Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost

Wang, Yong; Assogba, Kevin; Fan, Jintai; Xu, Maozeng; Liu, Yong; Wang, Haizhong

doi:10.1016/j.jclepro.2019.05.344

Cited by 110 publications

(64 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The work of Kaabachi et al [9], Jabir et al [10], Li et al [11] and Wang et al [12] also focuses on MDGVRP; however, their MDGVRP model is developed from the Pollution Routing Problem (PRP) and does not consider the tank capacity of vehicles. One of the aims of this paper is to propose the MDGVRP model with a limited tank capacity of AFVs.…”

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions

et al. 2020

View full text Add to dashboard Cite

A Multi-Depot Green Vehicle Routing Problem (MDGVRP) is considered in this paper. In MDGVRP, Alternative Fuel-powered Vehicles (AFVs) start from different depots, serve customers, and, at the end, return to the original depots. The limited fuel tank capacity of AFVs forces them to visit Alternative Fuel Stations (AFS) for refueling. The objective is to minimize the total carbon emissions. A Two-stage Ant Colony System (TSACS) is proposed to find a feasible and acceptable solution for this NP-hard (Non-deterministic polynomial-time) optimization problem. The distinct characteristic of the proposed TSACS is the use of two distinct types of ants for two different purposes. The first type of ant is used to assign customers to depots, while the second type of ant is used to find the routes. The solution for the MDGVRP is useful and beneficial for companies that employ AFVs to deal with the various inconveniences brought by the limited number of AFSs. The numerical experiments confirm the effectiveness of the proposed algorithms in this research.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, the MDGVRP problem has been considered by some researchers [9][10][11][12]. However, compared with their models, our MDGVRP model considers different constraints and objective functions.…”

Section: Introductionmentioning

confidence: 99%

Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions

et al. 2020

View full text Add to dashboard Cite

show abstract

“…A progressive hedging algorithm was applied by Hu et al [6] to solve a multistage stochastic programming model that considered uncertain and dynamic road capacity. Wang et al [18] investigated a time-dependent speed green vehicle routing problem. In these studies, the probability or distribution of a specific scenario or an uncertain parameter must be provided which renders these methods less popular than dynamic optimization approaches and robust optimization approaches.…”

Section: Related Workmentioning

confidence: 99%

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Zhang¹,

Liu²

2021

IEEE Access

View full text Add to dashboard Cite

In the immediate aftermath of large-scale disasters, emergency logistics services play important roles in saving lives and reducing losses. Efficient relief logistics scheduling depends on the accurate transport time information for available routes. However, this information cannot be obtained precisely until a vehicle uses the road. Considering the correlation between information acquisition and logistics operations, this paper focuses on a multiperiod online decision-making problem to simulate the information acquiring process. This problem can be referenced for emergency resource scheduling scenarios in which previous decisions impact knowledge for future logistics plans. A multi-trip cumulative capacitated vehicle routing problem with uncertain transportation time is investigated as the basic model. The tradeoff between transportation efficiency and the unknown transport time discovery rate is considered in a multiobjective evolutionary algorithm (MOEA). A memetic algorithm (MA) and a robust optimization (RO) -MA for single-period postdisaster emergency logistics are also proposed to solve the problem for comparison. In these algorithms, evolutionary operators that benefit solution fixing and variation are proposed. In the experiments, a realworld instance is employed. A simulative experimental environment is established. Dynamic information gained within the process of logistics scheduling is highlighted via multi-period online optimization. Different scenarios corresponding to estimates in emergency situations are provided to validate the performance of the algorithms. The experimental results show that the hybrid strategy, MOEA+MA, can obtain the best result in more than half of the considered cases which demonstrates the necessary balance between obtaining information and transportation efficiency.INDEX TERMS Emergency service, uncertain environment, humanitarian logistics, Pareto optimization, robustness, multiphase scheduling.

show abstract

“…Bortolini et al [15] proposed a three-objective distribution planner to solve the tactical optimization problem of fresh food distribution network, involving the operation cost, carbon footprint and delivery time objectives into consideration. Finally, Wang [16] et al introduced a green vehicle routing problem with multiple warehouses. It also proposes a dual-target model to minimize total carbon emissions and operating costs, and implement staged fines for advancement and delay to reduce waiting time and increase customer satisfaction.…”

Section: Related Work a Multi-objective Optimizationmentioning

confidence: 99%

Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

Zhao

Gui

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In order to improve the performance and change the current situation of the cost minimization model widely used in the cold chain logistics distribution process, a multi-objective optimization model based on cost, carbon emissions and customer satisfaction is proposed. Considering the characteristic of this proposed optimization model, we design an improved ant colony algorithm with a multi-objective heuristic function to solve it, termed as ACOMO. Experimental results show that the proposed ACOMO can effectively solve the vehicle routing problem of the multi-objective optimization model, and outperforms the classic ant colony algorithms, resulting in more Pareto optimal solutions. It offers an environmentally friendly distribution solution for the problem. Specifically, the distribution path obtained by the improved ant colony algorithm manages to achieve the above multiple goals, including reduction of distribution costs and carbon emissions, and improvement of customer satisfaction. In addition, compared with a single-target model that only provides one single distribution route to cost minimization, multi-objective optimization can provide a variety of distribution route options for logistics companies in practice. Finally, through the sensitivity analysis of temperature changes and cargo damage coefficients, the proposed system successfully provides reference for the optimization of the path of cold chain logistics enterprises, and promotes logistics enterprises to effectively arrange their work and to be more socially responsible. INDEX TERMS Cold chain logistics, path optimization, multi-objective optimization, carbon constraints, customer satisfaction.

show abstract

Multi-depot green vehicle routing problem with shared transportation resource: Integration of time-dependent speed and piecewise penalty cost

Cited by 110 publications

References 35 publications

Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions

Multi-Depot Green Vehicle Routing Problem to Minimize Carbon Emissions

Emergency Logistics Scheduling Under Uncertain Transportation Time Using Online Optimization Methods

Cold Chain Logistics Path Optimization via Improved Multi-Objective Ant Colony Algorithm

Contact Info

Product

Resources

About