Routing with time-windows for multiple environmental vehicle types

doi:10.1016/j.cie.2015.02.001

Computers & Industrial Engineering

Volume 89, November 2015, Pages 150-161

https://doi.org/10.1016/j.cie.2015.02.001 Get rights and content

Highlights

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An environmental routing problem with multiple vehicle types is proposed.
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A hybrid genetic algorithm is developed.
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Vehicle speed and load capacity affect cost, customer satisfaction and pollution.

Abstract

With the goal of reducing cost, improving customer satisfaction and controlling the environmental pollution, a environmental routing optimization problem with time windows and multiple vehicle types is proposed by considering the concept of low-carbon logistics. A multi-objective vehicle routing problem (VRP) model with soft time-windows for multiple environmental vehicle types is presented, and a hybrid genetic algorithm (GA) is designed. Based on the experiments, the effectiveness of the algorithm is examined. With Pareto analysis, the relationship among the three objectives (distribution cost, customer satisfaction and environmental pollution) is examined. Sensitivity analysis is conducted to identify the influence of different type vehicle on the environmental performance. The results shows that the vehicle speed has strong correlation with the operation cost and environmental pollution, while the load capacity affects the operation cost, customer satisfaction and environmental pollution.

Introduction

With the development of social economy, the progress of science and technology, and the expansion of production scale, energy consumption, waste pollution and CO₂ emissions and many other environmental problem arose. In such a period of sharp contradictions between economic growth and environment deterioration, saving energy and controlling energy consumption become global issues. In 2010, the tertiary industry in China consumes approximately 16% of the whole society energy (Xiang, Xu, & Sha, 2013). Particularly, the modern logistics industry (transportation and warehousing activities) has consumed almost 80% of the energy of the tertiary industry. To improve the energy utilization rate and alleviate the pressure of energy requirement, researchers focus on the logistics optimization to control the environmental pollution. Cooper, Browne, and Peters (1994) proposed the concept of “Green Logistics”, and put forward the framework of green logistics by improving vehicle design, increasing highway tolls and encouraging the transport combination. With the prevailing concept of low-carbon logistics, more and more attentions have been paid to the study on the low-carbon vehicle routing problem (VRP) (Ćirović et al., 2014, Erdoğan and Miller-Hooks, 2012, Zhang et al., 2014). This problem is a heterogeneous fleet vehicle routing problem, which is a NP-hard problem. We proposed an environmental vehicle routing model to study the influence of low-carbon vehicles on the green logistics.

Section snippets

Literature review

Nowadays, many models and solution algorithms have been proposed quite extensively for the vehicle scheduling problem with one type vehicles in literature. Meanwhile, multi-vehicle scheduling problem and its variations are paid many attentions, as well as the vehicle routing problem (VRP) with time windows. Karunoa and Nagamochib (2003) presented a nearly linear time 2-approximation algorithms for the multi-vehicle scheduling problem considering the release and handling times. Tütüncü (2010)

Problem definitions

The heterogeneous fleet vehicle routing problem (HFVRP) is often encountered in the logistics operation management. The general objective of HFVRP is to decide the routes, load capability, speed, and vehicle type of the fleet to satisfy different customers’ demands with a proper cost. Moreover, we also consider the environmental performance of the vehicles used for the logistics. Thus, we study two key points of HFVRP: one is to decide the type of the vehicles to improve the environmental

Models

Without loss of generality, this paper considers the logistics center and demand nodes are given in the vehicle routing problem with multiple vehicle types. Moreover, the quality of the goods is not changing in the delivery, and the working time of drivers, traffic situation and weather impacts are assumed to be ignored.

According to the problem description, the decision variable is defined as follows:

(1)
$x_{t, k, i, j} \in {0, 1}$ , for all $t \in V^{T}, k \in V^{N}, i \in V$ and $j \in V$ , specifies that if customer j is served followed

The hybrids genetic algorithm

GA was proposed in 1975 by (Holland, 1975), an American professor at the University of Michigan, based on the phenomenon of biological genetic mechanism of biological evolution (natural selection, crossover and mutation, etc.). GA simulates the evolution process, searches and evolves through the computing, finally obtains optimal solutions (Gen and Cheng, 1997, Holland, 1975, Zbigniew, 1996). Nowadays, numerous meta-heuristic algorithms have been developed, such as GA, ant colony systems and

Experiments

The proposed HGA is implemented by C#.NET 2010 on Win 7 platform. Experimental studies are carried out on a personal computer with two Intel (R) Core (TM) i5-2450M CPU @ 2.50 GHz processors and 4 GB RAM.

The data sets used in our experiments are named in the form of “CxVy” strings, where “x” stands for the number of customers and “y” stands for the number of vehicle types. The locations of customers are denoted as $(X, Y)$ , where $X, Y \sim U (0, 400)$ ; the depot is located at $(150, 150)$ . The generated

Conclusions

Faced with the severe situations of crowed traffic condition, ever-increasing energy consumption and carbon emissions, the current logistics service and efficiency of resource utilization are seriously affected. Based on the research of VRP, the environmental VRP with multiple vehicle types is proposed and studied. With the goal of reducing cost, improving customer satisfaction and reducing the environmental pollution, a new VRP model with soft time windows is established, which reflects the

Acknowledgements

This study is partially supported by Research supported by the National Nature Science of China (71101088, 71171129, 71390521, 71471109), the Science Foundation of Ministry of Education of China and Shanghai (20113121120002, 14YZ100, 20123121110004, 13SG48), the ministry of transport of the People’s Republic of China (2015329810260), and the Science and Technology Commission of Shanghai (12510501600).

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View full text

Routing with time-windows for multiple environmental vehicle types

Highlights

Abstract

Introduction

Section snippets

Literature review

Problem definitions

Models

The hybrids genetic algorithm

Experiments

Conclusions

Acknowledgements

Computers & Industrial Engineering

Computers & Operations Research

Computers & Operations Research

Procedia Social and Behavioral Sciences

Expert Systems with Applications

Transportation Research Part D

Transportation Research Part E: Logistics and Transportation Review

International Journal of Production Economics

Expert Systems with Applications

Transportation Research Part C: Emerging Technologies

European Journal of Operational Research

International Journal of Production Economics

Atmospheric Environment

Procedia – Social and Behavioral Sciences