Search Results (148)

With the development of dynamic wireless power transfer (DWPT) technology, the introduction of wireless charging lanes (WCLs) in traffic systems is seen as a promising trend for electrified transportation. Though there has been extensive discussion about the planning and allocation of WCLs in different situations, studies on traffic control models for WCLs are relatively lacking. Thus, this paper aims to design a coordinated optimization strategy for managing electric vehicle (EV) traffic on an expressway network, which integrates a corridor traffic flow model with a wireless power transmission model. Two components are considered in the control objective: the total energy increased for the EVs and the total number of EVs served by the expressway, over the problem horizon. By setting the trade-off coefficients for these two objectives, our model can be used to achieve mixed optimization of WCL traffic management. The decisions include metering of different on-ramps as well as routing plans for different groups of EVs defined by origin/destination pairs and initial SOC levels. The control problem is formulated as a novel linear programming model, rendering an efficient solution. Numerical examples are used to verify the effectiveness of the proposed traffic control model. The results show that with the properly designed traffic management strategy, a notable increase in charging performance can be achieved by compromising slightly the traffic performance while maintaining overall smooth operation throughout the expressway system. Full article

Complex optimization problems, such as traffic routing and electric vehicle (EV) charging scheduling, are becoming increasingly challenging for intelligent transportation systems (ITSs), in particular as computational resources are limited and network conditions evolve frequently. This paper explores a quantum computing approach to address these issues by proposing a hybrid quantum-classical (HQC) workflow that leverages the variational quantum eigensolver (VQE), an algorithm particularly well suited for execution on noisy intermediate-scale quantum (NISQ) hardware. To this end, the EV charging scheduling and traffic routing problems are both reformulated as binary optimization problems and then encoded into Ising Hamiltonians. Within each VQE iteration, a parametrized quantum circuit (PQC) is prepared and measured on the quantum processor to evaluate the Hamiltonian’s expectation value, while a classical optimizer—such as COBYLA, SPSA, Adam, or RMSProp—updates the circuit parameters until convergence. In order to find optimal or nearly optimal solutions, VQE uses PQCs in combination with classical optimization algorithms to iteratively minimize the problem Hamiltonian. Simulation results exhibit that the VQE-based method increases the efficiency of EV charging coordination and improves route selection performance. These results demonstrate how quantum computing will potentially advance optimization algorithms for next-generation ITSs, representing a practical step toward quantum-assisted mobility solutions. Full article

This study addresses the Plug-in Hybrid Electric Vehicle Routing Problem (PHEVRP), an extension of the classical VRP that incorporates energy mode switching and partial charging strategies. We propose a novel routing model that integrates three energy modes—fuel-only, electric-only, and hybrid—along with partial recharging decisions to enhance energy flexibility and reduce operational costs. To overcome the computational challenges of large-scale instances, a Dantzig–Wolfe decomposition algorithm is designed to efficiently reduce the solution space via column generation. Experimental results demonstrate that the hybrid-mode with partial charging strategy consistently outperforms full-charging and single-mode approaches, especially in clustered customer scenarios. To further evaluate algorithmic performance, an Ant Colony Optimization (ACO) heuristic is introduced for comparison. While the full model fails to solve instances with more than 30 customers, the DW algorithm achieves high-quality solutions with optimality gaps typically below 3%. Compared to ACO, DW consistently provides better solution quality and is faster in most cases, though its computation time may vary due to pricing complexity. Full article

This study investigates a variant of the shortest path problem (SPP) tailored for plug-in hybrid electric vehicles (PHEVs), incorporating two practical features: hybrid energy mode switching and partial charging. A novel modeling framework is proposed that enables PHEVs to dynamically switch between electricity and fuel along each edge and to recharge partially at charging stations. Unlike most prior studies that rely on more complex modeling approaches, this paper introduces a compact mixed-integer linear programming (MILP) model that remains directly solvable using commercial solvers such as Gurobi. To address large-scale networks, a customized labeling algorithm is developed for an efficient solution. Numerical results on benchmark networks show that the hybrid mode and partial charging can reduce total cost by up to 29.76% and significantly affect route choices. The proposed algorithm demonstrates strong scalability, solving instances with up to 33,000 nodes while maintaining near-optimal performance, with less than 5% deviation in smaller cases. Full article

