Green Vehicle Routing Research Articles

A two-phase algorithm for the dynamic time-dependent green vehicle routing problem in decoration waste collection

Transportation activities associated with construction waste generate substantial carbon emissions, an issue that is attracting increasing environmental concern. To raise construction waste transportation efficiency and reduce carbon emission, we address the dynamic time-dependent green vehicle routing problem for decoration waste collection (DTDGVRP-DWC). In the existing literature, many deterministic approaches to the dynamic vehicle routing problem are myopic, as they only react to already arrived requests. In this paper, we propose a stochastic sampling method to tackle with uncertain customer request in the real world. The DTDGVRP-DWC is formulated as an 0-1 programming. In the new model, we consider urban traffic congestion and variable speeds over time, and factors that significantly influence both carbon emissions and fuel costs. To quickly solve the complicated optimization problem, we develop a two-phase algorithm. In the first phase, we embed a competitive simulated annealing algorithm to determine visitation schedules for anticipated and early-request customers. In the second phase, we propose an event-trigger mechanism to decompose the dynamic problem into a series of static sub-problems, and an effective heuristic to solve each sub-problem. Computational results show that as long as the prediction accuracy exceeds a threshold, the forecast routing method consistently performs better than reactive routing. A real-world case demonstrates that an early commitment waiting time strategy benefits timely service requirements, whereas a late or hybrid waiting time strategy takes overall efficiency and customer satisfaction into account.

Optimization research on multi-trip distribution of reverse logistics terminal for automobile scrap parts under the background of sustainable development strategy

To effectively solve the reverse logistics distribution problem caused by the increasing number of scrapped parts in the automotive market, this study constructs a multi-trip green vehicle routing problem model with time windows by comprehensively considering the coordination between carbon dioxide emissions and cost efficiency. A hybrid adaptive genetic algorithm is proposed to solve this problem, featuring innovative improvements in the nearest neighbor rule based on minimum cost, adaptive strategies, bin packing algorithm based on the transfer-of-state equation, and large-scale neighborhood search. Additionally, to efficiently obtain location data for supplier factory sites in the distribution network, a coordinate extraction method based on image recognition technology is proposed. Finally, the scientific validity of this study is verified based on the actual case data, and the robust optimization ability of the algorithm is verified by numerical calculations of different examples. This research not only enriches the study of green vehicle routing problems but also provides valuable insights for the industry to achieve cost reduction, efficiency enhancement, and sustainable development in reverse logistics.

A Sectoral Application for Green Vehicle Routing Problem Optimization with Capacity Constrained and Heterogeneous Fleet

A Sectoral Application for Green Vehicle Routing Problem Optimization with Capacity Constrained and Heterogeneous Fleet

Self-adaptive metaheuristic-based emissions reduction in a collaborative vehicle routing problem

Global climate change-related initiatives such as the 2015 Paris Agreement have highlighted the necessity of sustainable transportation. Nevertheless, the rapid growth of e-commerce has notably escalated vehicle kilometres travelled (VKT) and CO2 emissions within cities, posing a direct challenge to sustainability initiatives. To address these challenges, this study formulates a collaborative multi-depot green vehicle routing problem. This model utilises micro-consolidation centres (MCCs) as shared hubs alongside a microscopic approach linking emission rates to vehicle and route characteristics, in order to assess MCCs' effectiveness in reducing CO2 emissions. Introduced here is an innovative self-adaptive metaheuristic algorithm hybridising intelligent water drops and simulated annealing. This methodology differs from established approaches by incorporating a feedback control system that actively monitors the algorithm's performance and convergence towards the global minimum solution. Through continuous adjustments to algorithm parameters via a feedback loop, this methodology strikes a balance between exploitation and exploration. The algorithm is tested in a context-specific approach, first applying it to the Cordeau benchmark and comparing it with previous state-of-the-arts, followed by a case study comparing the collaborative network to an independent one. This approach achieves 43 % and 25 % reductions in VKT and emissions, respectively, enhancing urban logistics networks' efficiency and sustainability.

