Two-echelon vehicle routing problem with time windows and simultaneous pickup and delivery

2022 ◽  
Author(s):  
Hang Zhou ◽  
Hu Qin ◽  
Zizhen Zhang ◽  
Jiliu Li
2020 ◽  
Vol 7 (4) ◽  
pp. 261-268
Author(s):  
Lisandra Quintana ◽  
Yalexa Herrera-Mena ◽  
José-Luis Martínez-Flores ◽  
Marcos Coronado ◽  
Gisela Montero ◽  
...  

The growth of industrialization in Mexico has caused an increase in the demand for materials to satisfy the consumption of goods and services of a growing population. Given this scenario, there is a rise of the residual generation with affectations on the ecosystem and population health. Hence, the objective of this research was to design a network for waste vegetable oil collection based on vehicle routing problem with simultaneous pickup and delivery, starting from a distribution centre to 49 restaurants, as the generation sources of waste vegetable oil. The Vehicle Routing Problem Simultaneous Pickup and Delivery with Time Windows was the variant used as a vehicle routing method to solve the problem. The free software VPRPD was the tool used to solve the vehicle routing problem with simultaneous pickup and delivery that allowed to specify time restrictions. This software uses the simulated annealing metaheuristics in its syntax. As a result, it was obtained a total of 8 networks, for a vehicle capacity utilization of 70 percent in the 6 t vehicle and 46 percent in the 8 t vehicle.


2021 ◽  
Author(s):  
Hang Zhou ◽  
Hu Qin ◽  
Zizhen Zhang ◽  
Jiliu Li

Abstract In this paper, we propose a tabu search algorithm for the two-echelon vehicle routing problem with time windows and simultaneous pickup and delivery (2E-VRPTWSPD), which is a new variant of the two-echelon vehicle routing problem (2E-VRP) by considering the time windows constraints and simultaneous pickup and delivery. In 2EVRPTWSPD, the pickup and delivery activities are performed simultaneously by the same vehicles through the depot to satellites in the first echelon and satellites to customers in the second echelon, where each customer has a specified time window. To solve this problem, firstly, we formulate the problem with a mathematical model. Then, we implement a variable neighborhood tabu search algorithm with the proposed solution representation of dummy satellites to solve large-scale instances. Dummy satellites time windows are used in our algorithm to speed up the algorithm. Finally, we generate two instance sets based on the existing 2E-VRP and 2E-VRPTW benchmark sets and conduct additional experiments to analyze the performance of our algorithm.


2015 ◽  
Vol 738-739 ◽  
pp. 361-365 ◽  
Author(s):  
Yan Guang Cai ◽  
Ya Lian Tang ◽  
Qi Jiang Yang

Multi-depot heterogeneous vehicle routing problem with simultaneous pickup and delivery and time windows (MDHVRPSPDTW) is an extension of vehicle routing problem (VRP), MDHVRPSPDTW mathematical model was established. The improved genetic algorithm (IGA) is proposed for solving the model. Firstly, MDHVRPSPDTW is transferred into different groups by the seed customer selecting method and scanning algorithm (SA).Secondly, IGA based on elite selection and inversion operator is used to solve the model, and then cutting merge strategy based on greedy thought and three kinds of neighborhood search methods is applied to optimize the feasible solutions further. Finally, 3-opt local search is applied to adjust the solution. The proposed IGA has been test on a random new numerical example.The computational results show that IGA is superior to branch and bound algorithm (BBD) by Lingo 9.0 in terms of optimum speed and solution quality, and the model and the proposed approach is effective and feasible.


2019 ◽  
Vol 119 (9) ◽  
pp. 2055-2071 ◽  
Author(s):  
Gaoyuan Qin ◽  
Fengming Tao ◽  
Lixia Li ◽  
Zhenyu Chen

Purpose In order to reduce logistics transportation costs and respond to low-carbon economy, the purpose of this paper is to study the more practical and common simultaneous pickup and delivery vehicle routing problem, which considers the carbon tax policy. A low-carbon simultaneous pickup and delivery vehicle routing problem model is constructed with the minimum total costs as the objective function. Design/methodology/approach This study develops a mathematical optimization model with the minimum total costs, including the carbon emissions costs as the objective function. An adaptive genetic hill-climbing algorithm is designed to solve the model. Findings First, the effectiveness of the algorithm is verified by numerical experiments. Second, the research results prove that carbon tax mechanism can effectively reduce carbon emissions within effective carbon tax interval. Finally, the research results also show that, under the carbon tax mechanism, the effect of vehicle speed on total costs will become more obvious with the increase of carbon tax. Research limitations/implications This paper only considers the weight of the cargo, but it does not consider the volume of the cargo. Originality/value Few studies focus on environmental issues in the simultaneous pickup and delivery problem. Thus, this paper constructs a green path optimization model, combining the carbon tax mechanism for the problem. This paper further analyzes the impact of carbon tax value on total costs and carbon emission; at the same time, the effect of vehicle speed on total cost is also analyzed.


Author(s):  
Robin Scanlon ◽  
Qing Wang ◽  
Jie Wang

Reverse logistics is an area that has come under increased scrutiny in recent years as legislators and companies try to increase the amount of goods that businesses reuse and recycle. The vehicle routing problem with simultaneous pickup and delivery arises when firms want to reduce handling costs by dealing with deliveries and returns in one operation. This is a complex problem for planners who aim to minimise the vehicle route length as the vehicle load rises and falls during a tour of facilities. This paper investigates the use of Ant Colony Optimisation to find solutions to this problem. An algorithm combining elements of three different studies is proposed. The algorithm finds results within 0.2% of the best known results and performs well for half of the benchmark problems, but needs further work to reach the same level on the other half. It is found that the proposed changes can have up to a 3.1% improvement in results when compared to previous methods run on this algorithm.


Sign in / Sign up

Export Citation Format

Share Document