With the rapid proliferation of electric vehicles (EVs), urban logistics faces increasing challenges in optimizing vehicle routing. This paper presents a new modeling framework for the Electric Vehicle Routing Problem (EVRP), where multiple electric trucks serve a set of customers within their capacity limits. The model incorporates critical EV-specific constraints, including limited battery range, charging demand, and dynamic urban traffic conditions, with the objective of minimizing total delivery cost. To efficiently solve this problem, a Dual Population Cooperative Genetic Algorithm (DPCGA) is proposed. The algorithm employs a dual-population mechanism for global exploration, effectively expanding the search space and accelerating convergence. It then introduces local refinement operators to improve solution quality and enhance population diversity. A large number of experimental results demonstrate that DPCGA significantly outperforms traditional algorithms in terms of performance, achieving an average 3% improvement in customer satisfaction and a 15% reduction in computation time. Furthermore, this algorithm shows superior solution quality and robustness compared to the AVNS and ESA-VRPO algorithms, particularly in complex scenarios such as adjustments in charging station layouts and fluctuations in vehicle range. Sensitivity analysis further verifies the stability and practicality of DPCGA in real-world urban delivery environments. Full article

The implementation of innovative strategies in transportation is fundamental for the transition to sustainable mobility in road freight transport. Electric trucks provide a sustainable solution, significantly contributing to the reduction in pollutant emissions, lower operational costs, and the ability to recharge from renewable energy sources. In this context, this article proposes a methodology to support sustainable mobility optimization considering the variables related to the logistical problems of electric vehicles (recharging time and autonomy), which allows for routes to be compared based on the shortest time, lowest costs, and shortest distance for delivering goods while integrating recharge time windows into optimized routes. The study results reveal that additional recharging can significantly impact total travel time and total costs due to variable tariffs at charging stations. Consequently, the model assists in improving resource management and delivery schedule management, thereby increasing operational efficiency and correcting potential conflicts or delays. Therefore, the method provides mobility as a service and offers greater flexibility to decision-makers in selecting the path that best meets delivery objectives, aiming to propose solutions to reduce the impact on the logistics process through the adoption of electric trucks in last-mile freight transport. Full article

The use of electric vehicles in urban transportation is increasing daily due to their energy efficiency and environmental friendliness. In last-mile logistics, route optimization must consider charging station locations while balancing operational costs and customer satisfaction. In this context, solutions for cost-oriented route optimization have been presented in the literature. On the other hand, customer satisfaction is also important for third-party logistics companies. This study discusses the Capacitated Electric Vehicle Routing Problem with Time Windows (CEVRPTW) encountered in last-mile logistics. This article defines the objective function of minimizing total tardiness and compares the routes between the service provider logistics company and the customer receiving the service. In this study, the CEVRPTW was solved for the minimum total tardiness objective function with the hybrid adaptive large neighborhood search (ALNS) algorithm. The success of ALNS was proven by comparing the differences between the optimal solutions obtained with the CPLEX Solver and the ALNS solutions. Tardiness objective function-specific operators for ALNS are proposed and supported by local search and VNS algorithms. The findings of this study contribute to the literature by analyzing the balance trade-offs between customer-oriented and cost-oriented and the effect of time windows on the number of vehicles. Full article

This study demonstrates that small-scale liquefied natural gas (SS LNG) is a viable and cost-effective alternative to High-Speed Diesel (HSD) for power generation in remote areas of Indonesia. An integrated supply chain model is developed to optimize total costs based on LNG inventory levels. The model minimizes transportation costs from supply depots to demand points and handling costs at receiving terminals, which utilize Floating Storage Regasification Units (FSRUs). LNG distribution is optimized using a Multi-Depot Capacitated Vehicle Routing Problem (MDCVRP), formulated as a Mixed Integer Linear Programming (MILP) problem to reduce fuel consumption, CO₂ emissions, and vessel rental expenses. The novelty of this research lies in its integrated cost optimization, combining transportation and handling within a model specifically adapted to Indonesia’s complex geography and infrastructure. The simulation involves four LNG plant supply nodes and 50 demand locations, serving a total demand of 15,528 m³/day across four clusters. The analysis estimates a total investment of USD 685.3 million, with a plant-gate LNG price of 10.35 to 11.28 USD/MMBTU at a 10 percent discount rate, representing a 55 to 60 percent cost reduction compared to HSD. These findings support the strategic deployment of SS LNG to expand affordable electricity access in remote and underserved regions. Full article

