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With the development of logistics enterprises and the adjustment of some relevant laws and regulations, the profit space of vehicle logistics enterprises has been further compressed. To reduce vehicle logistics transportation cost and increase the profit space of vehicle logistics, the vehicle logistics multimodal transport network is constructed and the graph traversal algorithm is used to screen the feasible paths in the vehicle logistics multimodal transport network. Then, the Tabu search algorithm can optimize vehicle logistics multimodal transport route model. Results showed that Tabu search performed better than other methods in solving route optimization problems. The cost of Tabu search algorithm after convergence was 1.2 yuan/km × per set. The performance of Tabu search algorithm on NGSIM data set was better than other methods. On this data set, the area under the curve of Tabu search algorithm was much higher than that of other methods. The optimization results of Tabu search for vehicle logistics intermodal routes were effective. Among the 15 routes, only four routes were not optimized, and other routes were optimized. After optimization, the profits have increased, and the profit of Route 9 had the largest increase, which was 18%. The research successfully constructs the optimization model of vehicle logistics intermodal route, and completes the solution to increase the profit space of vehicle logistics enterprises.

An improved particle swarm optimization (PSO) algorithm is presented by dynamically adjusting the inertia weight in the iterative process of PSO, and it is used to solve the problem of logistics route optimization. This algorithm can not only improve the convergence speed, but also avoid falling into local optimum. In the process of improving the standard algorithm, two methods are proposed to adjust the inertia weight value according to the number of iterations. One is piecewise linear decreasing, another is linear decreasing. The results show that linear decline is better than piecewise linear decline to achieve the purpose of optimization, which is more conducive to accelerate the convergence rate and enhance the ability of optimization. Through the simulation experiment of the specific vehicle routing optimization problem, the results show that after the improvement, the optimization performance is enhanced, the optimization speed is accelerated, and the complexity is not increased, which greatly improves the performance of the original algorithm.

Public transport systems play a crucial role in the development of large cities. Bus network design to optimize passenger flow coverage in a global metropolis is a challenging task. As an essential part of bus travel planning, considering the bus transfer factor in the existing extremely complex and extensive public bus network usually leads to a optimization problem characterized by high-dimensionality and non-linearity. While classical computers struggle to deal with this kind of problems, quantum computers shed new light into this field. The coherent Ising machine (CIM), a specialized optical quantum computer using a photonic dissipative architecture, has shown its remarkable computational power in combinatorial optimization problems. We construct the classical model and the quadratic unconstrained binary optimization (QUBO) model of the bus route optimization problem, and solve it using a classical computer and CIM, respectively. Our experimental results demonstrate the significant acceleration capability of CIM over classical computers in finding the optimal or near-optimal solutions, albeit subject to the hardware limitations of the 100-qubit CIM.

Efficient sales route optimization is a critical challenge in logistics and distribution, especially under real-world conditions involving traffic variability and dynamic constraints. This study proposes a novel Hybrid Genetic Algorithm (GAAM-TS) that integrates Adaptive Mutation, Tabu Search, and an LSTM-based travel time prediction model to enable real-time, intelligent route planning. The approach addresses the limitations of traditional genetic algorithms by enhancing solution quality, maintaining population diversity, and incorporating data-driven traffic estimations via deep learning. Experimental results on real-world data from the NYC Taxi dataset show that GAAM-TS significantly outperforms both Standard GA and GA-AM variants, achieving up to 20% improvement in travel efficiency while maintaining robustness across problem sizes. Although GAAM-TS incurs higher computational costs, it is best suited for offline or batch optimization scenarios, whereas GA-AM provides a balanced alternative for near-real-time applications. The proposed methodology is applicable to last-mile delivery, fleet routing, and sales territory management, offering a scalable and adaptive solution. Future work will explore parallelization strategies and multi-objective extensions for sustainability-aware routing.

Formulating effective typhoon avoidance routes is crucial for ensuring the safe navigation of ocean-going vessels. From a maritime safety perspective, this paper investigates ship route optimization under typhoon forecast uncertainty. Initially, the study calculates the probability of a ship encountering a typhoon based on the distribution of historical typhoon data within the radius of seven-level winds and the distance between the ship and the typhoon. Subsequently, the minimum safe distance is quantified, and a multi-objective ship route optimization model for typhoon avoidance is established. A three-dimensional multi-objective ant colony algorithm is designed to solve this model. Finally, a typhoon avoidance simulation experiment is conducted using Typhoon TAMRI and a classic route in the South China Sea as a case study. The experimental results demonstrate that under adverse conditions of uncertain typhoon forecasts, the proposed multi-objective typhoon avoidance route optimization model can effectively avoid high wind and wave areas of the typhoon while balancing and optimizing multiple navigation indicators. This model can serve as a reference for shipping companies in formulating typhoon avoidance strategies.

The logistics landscape in e-commerce is undergoing a profound transformation toward sustainability and autonomy. This paper explores the implementation of autonomous maritime and last-mile transportation solutions to optimize the entire logistics chain from factory to customer. Building on the lessons learned from the maritime industry’s digital transformation, the study identifies key features and proposes a forward-looking autonomous maritime and last-mile transportation system. Emphasizing the role of geospatial technologies, the proposed system employs GIS-based electronic route optimization for efficient goods delivery, integrating onboard and ashore GIS-based sensors for enhanced location precision. A case study was built to analyze the implementation of autonomous means of transport along the route of a product from factory to customer. The integration of autonomous systems shows substantial improvements in logistics performance. Synchromodal logistics and smart steaming techniques can be utilized to optimize transportation routes, resulting in reduced fuel consumption and emissions. The findings reveal that autonomous maritime and last-mile transport systems can significantly enhance the efficiency, flexibility and sustainability of e-commerce logistics. The study emphasizes the need for advanced technological integration and provides a comprehensive framework for future research and practical applications in the logistics industry.

