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The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alternative-Fuel Vehicles (AFVs), Electric Vehicles (EVs) and Hybrid Vehicles (HVs). Second, it presents a comprehensive survey by considering each variant of the classification, technical constraints and solution methods arising in the literature. The results of this paper show that studies on EF-VRP are relatively novel and there is still room for large improvements in several areas. So, to determine future insights, for each classification of EF-VRP studies, the paper provides the literature gaps and future research needs.

We address a particular pickup and delivery vehicle routing problem arising in the collection and disposal of bulky recyclable waste. Containers of different types, used to collect different waste materials, once full, must be picked up to be emptied at suitable disposal plants and replaced by empty containers alike. All requests must be served, and routes are subject to a maximum duration constraint. Minimizing the number of vehicles is the main objective, while minimizing the total route duration is a secondary objective. The problem belongs to the class of rollon–rolloff vehicle routing problems (RR-VRPs), though some characteristics of the case study, such as the free circulation of containers and the limited availability of spare containers, allow us to exploit them in the solution approach. We formalize the problem as a special vehicle routing problem on a bipartite graph, we analyze its structure, and we compare it to similar problems emphasizing the impact of limited spare containers. Moreover, we propose a neighborhood-based metaheuristic that alternatively switches from one objective to the other along the search path and periodically destroys and rebuilds parts of the solution. The main algorithm components are experimentally evaluated on real and realistic instances, the largest of which fail to be solved by a mixed-integer linear programming solver. We are increasingly competitive with the solver as the instance size increases, especially regarding fleet size. In addition, the algorithm is applied to the benchmark instances for the RR-VRP.

The scheduling of deliveries and the routing of vehicles are of great importance for supply chain operations, as both determine to a great extent the distribution costs, as well as customer satisfaction. The fact that the distribution of goods is being affected by multiple factors, stemming from the demands of transportation companies, customers, and the external environment, has made the vehicle routing problem (VRP) among the most studied topics in operational research. These factors are transformed either to constraints or variables of the problem and finally lead to the creation of different variants of the VRP, formulated and studied by researchers. Moreover, the management of logistics and supply chain operations is being enhanced by the use of algorithms, integrated into information systems, enabling the optimization of real-life distribution cases. This paper presents a methodology for classifying the multiple VRP variants related to freight transportation, that most logistics and distribution companies face in their daily operations, as well as the algorithms solving the various problems. The application of the research methodology concluded to 334 papers, which were further sorted to 263 papers on the subject of freight transportation, aiming to identify the trends of the VRP variants and the applied algorithms, during the last decade. The correlation between the VRP variants and the applied algorithms is also identified. Finally, the paper presents the quantitative and qualitative results of the literature review and discusses the scientific publications with a significant impact on the research community.

In this paper, we introduce a novel variant of the Vehicle Routing Problem (VRP), the Rechargeable Rover Routing Problem (RRRP), which addresses the routing of energy-constrained autonomous electric rovers for Martian missions. We formulate a graphbased representation of the problem and propose an initial formulation as a mixed-integer non-linear program (MINLP). To enhance computational efficiency, we demonstrate how the model can be linearized. The resulting mixed integer linear model is evaluated on small-scale test cases, and its computational complexity is analyzed for larger problems with up to 30 Points of Interest (PoIs). Our experiments show that the problem can be solved to optimality for problem sizes anticipated in upcoming Mars expeditions. However, for future missions involving swarms of rovers, the development of more efficient heuristic or approximation algorithms will be necessary.

New solutions for managing waste have emerged due to the rise of Smart Cities and the Internet of Things. These solutions can also be applied in rural environments, but they require the deployment of a low cost and low consumption sensor network which can be used by different applications. Wireless technologies such as LoRa and low consumption microcontrollers, such as the SAM L21 family make the implementation and deployment of this kind of sensor network possible. This paper introduces a waste monitoring and management platform used in rural environments. A prototype of a low consumption wireless node is developed to obtain measurements of the weight, filling volume and temperature of a waste container. This monitoring allows the progressive filling data of every town container to be gathered and analysed as well as creating alerts in case of incidence. The platform features a module for optimising waste collection routes. This module dynamically generates routes from data obtained through the deployed nodes to save energy, time and consequently, costs. It also features a mobile application for the collection fleet which guides every driver through the best route—previously calculated for each journey. This paper presents a case study performed in the region of Salamanca to evaluate the efficiency and the viability of the system’s implementation. Data used for this case study come from open data sources, the report of the Castilla y León waste management plan and data from public tender procedures in the region of Salamanca. The results of the case study show a developed node with a great lifetime of operation, a large coverage with small deployment of antennas in the region, and a route optimization system which uses weight and volume measured by the node, and provides savings in cost, time and workforce compared to a static collection route approach.

