Green Vehicle Routing Optimization in Urban Traffic Congestion Environment Based On K-Means Clustering and Genetic Algorithm

Nowadays, environmental effects of logistics and extensive consumption of natural resources have been given more attention by governments and industries. Conventionally, these issues and their impact on distribution logistics were not emphasized specifically or addressed directly when solving the Vehicle Routing Problem (VRP). Hence, Green VRP (GVRP) has been introduced to take into consideration both the economic and environmental costs when determining effective routes for distribution services. GVRP is a branch of green logistics in which the externalities of using vehicles, enhancement of transportation effectiveness at operational level, ensuring optimal energy consumption of vehicles, and minimizing fuel consumption are taken into account in the routing and scheduling. This paper presents our study which concerns with formulating a mathematical programming model of Green Capacitated VRP (GCVRP), which focuses are on minimization of the greenhouse gas emissions and fuel consumption, to assuage the resulting effects of transportation on the environment. The formulated Mixed Integer Goal Programming (MIGP) model has multiple objective functions as its goal, which are minimizing the total distance travelled, minimizing the total fuel consumption and minimizing the total Carbon Dioxide emissions. Two set of benchmark instances have been used to test the proposed model. The MIGP model is solved by the preemptive GP approach and using the MATLAB intlinprog solver. Based on the computational results, the model has been proven to be able to produce optimal solutions, thus indicating that it has the potential to be applied to real-world VRPs.

Green vehicle routing problem (GVRP) aims to consider greenhouse gas emissions reduction, while routing the vehicles. It can be either through adopting Alternative Fuel Vehicles (AFVs) or with existing conventional fossil fuel vehicles in fleets. GVRP also takes into account environmental sustainability in transportation and logistics. We critically review several variations and specializations of GVRP to address issues related to charging, pickup, delivery, and energy consumption. Starting with the concepts and definitions of GVRP, we summarize the key elements and contributors to GVRP publications. Afterward, the issues regarding each category of green vehicle routing are reviewed, based on which key future research directions and challenges are suggested. It was observed that the main focus of previous publications is on the operational level routing decision and not the supply chain issues. The majority of publications used metaheuristic methods, while overlooking the emerging machine learning methods. We envision that in addition to machine learning, reinforcement learning, distributed systems, the internet of vehicles (IoV), and new fuel technologies have a strong role in developing the GVRP research further.

Green vehicle routing problem (GVRP) has emerged as an important agenda in green logistics and received scientific attention from researchers. In this paper, a literature review on recent developments regarding the GVRP is presented. In order to further clarify the research status, a classification of GVRP that categorizes GVRP into pollution routing problem and new energy vehicle route problem. it is concluded with some significantly promising tends and future directions about the research on GVRP.

Based on an analysis of the congestion effect and changes in the speed of vehicle flow during morning and evening peaks in a large- or medium-sized city, the piecewise function is used to capture the rules of the time-varying speed of vehicles, which are very important in modelling their fuel consumption and CO2 emission. A joint optimization model of the green vehicle scheduling and routing problem with time-varying speeds is presented in this study. Extra wages during nonworking periods and soft time-window constraints are considered. A heuristic algorithm based on the adaptive large neighborhood search algorithm is also presented. Finally, a numerical simulation example is provided to illustrate the optimization model and its algorithm. Results show that, (1) the shortest route is not necessarily the route that consumes the least energy, (2) the departure time influences the vehicle fuel consumption and CO2 emissions and the optimal departure time saves on fuel consumption and reduces CO2 emissions by up to 5.4%, and (3) extra driver wages have significant effects on routing and departure time slot decisions.