A lightweight genetic algorithm with variable neighborhood search for multi-depot vehicle routing problem with time windows

A multi-depot green vehicle routing problem with time windows considering customer satisfaction (MDGVRPTW-CS) was constructed. To optimize it, we proposed a lightweight genetic algorithm with variable neighborhood search (LGAVNS), which takes the genetic algorithm as the upper algorithm and the variable neighborhood algorithm as the neighborhood search algorithm. In our constructed algorithm, the neighborhood search operators were applied to the optimal gene instead of all genes. Additionally, an enhanced crossover operator was developed to facilitate the effective transmission of gene information. Finally, we extended and optimized the parameters of the algorithm. Experimental results demonstrated that our approach exhibited significant advantages in terms of optimization effectiveness and convergence speed compared to six state-of-the-art algorithms, particularly when addressing MDGVRPTW-CS in large-scale scenarios. Moreover, the lightweight nature of our algorithm can be utilized for optimizing both the number and location of depots while further exploring the optimization potential of MDGVRPTW-CS.

Optimizing sustainable multimodal distribution networks in the context of carbon pricing, with a case study in the Thai sugar industry

Transportation is a major cause of energy consumption and emissions which can be reduced by optimizing routings and using alternative modes of transport. This paper relates to the strategic design of multimodal transportation networks. It presents a general model of green vehicle routing problems that supports strategic decision-making by identifying optimal solutions and provides data on costs and emissions. Three general linear programming models were developed that optimize multimodal distribution networks that could be applied in many industries. Model I evaluates carbon emissions; model II assesses carbon emissions and capacity constraints; and model III establishes total costs including transportation, handling, storage, fuel and carbon costs.Thailand is the third largest world sugar exporter in the world and is piloting carbon pricing, which will affect energy intensive industries, including the sugar industry. The models are applied using data obtained from a collaborating company. The research contributed to practice by informing managerial decisions relating to the export of sugar from the factory. This included evaluating the possible use of a dry port with rail connections, which could reduce transportation and carbon costs by 54.3 % and facilitate the building of another factory to increase exports.

An integrated Bi-objective green vehicle routing and partial disassembly line problem for electronic waste: an industrial case study

ABSTRACT Today, serious environmental problems arise with overconsumption. The most effective way of protecting the environment is recycling. To obtain maximum benefit from the recycling activities depends on the effective and robust design of the disassembly lines. In addition, the distribution plan of products to be recycled is as important as the disassembly process. Therefore, this study includes the integrated optimization of the partial disassembly line balancing problem and the multi-objective green vehicle routing problem for the first time. The integrated problem is formulated as a Multi-Objective Mixed Integer Linear Programming. The model’s objectives consist of minimization of total CO2 emission and cost. The Pareto optimal solution set is found by solving this problem with the Augmented ℇ-Constraint Method. The model respectively determines (i) the total number of opened workstations in the disassembly center, (ii) optimal task assignments to workstations in disassembly centers and non-disassembled tasks, (iii) total workstation times and idle times of each workstation, (iv) optimal distribution routes for all vehicles. This study examines a case study in Turkey. To determine the best Pareto optimal solution for homogeneous and heterogeneous fleets, the integrated multi-criteria decision-making method consisting of EWM (Entropy Weight Method) and COPRAS (Complex Proportional Assessment) is used.

Multi-objective Optimization for Green Delivery Routing Problems with Flexible Time Windows

ABSTRACT This paper presents a model and heuristic solution algorithms for the Green Vehicle Routing Problem with Flexible Time Windows. A scenario of new vehicle routing is analyzed in which customers are asked to provide alternative time windows to offer flexibility to help route planners find more fuel-efficient routes (“green delivery”). Customers can rank their preferred time windows as first, second, and third. The optimization model aims to reduce tour costs, promote electromobility over fossil fuels, such as diesel, and meet customer preferences when possible and affordable. The study incorporates a multi-objective optimization model with three objectives, which are overall cost, use of fossil fuel, and customer satisfaction. For the new problem, a set of realistic benchmark problems is created and four mainstream solvers are applied for the Pareto front approximation: NSGA-II, NSGA-III, MOEA/D, and SMS-EMOA. These algorithms are compared in terms of their effectiveness in achieving the objectives of minimizing travel costs, promoting electromobility, and meeting customer preferences. The study uses five different problems of single-vehicle route planning. Two major findings are that the selection of the metaheuristic can make a big difference in terms of algorithm performance. The resulting 3-D Pareto fronts reveal the nature of this new class of problems: Interestingly, in the new model with flexible time windows, most users can still be delivered in their most preferred time windows with only small concessions to the other objectives. However, using only one time window per user can lead to an increasingly drastic cost and fossil fuel consumption.

