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As one of the primary sources of carbon emissions, transportation sector has proposed various measures to reduce its carbon emissions. Introducing energy-efficient and low-carbon autonomous trucks into freight transportation is highly promising, but faces various challenges, especially safety issues. This study addresses eco-friendly lane reservation-based autonomous truck transportation network design for transportation safety and low carbon emissions. It aims to optimally implement dedicated truck lanes in an existing network and design dedicated routes for autonomous truck transportation to simultaneously minimise the negative impact caused by dedicated truck lanes and carbon emissions of the entire transportation system. We first formulate this problem into a bi-objective integer linear program. Then, an ϵ-constraint-based two-stage algorithm (ETSA) is proposed to solve it based on explored problem properties. A case study based on the well-known Sioux Falls network is conducted to demonstrate the applicability of the proposed model and algorithm. Computational results for 310 instances from the literature demonstrate that the proposed algorithm significantly outperforms the ϵ-constraint combined with the proposed ILP in obtaining the Pareto front. Moreover, helpful managerial insights are derived based on sensitivity analysis. [ABSTRACT FROM AUTHOR]

Automatic heavy-haul train (HHT) operation technology has recently received considerable attention in the field of rail transportation. In this paper, a discrete-time-based mathematical formulation is proposed to address the speed profile optimization problem in order to ensure the safe, efficient, and economical operation of heavy-haul trains (HHTs). Due to the presence of long and steep downgrades (LSDs) on some heavy-haul lines, the brake forces of the HHT are typically jointly determined by air braking and electric braking. The time characteristics of the air braking, such as the command delay and the change process caused by the air pressure, are taken into account, and then formulas are presented to calculate the air brake force. In addition, the influence of the neutral section on the control of the electric braking is considered via space-based constraints. The resulting problem is a nonlinear optimal control problem. To achieve linearization, auxiliary 0-1 binary variables and the big-M approach are introduced to transform the nonlinear constraints regarding slope, curve, neutral section, air brake force, and air-filled time into linear constraints. Moreover, piecewise affine (PWA) functions are used to approximate the basic resistance of the HHT. Finally, a mixed integer linear programming (MILP) model is developed, which can be solved by CPLEX. The experiments are carried out using data from a heavy-haul railway line in China, and the results show that the proposed approach is effective and flexible., Author(s): Huazhen Yu [1]; Yihui Wang [1]; Andrea D'Ariano [2]; Anzheng Lai [1]; Youneng Huang (corresponding author) [1,3] 1. Introduction Heavy-haul transportation is of great importance for improving transport efficiency, [...]

The article presents two distributed motion planning methods for dynamically coordinating multiple automated guide vehicles (AGVs) in an industrial setting, aiming to enhance their flexibility, robustness and scalability. A predictive motion model is utilized to describe the transport process mathematically as a dynamical system. Subsequently, alternating direction method of multipliers (ADMM)-based decomposition techniques, both serial and parallel, are developed to coordinate the AGVs and mitigate the computational burden. The efficacy of the distributed methods is verified through testing in industrial scenarios, demonstrating that they can significantly improve multi-AGV system transport productivity with minimal computational effort. Notably, the parallel scheme exhibits superior performance in coordinating the AGVs compared to the serial scheme. [ABSTRACT FROM AUTHOR]

The current hybrid flow shop task allocation usually assumes a static manufacturing environment, which cannot effectively handle uncertain events in the production process. To address this, the dynamic task allocation problem for machines in parallel with different speeds is studied, and a Mixed Logical Dynamical (MLD) model for predicting the state of the production system is established. A dynamic allocation method based on Model Predictive Control (MPC) is proposed. The proposed novel method integrates dynamic model with rolling optimization to better deal with uncertain events in production process by decomposing the overall planning problem into smaller local planning models. Numerical results show that the proposed method outperform the traditional global planning method and rule-based allocation method in terms of time and job processing rate. In addition, through the Plant Simulation software, the simulation results are consistent with the numerical results, which fully proves the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This study introduces a decision support framework that integrates aircraft trajectory optimization and arrival scheduling to facilitate efficient management of descent operations for arriving aircraft within terminal maneuvering areas. The framework comprises three modules designed to tackle specific challenges in the descent process. The first module formulates and solves a trajectory optimization problem, generating a range of candidate descent trajectories for each arriving aircraft. The options for descent operations include step-down descent operation, Continuous Descent Operation (CDO), and CDO with a lateral path stretching strategy. The second module addresses the assignment of conflict-free trajectories to aircraft, determining precise arrival times at each waypoint. This is achieved by solving an aircraft arrival scheduling problem. To overcome computational complexities, a novel variable neighborhood search algorithm is proposed as the solution approach. This algorithm utilizes three neighborhood structures within an extended relaxing and solving framework, and incorporates a tabu search algorithm to enhance the efficiency of the search process in the solution space. The third module focuses on comparing the total cost incurred from flight delays and fuel consumption across the three descent operations, enabling the selection of the most suitable operation for the descent process. The decision support framework is evaluated using real air traffic data from Guangzhou Baiyun International Airport. Experimental results demonstrate that the framework effectively supports air traffic controllers by scheduling more cost-efficient descent operations for arrival aircraft. [ABSTRACT FROM AUTHOR]