This study aims to investigate the Green Vehicle Routing Problem (GVRP), which considers stochastic traffic speeds, so that fuel consumption and emissions can be reduced. Considering a heterogeneous fleet, the fuel consumption rate differs due to several factors, such as vehicle types and conditions, travel speeds, roadway gradients, and payloads. A mathematical model was proposed to deal with the GVRP, and its objective is to minimize the sum of the fixed costs and the expected fuel consumption costs. A customized genetic algorithm was proposed for solving the model. The computational experiments confirm the efficiency of the algorithm and show that the solution of GVRP is quite different from that of the traditional vehicle routing problem. We suggest that a company should use light vehicles to service the customers situated at higher terrains. The customers with higher demands can be visited earlier, but the customers situated at higher terrains or far away from the depot should be visited later. The study also found that the fixed costs of dispatching vehicles are critical in GVRP; a logistics company may thus tend to use large vehicles, despite that it may cause higher fuel consumption and emissions. The proposed model and algorithm are capable of suggesting a guidance for green logistics service providers to adopt a beneficial vehicle routing plan so as to eventually achieve a low economic and environmental cost.

The green vehicle routing problem (GVRP) has been a prominent topic in the literature on logistics and transportation, leading to extensive research and previous review studies covering various aspects. Operations research has seen the development of various exact and approximation approaches for different extensions of the GVRP. This paper presents an up-to-date and thorough review of GVRP literature spanning from 2016 to 2023, encompassing 458 papers. significant contribution lies in the updated solution approaches and algorithms applied to both single-objective and multi-objective GVRP. Notably, 92.58 % of the papers introduced a mathematical model for GVRP, with many researchers adopting mixed integer linear programming as the preferred modeling approach. The findings indicate that both metaheuristics and hybrid are the most employed solution approaches for addressing single-objective GVRP. Among hybrid approaches, the combination of metaheuristics-metaheuristics is particularly favored by GVRP researchers. Furthermore, large neighborhood search (LNS) and its variants (especially adaptive large neighborhood search) emerges as the most widely adopted algorithm in single-objective GVRP. These algorithms are proposed within both metaheuristic and hybrid approaches, where A-/LNS is often combined with other algorithms. Conversely, metaheuristics are predominant in addressing multi-objective GVRP, with NSGA-II being the most frequently proposed algorithm. Researchers frequently utilize GAMS and CPLEX as optimization modeling software and solvers. Furthermore, MATLAB is a commonly employed programming language for implementing proposed algorithms.

A model for capacitated green vehicle routing problem with the time-varying vehicle speed and soft time windows

The worldwide awareness of sustainable development during the past few decades has pushed organizations to incorporate sustainability within their supply chain operations. The Logistics division of the supply chain management was already significantly affected, resulting in a change in conventional vehicle routing problem (VRP) goals towards minimizing environmental effect of transportation. Green vehicle routing Optimization has been used to help minimize the unfavourable impacts on the environment due to supply chains. The GVRP is of great importance to policymakers who want to decrease emissions of GHGs. This research introduces a methodology for optimising the route of the AFVs with the aim of minimizingoperating costs and CO <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf> emissions in the manner of an overall cost incurred. This research work has proposed a GA-based solution for solving the Green Vehicle Routing Problem that can be regarded as a basic model for alternative-fuel vehicles routing optimization. The research was focused on genetic algorithm (GA) design and optimization to address the problem formulation utilising sample cases.

A Genetic Algorithm for a Green Vehicle Routing Problem

This study aims to investigate the green vehicle routing problem (GVRP), which considers stochastic traffic speeds, so that fuel consumption and emissions can be reduced. Considering a heterogeneous fleet, the fuel consumption rate differs due to several factors, such as vehicle types and conditions, travel speeds, roadway gradients, and payloads. A mathematical model was proposed to deal with the GVRP, and its objective is to minimize the sum of the fixed costs and the expected fuel consumption costs. A customized genetic algorithm was proposed for solving the model. The computational experiments confirm the efficiency of the algorithm and show that the solution of GVRP is quite different from that of the traditional vehicle routing problem. The proposed model and algorithm are capable of suggesting a guidance for green logistics service providers to adopt a beneficial vehicle routing plan so as to eventually achieve a low economic and environmental cost.