Efficient waste management remains critical to achieving sustainable urban development, addressing challenges related to resource conservation, environmental preservation, and carbon emissions reduction. This review synthesizes advancements in waste management technologies, focusing on three transformative areas: optimization techniques, the integration of electric vehicles (EVs), and the adoption of smart technologies. Optimization methodologies, such as vehicle routing problems (VRPs) and dynamic scheduling, have demonstrated significant improvements in operational efficiency and emissions reduction. The integration of EVs has emerged as a sustainable alternative to traditional diesel fleets, reducing greenhouse gas emissions while addressing infrastructure and economic challenges. Additionally, the application of smart technologies, including Internet of Things (IoT), artificial intelligence (AI), and the Geographic Information System (GIS), has revolutionized waste monitoring and decision-making, enhancing the alignment of waste systems with circular economy principles. Despite these advancements, barriers such as high costs, technological complexities, and geographic disparities persist, necessitating scalable, inclusive solutions. This review highlights the need for interdisciplinary research, policy standardization, and global collaboration to overcome these challenges. The findings provide actionable insights for policymakers, municipalities, and businesses, enabling data-driven decision-making, optimized waste collection, and enhanced sustainability strategies in modern waste management systems. Full article

With the escalating environmental crisis, electric vehicles have emerged as a key solution for emission reductions in logistics due to their low-carbon attributes, prompting significant attention and extensive research on the electric vehicle routing problem (EVRP). However, existing studies often overlook charging infrastructure (CI) capacity constraints and fail to fully exploit the synergistic potential of heterogeneous energy replenishment infrastructures (HERIs). This paper addresses the EVRP with HERIs under various capacity constraints (EVRP-HERI-CC), proposing a mixed-integer programming (MIP) model and a hybrid ant colony optimization (HACO) algorithm integrated with a variable neighborhood search (VNS) mechanism. Extensive numerical experiments demonstrate HACO’s effective integration of problem-specific characteristics. The algorithm resolves charging conflicts via dynamic rescheduling while optimizing charging-battery swapping decisions under an on-demand energy replenishment strategy, achieving global cost minimization. Through small-scale instance experiments, we have verified the computational complexity of the problem and demonstrated HACO’s superior performance compared to the Gurobi solver. Furthermore, comparative studies with other advanced heuristic algorithms confirm HACO’s effectiveness in solving the EVRP-HERI-CC. Sensitivity analysis reveals that appropriate CI capacity configurations achieve economic efficiency while maximizing resource utilization, further validating the engineering value of HERI networks. Full article

To address the challenges of distribution cost and efficiency in electric vehicle (EV) logistics, this study proposes a time-dependent, multi-center, semi-open heterogeneous fleet model. The model incorporates a nonlinear power consumption measurement framework that accounts for vehicle parameters and road impedance, alongside an objective function designed to minimize the total cost, which includes fixed vehicle costs, driving costs, power consumption costs, and time window penalty costs. The self-organizing mapping network method is employed to initialize the EV routing, and an improved adaptive large neighborhood search (IALNS) algorithm is developed to solve the optimization problem. Experimental results demonstrate that the proposed algorithm significantly outperforms traditional methods in terms of solution quality and computational efficiency. Furthermore, through real-world case studies, the impacts of different distribution modes, fleet sizes, and charging strategies on key performance indicators are analyzed. These findings provide valuable insights for the optimization and management of EV distribution routes in logistics enterprises. Full article