A variable neighborhood search for the green vehicle routing problem with two-dimensional loading constraints and split delivery

We address the Green Vehicle Routing Problem with Two-Dimensional Loading Constraints and Split Delivery (G2L-SDVRP), which extends the split delivery vehicle routing problem to include customer demands represented by two-dimensional, rectangular items. We aim to minimize carbon dioxide (CO2) emissions instead of travel distance, a critical issue in contemporary logistics activities. The CO2 emission rate is proportional to fuel consumption and measured in terms of the vehicle’s total weight and traveled distance. We propose the first metaheuristic for the G2L-SDVRP, based on a variable neighborhood search approach that designs effective routes and guarantees the feasibility of loading constraints using various strategies, such as lower bound procedures, the open space heuristic, and a constraint programming model. We evaluate the performance of our approach through computational experiments using benchmark and newly created instances. The results indicate that the proposed approach is effective. It achieves improved solutions for 21 out of 60 instances in relatively short computing times when compared to existing methods for the G2L-SDVRP. Furthermore, our approach is competitive on benchmark instances of a related variant, namely the Capacitated Vehicle Routing Problem with Two-Dimensional Loading Constraints, improving the best-known solutions for 50 out of 180 instances.

A Deep Reinforcement Learning-Based Adaptive Search for Solving Time-Dependent Green Vehicle Routing Problem

A Deep Reinforcement Learning-Based Adaptive Search for Solving Time-Dependent Green Vehicle Routing Problem

Cooperative trucks and drones for rural last-mile delivery with steep roads

The cooperative delivery of trucks and drones promises considerable advantages in delivery efficiency and environmental friendliness over pure fossil fuel fleets. As the prosperity of rural B2C e-commerce grows, this study intends to explore the prospect of this cooperation mode for rural last-mile delivery by developing a green vehicle routing problem with drones that considers the presence of steep roads (GVRPD-SR). Realistic energy consumption calculations for trucks and drones that both consider the impacts of general factors and steep roads are incorporated into the GVRPD-SR model, and the objective is to minimize the total energy consumption. To solve the proposed model, an improved adaptive large neighborhood search (IALNS) algorithm is introduced, which incorporates several novel operators designed based on the characteristics of the problem. The effectiveness of the IALNS algorithm and the feasibility of the GVRPD-SR are verified through extensive computational experiments. Furthermore, a detailed sensitivity analysis is conducted on several critical parameters to derive managerial insights.

Evolution of Green Vehicle Routing Problem: A Bibliometric and Visualized Review

The Green Vehicle Routing Problem (GVRP) has garnered considerable attention as a prominent subject within the field of green logistics. Scholars, organizations, and legislators have dedicated substantial efforts to comprehensively investigate and implement solutions for this problem. To thoroughly understand the research status in this field, this paper provides a bibliometric review of all-round GVRPs between 2000 and 2021 based on 1230 publications filtered from the Web of Science Core Collection with the help of CiteSpace V5.8.R3 and VOSviewer 1.6.13. The results of bibliometric features analyses indicate that GVRP has entered into a stage of prosperity in the past decade, and over 2500 researchers from 72 countries and regions have contributed to the development of this field. Furthermore, combining the keywords and co-citation analyses, we obtain nine subfields of GVRP, elaborate their study content evolution history, and accordingly apply the research potential evaluation model to envisage its future directions. The findings show that fuel consumption and electric vehicles are major research hotspots with the most optimistic prospects, and cold chain logistics, which has both high maturity and high recent attention, is the current mainstream of GVRP. The findings may provide guidance for future research in this field.

