Logistics Network ◽  
The City

This paper explored the problem of collaborative vehicle routing in the urban ring logistics network (Co-VRP-URLN) during the COVID-19 epidemic. According to the characteristics of urban distribution and the restriction of traffic all over China during this period, this study mainly considers a common distribution mode of order exchange through the outer ring of the city and then solves the vehicle routing problem of distribution, which belongs to a special multidepot vehicle routing problem with time windows. According to the definition of the problem, the corresponding mixed-integer programming problem of multicommodity flow is established, and the variable neighborhood search algorithm is designed in detail to solve it. The effectiveness of the algorithm is verified by a standard example, and the benefits of joint distribution are revealed through the improved standard example. At last, the influence of different distribution centers is compared. The results show that this model can significantly improve the distribution efficiency within the city under the restriction of traffic.

scholarly journals An Adaptive Large Neighborhood Search for the Larger-Scale Instances of Green Vehicle Routing Problem with Time Windows

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Zixuan Yu ◽  
Ping Zhang ◽  
Yang Yu ◽  
Wei Sun ◽  
Min Huang
Keyword(s):  
Vehicle Routing ◽  
Carbon Emissions ◽  
Vehicle Routing Problem ◽  
Computational Time ◽  
Neighborhood Search ◽  
Large Neighborhood Search ◽  
Adaptive Large Neighborhood Search ◽  
Routing Problem ◽  
Large Neighborhood ◽  
Insertion Method

Due to huge amount of greenhouse gases emission (such as CO2), freight has been adversely affecting the global environment in facilitating the global economy. Therefore, green vehicle routing problem (GVRP), aiming to minimize the total carbon emissions in the transportation, has become a hot issue. In this paper, an adaptive large neighborhood search (ALNS) algorithm is proposed to solve large-scale instances of GVRP. The core of ALNS algorithm is destroy operators and repair operators. In the destroy operators, a new removal heuristic applying to the characteristics of GVRP is proposed. The heuristic can quickly remove customers who bring a large amount of carbon emissions with pertinence, and these customers may be arranged more properly in future repair operators. In the repair operators, a fast insertion method is developed. In the fast insertion method, the feasibility of a new route is judged by checking the constraints of partial customers after the inserted customer, instead of checking the constraints of all customers. Thus, the computational time of the ALNS algorithm is greatly saved. Computational experiments were performed on Solomon benchmark with 100 customers and Homberger benchmark instances with up to 1000 customers. Given the same computational time, the proposed ALNS improves the average accuracy by 8.49% compared with the classic ALNS. In the optimal situation, the improvement can achieve 33.61%.

scholarly journals A rich vehicle routing problem arising in the replenishment of automated teller machines

2018 ◽  
Vol 8 (2) ◽  
pp. 276-287
Author(s):  
Çağrı Koç ◽  
Mehmet Erbaş ◽  
Eren Ozceylan
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Practical Problem ◽  
Programming Model ◽  
Optimization Method ◽  
Mixed Integer ◽  
Routing Problem ◽  
Automated Teller Machines ◽  
Trade Offs ◽  
Multiple Depots

This paper introduces, models, and solves a rich vehicle routing problem (VRP) motivated by the case study of replenishment of automated teller machines (ATMs) in Turkey. In this practical problem, commodities can be taken from the depot, as well as from the branches to efficiently manage the inventory shortages at ATMs. This rich VRP variant concerns with the joint multiple depots, pickup and delivery, multi-trip, and homogeneous fixed vehicle fleet. We first mathematically formulate the problem as a mixed-integer linear programming model. We then apply a Geographic Information System (GIS)-based solution method, which uses a tabu search heuristic optimization method, to a real dataset of one of the major bank. Our numerical results show that we are able to obtain solutions within reasonable solution time for this new and challenging practical problem. The paper presents computational and managerial results by analyzing the trade-offs between various constraints.

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