With increasingly diverse customer demands and the rapid growth of the new energy logistics industry, establishing a sustainable and responsive logistics network is critical. In a multi-depot logistics network, adopting collaborative distribution and resource sharing can significantly improve operational efficiency. This study proposes collaboration and resource sharing for a multi-depot electric vehicle (EV) routing problem with time windows and dynamic customer demands. A bi-objective optimization model is formulated to minimize the total operating costs and the number of EVs. To solve the model, a novel hybrid algorithm combining a mini-batch k-means clustering algorithm with an improved multi-objective differential evolutionary algorithm (IMODE) is proposed. This algorithm integrates genetic operations and a non-dominated sorting operation to enhance the solution quality. The strategies for dynamically inserting customer demands and charging stations are embedded within the algorithm to identify Pareto-optimal solutions effectively. The algorithm’s efficacy and applicability are verified through comparisons with the multi-objective genetic algorithm, the multi-objective evolutionary algorithm, the multi-objective particle swarm optimization algorithm, multi-objective ant colony optimization, and a multi-objective tabu search. Additionally, a case study of a new energy logistics company in Chongqing City, China demonstrates that the proposed method significantly reduces the logistics operating costs and improves logistics network efficiency. Sensitivity analysis considering different dynamic customer demand response modes and distribution strategies provides insights for reducing the total operating costs and enhancing distribution efficiency. The findings offer essential insights for promoting an environmentally sustainable and resource-efficient city. Full article

The growth of the urban population and the increase in e-commerce activities have resulted in challenges for last-mile delivery. On the other hand, electric vehicles (EVs) have been introduced to last-mile delivery as an alternative to fossil fuel vehicles. Electric vehicles (EVs) not only play a pivotal role in reducing greenhouse gas emissions and air pollution but also contribute significantly to the development of more energy-efficient and environmentally sustainable urban transportation systems. Within these dynamics, the Electric Vehicle Routing Problem (EVRP) has begun to replace the Vehicle Routing Problem (VRP) in last-mile delivery. While classic vehicle routing ignores fueling, both the location of charging stations and charging time should be included in the Electric Vehicle Routing Problem due to the long recharging time. This study addresses the Capacitated EVRP (CEVRP) with a novel Q-learning algorithm. Q-learning is a model-free reinforcement learning algorithm designed to maximize an agent’s cumulative reward over time by selecting optimal actions. Additionally, a new dataset is also published for the EVRP considering field constraints. For the design of the dataset, real geographical positions have been used, located in the province of Eskisehir, Türkiye. It also includes environmental information, such as streets, intersections, and traffic density, unlike classical EVRP datasets. Optimal solutions are obtained for each instance of the EVRP by using the mathematical model. The results of the proposed Q-learning algorithm are compared with the optimal solutions of the presented dataset. Test results show that the proposed algorithm provides remarkable advantages in obtaining routes in a shorter time for EVs. Full article

The promotion of electric mobility is a key objective of energy transition, and it is aimed at significantly reducing greenhouse gas emissions, with road transport being understood as a major contributor. Despite its potential, the adoption of electric vehicles (EVs) in logistics faces critical challenges, including limited battery range, charging time, and the availability of charging infrastructure. Moreover, deploying charging stations must be carefully coordinated with the public grid to ensure seamless integration. This paper proposes a novel methodology for the optimal design and management of EV fleets in logistics. Our approach introduces innovations such as leveraging self-produced electricity and incorporating time-varying energy prices that can be tailored to individual nodes. This marks an important step toward a comprehensive interdisciplinary framework that integrates technical solutions with public policy considerations. Through case studies, we explore how various parameters and resource distributions influence optimal decisions. The findings demonstrate significant potential for cost reduction and enhanced efficiency when applying this methodology to EV-based logistics, thereby offering actionable insights for advancing sustainable transportation. Full article

Compared to fossil fuel-based internal combustion vehicles, electric vehicles with lower local pollution and noise are becoming more and more popular in urban logistic distribution. When electric vehicles are involved, high-quality delivery depends on energy consumption. This research proposes an electric vehicle routing problem considering time windows under energy consumption uncertainty. A mixed-integer programming model is established. The robust optimization method is adopted to deal with the uncertainty. Based on the modification of adaptive large neighborhood search algorithm, a metaheuristic procedure, called novel hybrid adaptive large neighborhood search, is designed to solve the problem, and some new operators are proposed. The numerical experiments show that the proposed metaheuristic can obtain high-performance solutions with high efficiency for large-scale instances. Furthermore, the robust solution based on the proposed model can achieve a satisfactory tradeoff between performance and risk. Full article