Path Planning of an Electric Vehicle for Logistics Distribution Considering Carbon Emissions and Green Power Trading

As environmental awareness continues to grow and government policies provide incentives, electric vehicles (EVs) are becoming more widely used in logistics distribution. Considering green power trading and carbon emissions, this paper addresses the green vehicle routing problem (GVRP) and constructs an electric vehicle path model with time windows to minimize the total cost. To solve the model, a hybrid adaptive genetic algorithm (HAGA) is proposed. An improved nearest-neighbor algorithm is adopted to improve the quality of the initial population, and the adaptive crossover and mutation operators are introduced to achieve the better solution. In addition, based on the Schneider case, HAGA is used to solve the models with and without considering green power trading separately, and the results show that considering green power trading can reduce the total cost by 3.22% and carbon emissions by 23.38 kg. Finally, the experimental simulations further prove that with the increase in case size, HAGA can effectively reduce total cost. And it is beneficial for the popularization of electric vehicles in logistics distribution.

Industry 4.0 in waste management: An integrated IoT-based approach for facility location and green vehicle routing

The increasing production of solid waste rate in urban areas plays a critical role in sustainable development. To mitigate the adverse effects of waste and enhance waste management efficiency, this paper introduces a holistic approach that notably reduces the overall cost while mitigating social and environmental impacts. Central to the system's efficacy is the critical process of waste sorting, which enhances the output value of the waste management system. While previous studies have not extensively addressed simultaneous waste collection and sorting, this paper provides an innovative integrated framework. This approach Integrates waste collection with various bins, followed by their transfer to separation centers. At these centers, waste is categorized into organic and non-organic varieties, which are then dispatched to a recovery center at the second level. In the context of optimizing the routes at both levels, this paper presents a green, multi-objective location-allocation model. This model is designed to optimize the number and location of separation center facilities. Since the routing problem is influenced by the facility location model, it is addressed as a multi-depot green vehicle routing problem, integrating real-time information from IoT-equipped bins. This paper also proposes the vehicle routing problem with a split pickup, aiming to minimize cost, CO2 emissions, and visual pollution. The developed mathematical models formulate the proposed problem and it is solved by the GAMS optimization software, to apply an exact method, while Social Engineering Optimization and Keshtel algorithms are deployed to solve the routing problem for larger sizes. The proposed approach offers a comprehensive and sustainable solution to waste management, filling crucial gaps in current research and practice.

Grey Wolf Optimizer for Green Vehicle Routing Problem

Grey Wolf Optimizer for Green Vehicle Routing Problem

Bi-objective Optimization for Multi-depot Green Vehicle Routing problem with Time Windows Considering Carbon Emission

The issue of carbon emission reduction has attracted extensive attention in academic circles, but there are few studies on the Pareto frontier analysis of the multi-depot green vehicle routing problem with multi-objective. In order to effectively balance economic and environmental effects, this paper constructs a bi-objective optimization model for the open multi-depot green vehicle routing problems with time windows, which takes operating cost and carbon emission as the objective functions. NSGA-III is used to obtain the Pareto solution set of the bi-objective model. The experimental results indicate that the proposed bi-objective optimization model is effective in controlling costs and reducing carbon emissions.

Heterogeneous Fleets for Green Vehicle Routing Problem With Traffic Restrictions

Suffering from environmental distress like carbon emissions, traffic restrictions have been enforced extensively in distribution logistics. Reasonable arrangement of urban freight transportation can effectively improve distribution efficiency, reduce distribution costs, and alleviate the impact of traffic restrictions in distribution logistics. In response to increasingly stringent traffic restrictions, we establish a multi-objective optimization model, including the minimum distributions and the minimum carbon emissions. Given that the limits of battery capacity and cargo capacity, we build a green vehicle routing problem with soft time windows (GVRPTW) model with heterogenous fleets. In this study, three different factors, that is restricted area, travel time of vehicles, and carbon tax prices, are discussed in details. In order to solve the NP-hard model, we propose an improved ant colony optimization algorithm (IACO) by optimizing the state transition probability, and verifies the worth of the algorithm. The experimental results can explore the impacts of traffic restriction policies on the formulation of distribution scheme and offer reference opinions for the government to formulate reasonable restriction policies and better guide logistics enterprises to reduce carbon emissions.