Vehicle system models can be roughly divided into two categories, dynamic and steady-state (or quasi-steady-state) models, and can be applied to evaluate vehicle transient performance such as vehicle longitudinal and lateral dynamics, as well as energy economies like fuel or electricity consumption. This paper reviews various energy consumption models for automotive systems, focusing on component- and vehicle-level models. As the foundation to calculate the energy consumption, powertrain component models of three main vehicle types (internal combustion engine (ICE) vehicles, electric vehicles (EVs), and hybrid vehicles) are reviewed with their key components, including internal combustion engines, electric motors, and batteries. Three types of vehicle energy consumption models are explored according to their interpretability: white-box, black-box, and grey-box models. Optimizing vehicle energy usage based upon a vehicle energy consumption model is reviewed from the aspects of eco-driving and eco-routing problems at the end of the paper. Eco-driving research primarily selects models focusing on transient performance; whereas eco-routing focuses on steady-state or quasi-steady-state conditions to balance the needs of model accuracy and calculation efficiency for real-time applications. This review aims to guide model selection and inspire future applications of energy consumption models for advancing sustainable automotive technologies.

Purpose The purpose of this paper is to create a flight route optimization for all flights that aims to minimize the total cost consists of fuel cost, ground delay cost and air delay cost over the fixed route and free route airspaces. Design/methodology/approach Efficient usage of current available airspace capacity becomes more and more important with the increasing flight demands. The efficient capacity usage of an airspace is generally in contradiction to optimum flight efficiency of a single flight. It can only be achieved with the holistic approach that focusing all flights over mixed airspaces and their routes instead of single flight route optimization for a single airspace. In the scope of this paper, optimization methods were developed to find the best route planning for all flights considering the benefits of all flights not only a single flight. This paper is searching for an optimization to reduce the total cost for all flights in mixed airspaces. With the developed optimization models, the determination of conflict-free optimum routes and delay amounts was achieved with airway capacity and separation minimum constraints in mixed airspaces. The mathematical model and the simulated annealing method were developed for these purposes. Findings The total cost values for flights were minimized by both developed mathematical model and simulated annealing algorithm. With the mathematical model, a reduction in total route length of 4.13% and a reduction in fuel consumption of 3.95% was achieved in a mixed airspace. The optimization algorithm with simulated annealing has also 3.11% flight distance saving and 3.03% fuel consumption enhancement. Research limitations/implications Although the wind condition can change the fuel consumption and flight durations, the paper does not include the wind condition effects. If the wind condition effect is considered, the shortest route may not always cause the least fuel consumption especially under the head wind condition. Practical implications The results of this paper show that a flight route optimization as a holistic approach considering the all flight demand information enhances the fuel consumption and flight duration. Because of this reason, the developed optimization model can be effectively used to minimize the fuel consumption and reduce the exhaust emissions of aircraft. Originality/value This paper develops the mathematical model and simulated annealing algorithm for the optimization of flight route over the mixed airspaces that compose of fixed and free route airspaces. Each model offers the best available and conflict-free route plan and if necessary required delay amounts for each demanded flight under the airspace capacity, airspace route structure and used separation minimum for each airspace.

Effective waste management is one of the major elements of urban sustainability, more so in rapidly growing cities like Dubai. This paper presents an overview of the development of a Smart Waste Management System (SWMS) that integrates Geographic Information System (GIS) technology with waste route optimization algorithms and adaptive demand management strategies. The system has four major components: (1) a mobile field application to add and modify collection points in real time; (2) a route optimization module that minimizes travel distance and CO₂ emissions while accounting for real-world constraints; (3) an interactive dashboard for decision-makers to monitor analytics, visualize routes, and make real-time adjustments; and (4) a navigator app for truck drivers to follow optimized routes seamlessly. Furthermore, the system includes a new adaptive waste demand management module, which dynamically updates the demand for each collection point using real-time usage data, rather than being based on static assumptions of capacity. The effectiveness of the system was tested on a sample of 110 collection bins located in three different areas in Dubai. Preliminary results indicate that route optimization alone has achieved a reduction of 19.1% in CO₂ emissions, and further improvement is expected with full implementation of the adaptive demand management module. The findings highlight the potential of intelligent systems to significantly reduce the environmental and financial costs associated with municipal waste collection, paving the way for scalable deployment in other urban environments.

As the carrier of small-piece logistics, urban joint distribution has frequent and complex operations, lacks systematic management and planning, and has large optimization space. Enterprises should bear the social responsibility of reducing carbon emissions in the logistics industry. Using Company M as an example, this article examines the urban joint distribution problem from a low-carbon point of view to reduce carbon emissions. By deriving the carbon emission formula, we obtain the crucial component for resolving the issue—the kilogram kilometers of distribution operation—and develop a mathematical model to minimize carbon emissions. The strategy of delayed delivery is used in distribution optimization to lower the no-load rate, and a scoring mechanism is presented to assist in determining the distribution time and location. In terms of route optimization, the problems of traditional ant colony algorithms that cannot consider distribution energy consumption, cannot deal with load limitations, and have slow iteration speeds are solved by using the introduction of minimum energy consumption, employing k-means clustering, and setting up elite ants, respectively. Finally, numerical simulations are implemented using C and Python, and the proposed optimization scheme demonstrates a 33.5% reduction in total carbon emissions compared to Company M’s original distribution model. It has been proven that the method proposed in this article has a certain effect on reducing carbon emissions from urban joint distribution.