This paper proposes a novel route optimization framework to solve the problem of instant pick-up and delivery for e-grocery orders. The proposed framework extends the traditional time-windowed package delivery problem. We demonstrate the effectiveness of our approach for this integrated problem using actual delivery data from HepsiJet, a leading e-commerce logistics provider in Turkey. We first employ several machine learning algorithms and simulations to investigate the capacity of the courier. Subsequently, a dynamic route planning workflow is executed with a highly specialized and novel routing algorithm. Our proposed heuristic approach considers combined fleet operations for delivering regular packages originating from a central depot and dynamic e-grocery orders picked up at local supermarkets and delivered to the customers. The heuristic algorithm constitutes k-opt and node transfer operation variations customized for this integrated problem. We report the performance of our approach in problem instances from the literature and instances from HepsiJet’s daily operations, which we also publicly share as new route optimization problem instances. Our results suggest that, despite the more complex nature of the integrated problem, our proposed algorithm and solution framework produce more efficient and cost-effective solutions that offer additional business opportunities for companies such as HepsiJet. The computational analyses reveal that implementing our proposed approach yields significant efficiency gains and cost reductions for the company, with a distance reduction of over 30%, underscoring our approach’s effectiveness in achieving substantial cost savings and enhanced efficiency through integrating two distinct delivery operations.

In large-scale disasters, such as earthquakes and tsunamis, quick and sufficient transportation of emergency relief supplies is required. Logistics activities conducted to quickly provide appropriate aid supplies (relief goods) to people affected by disasters are known as humanitarian logistics (HL), and play an important role in terms of saving the lives of those affected. In the previous last-mile distribution of HL, supplies are transported by trucks and helicopters, but these transport methods are sometimes not feasible. Therefore, the use of unmanned aerial vehicles (UAVs) to transport supplies is attracting attention due to their convenience regardless of the disaster conditions. However, existing transportation planning that utilizes UAVs may not meet some of the requirements for post-disaster transport of supplies. Equitable distribution of supplies among affected shelters is particularly important in a crisis situation, but it has not been a major consideration in the logistics of UAVs in the existing study. Therefore, this study proposes transportation planning by introducing three crucial performance metrics: (1) the rapidity of supplies, (2) the urgency of supplies, and (3) the equity of supply amounts. We formulated the routing problem of UAVs as the multi-objective, multi-trip, multi-item, and multi-UAV problem, and optimize the problem with Q-learning (QL), one of the reinforcement learning methods. We performed reinforcement learning for multiple cases with different rewards and quantitatively evaluated the transportation of each countermeasure by comparing them. The results suggest that the model improved the stability of the supply of emergency relief supplies to all evacuation centers when compared to other models.

Assessing Radiative Power (RP) output is essential for monitoring and understanding volcanic systems. While Mid‐Infrared channels are used to assess thermal outputs at volcanoes exhibiting effusive activity, Thermal‐InfraRed (TIR) bands are better suited for measuring moderate‐to‐low‐temperature (≲600 K) features, such as those associated with hydrothermal activity. However, failure to meet key assumptions in TIR‐based calculations results in up to a ∼90% RP underestimation of ≲600 K sources. We thus introduce the TIR‐based Volcanic Radiative Power (VRPTIR ${\\text{VRP}}_{\\text{TIR}}$) method to accurately retrieve RP from single‐band TIR (10.5–12 μm) spectral radiance at systems dominated by surface temperatures of ≲600 K, that is, crater lakes and fumarole fields, achieving an uncertainty of ±35%. Comparison with ground truth for Ruapehu, El Chichón, Taal, Vulcano, Puracé, Poás, and White Island demonstrates the accuracy of VRPTIR ${\\text{VRP}}_{\\text{TIR}}$ in quantifying thermal output and detecting subtle variations in volcanic activity. This exportable method will facilitate compilation of global RP inventories for moderate‐to‐low‐temperature volcanic systems.

This paper proposes a multi-objective Green Vehicle Routing Problem (G-VRP) considering two types of vehicles likely company-owned vehicle and third-party logistics in the imprecise environment. Focusing only on one objective, especially the distance in the VRP is not always right in the sustainability point of view. Here we present a bi-objective model for the G-VRP that can address the issue of the emission of GreenHouse Gases (GHGs). We also consider the demand as a rough variable. This paper uses the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) to solve the proposed model. Finally, it uses Multicriteria Optimization and Compromise Solution (abbreviation in Serbian – VIKOR) method to determine the best alternative from the Pareto front. First published online 25 February 2021

Vehicle routing problems are widely encountered in real-world applications. This paper addresses a specific variant known as the Vehicle Routing Problem with Flexible Time Windows (VRPFlexTW), where solutions must comply with constraints, including travel, service, and waiting times, along with time-window restrictions. We propose the Nondominated Sorting Genetic Algorithm II (NSGA-II) and detail its components. Additionally, we provide a computational comparison between NSGA-II and the Ant Colony Optimizer (ACO) for several instances of VRPFlexTW. This comparison aims to evaluate the efficiency and performance of these approaches in solving this complex problem. Finally, the experimental results demonstrate that NSGA-II significantly improves solution quality and reduces the optimal fleet size, establishing it as the most effective algorithm among those presented. The results reveal that the NSGA-II algorithm consistently outperforms ACO and ALNS across all tested client configurations. NSGA-II achieves the lowest cost function values, demonstrating superior cost optimization by significantly reducing the total routing costs compared to ACO and ALNS