This study develops a Green Vehicle Routing Problem (GVRP) model to address key logistics challenges, including time windows, simultaneous pickup and delivery, heterogeneous vehicle fleets, and multiple trip allocations. The model incorporates emissions-related costs, such as carbon taxes, to encourage sustainable supply chain operations. Emissions are calculated based on the total shipment weight and the travel distance of each vehicle. The objective is to minimize operational costs while balancing economic efficiency and environmental sustainability. A Genetic Algorithm (GA) is applied to optimize vehicle routing and allocation, enhancing efficiency and reducing costs. A Liquid Petroleum Gas (LPG) distribution case study in Yogyakarta, Indonesia, validates the model’s effectiveness. The results show significant cost savings compared to current route planning methods, alongside a slight increase in carbon. A sensitivity analysis was conducted by testing the model with varying numbers of stations, revealing its robustness and the impact of the station density on the solution quality. By integrating carbon taxes and detailed emission calculations into its objective function, the GVRP model offers a practical solution for real-world logistics challenges. This study provides valuable insights for achieving cost-effective operations while advancing green supply chain practices.

Transportation, as a part of the supply chain process, contributes to carbon emission which leads to climate change and global warming. This environmental issue gives an impact to decisions regarding the supply chain of a company. One way to deal with this issue is by analyzing their vehicle routing problem. In this study, the issue about routing problems in green supply chain by considering the heterogeneous fleet is being discussed. One variant of Green Vehicle Routing Problem (GVRP) reviewed in this paper is about Heterogeneous Alternative Fuel Vehicles for Green Vehicle Routing Problem (HAFVGVRP). The purpose of this study is to review the development of GVRP with heterogeneous alternative fuel vehicles and the gap or state-of-the-art on existing researches. The review was classified according to the objectives, type of fleet, and solution used. Moreover, this study also presents the trend and direction of further research.

The green vehicle routing problem (GVRP) is a relatively new topic, which aims to minimize greenhouse gasses (GHG) emissions produced by a fleet of vehicles. Both internal combustion vehicles (ICV) and alternative fuel vehicles (AFV) are considered, dividing GVRP into two separate subclasses: ICV-based GVRP and AFV-based GVRP. In the ICV-based subclass, the environmental aspect comes from the objective function which aims to minimize GHG emissions or fuel usage of ICVs. On the other hand, the environmental aspect of AFV-based GVRP is implicit and comes from using AFVs in transport. Since GVRP is NP-hard, finding the exact solution in a reasonable amount of time is often impossible for larger instances, which is why metaheuristic approaches are predominantly used. The purpose of this study is to detect gaps in the literature and present suggestions for future research in the field. For that purpose, we review recent papers in which GVRP was tackled by some metaheuristic methods and describe algorithm specifics, VRP attributes, and objectives used in them.

In this paper, the green vehicle routing problem with time windows constraint is studied in the presence of a heterogeneous fleet of vehicles and filling stations. In addition, the number of vehicles and their fuel tank capacity are both limited. The main contribution of this study is the simultaneous consideration of these features, which makes the problem more practical. For this purpose, a mixed integer linear programming model that minimises the transportation costs and (or carbon dioxide) emissions, is proposed. Furthermore, a genetic algorithm and a population-based simulated annealing are developed to find high-quality solutions for large-scale instances. To validate the proposed model and algorithms, 28 instances are generated using a benchmark database. The computational results demonstrate that both algorithms provide efficient solutions regarding the objective function value and CPU time. Finally, a comprehensive sensitivity analysis is carried out to show the importance of features mentioned above. [Received: 7 October 2016; Revised: 27 December 2018; Accepted: 13 January 2019]

Over the two last decades, distribution companies have been aware of the importance of paying attention to the all aspects of a distribution system simultaneously to be successful in the global market. These aspects are the economic, the environmental, the social and the safety aspects. In the Vehicle Routing Problem (VRP) literature, the economic issue has often been used, while the environmental, the safety and the social concerns have been less proportion of studies. The Green vehicle routing problem (GVRP) is one of the recent variants of the VRP, dealing with environmental aspects of distribution systems. In this paper, two developed mixed integer programming models are presented for the GVRP with social and safety concerns. Moreover, a Genetic Algorithm (GA) is developed to deal efficiently with the problem in large size. Different numerical analyses have performed to validate the presented algorithm in comparison to exact solutions and investigate the influence of several key factors like the effect of increasing the cost of safety aspect on route balancing, and customer waiting time. The results confirm that the proposed algorithm performs well and has more social and safety benefits (such as more balanced tours and fewer customers waiting time than the classic GVRP.