Green vehicle routing using mixed fleets for cold chain distribution

Introducing clean energy into urban cold chain distribution to achieve a green transition is crucial for reducing transportation and logistics emissions and promoting sustainable urban development. This study focuses on the Cold Chain Green Multi-Depot Vehicle Routing Problem with Time Windows and Mixed Fleets (CC-GMD-VRPTW-MF) for city logistics distribution, utilizing both electric vehicles (EVs) and gasoline and diesel vehicles (GDVs). To accurately assess energy consumption, a realistic energy consumption model is employed. The CC-GMD-VRPTW-MF aims to minimize total costs (including fixed, energy consumption, damage, and environmental costs). To address the problem, we propose an improved Variable Neighborhood Search (VNS) algorithm that introduces a new balanced mechanism for perturbation and a new memory-based mechanism for local search to enhance computational performance. Numerical studies on newly designed CC-GMD-VRPTW-MF instances are conducted to investigate the effect of incorporating EVs for joint delivery, considering different carbon prices, and adjusting time windows. Furthermore, we evaluate the effectiveness of the proposed improvement strategies in VNS and demonstrate the algorithm’s performance on benchmarks of related problems.

Determination of the Shortest Distribution Routes to Minimize Fuel Consumption and CO2 Emission Using Sweep and Clarke & Wright Saving Algorithms

Nowadays, one of the areas in the field of vehicle routing problems that have received a lot of attention is Green Vehicle Routing Problem (GVRP). GVRP aims to harmonize the financial aspects and environmental concerns while routing vehicles. This research takes a case study at PT. LISA CONCRETE INDONESIA is a company engaged in the concrete industry with various precast concrete products. One of the company's main products is U-Gutter which recorded the highest sales in the company in 2022 with sales of 14,000 tons. According to the high demand for these products, it can be confirmed that the distribution activities to customers are also increased which have a negative impact on the environment. In this research, classical heuristics namely Sweep and Clark & Wright saving algorithm are used. Both of these methods will be implemented using Microsoft Excel software to determine the shortest distribution routes to deliver products to customers in PT. LISA CONCRETE INDONESIA and also to minimize fuel consumption and CO2 emission. The result shows that Clark & Wright saving algorithm is 11,49% better than the Sweep algorithm and also 43,23% better than the company/existing method to minimize fuel consumption and CO2 emission.

Feasibility on the integration of passenger and freight transportation in rural areas: A service mode and an optimization model

Public transport operators in rural areas have been under pressure from weak profitability and emission issues. At the same time, scattered demand for transport has been preventing logistics systems from reaching the last mile in residential areas. Multimodal transport can synergistically integrate passenger and freight demand, increase transportation network coverage, and reduce the cost of transportation, while demand-driven services improve the flexibility and reliability of operational decisions. Therefore, this paper proposes a demand-driven passenger-and-freight-integration service (DDPFIS) mode. From the perspective of routing decisions, a new mixed-integer linear programming model based on the green vehicle routing problem is formulated to assist public transport operator’s complex decision-making. In the proposed model, vehicle capacity is fully utilized through a combination of passenger and freight demand so that optimal decisions are made about vehicle routing. Numerical experiments are designed and conducted based on realistic instances with the results indicating that: the DDPFIS mode enables effective integration of different demands, leading to high-level vehicle capacity utilization and cost reduction; and compared with two conventional models of vehicle routing problems, the proposed model achieves lower fuel consumption and cost for all problem sizes. In addition, some important management insights are provided, e.g., a greater proportion of integrated service nodes is not necessarily better; and it is more suitable to provide a service for rural residents who are relatively insensitive to time.