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Nowadays, automotive engines are controlled by electronic control units (ECUs), and the engine idle speed performance is significantly affected by the setup of control parameters in the ECU. The engine ECU tune‐up is done empirically through tests on a dynamometer (dyno). In this way, a lot of time, fuel and human resources are consumed, while the optimal control parameters may not be obtained. The purpose of this paper is to propose a novel ECU setup optimization approach for engine idle speed control.

In the first phase of the approach, Latin hypercube sampling (LHS) and a multi‐input/output least squares support vector machine (LS‐SVM) is proposed to build up an engine idle speed model based on dyno test data, and then a genetic algorithm (GA) is applied to obtain optimal ECU setting automatically subject to various user‐defined constraints.

The study shows that the predicted results using the estimated model from LS‐SVM are in good agreement with the actual test results. Moreover, the optimization results show a significant improvement on idle speed performance in a test engine.

As the methodology is generic it can be applied to different vehicle control optimization problems.

The research is the first attempt to integrate a couple of paradigms (LHS, multi‐input/output LS‐SVM and GA) into a general framework for constrained multivariable optimization problems under insufficient system information. The proposed multi‐input/output LS‐SVM for modelling of multi‐input/output systems is original, because the traditional LS‐SVM modelling approach is suitable for multi‐input, but single output systems. Finally, this is the first use of the novel integrated framework for automotive engine idle‐speed control optimization.

The nose length is the key design parameter affecting the aerodynamic performance of high-speed maglev train, and the horizontal profile has a significant impact on the aerodynamic lift of the leading and trailing cars Hence, the study analyzes aerodynamic parameters with multi-objective optimization design.

The nose of normal temperature and normal conduction high-speed maglev train is divided into streamlined part and equipment cabin according to its geometric characteristics. Then the modified vehicle modeling function (VMF) parameterization method and surface discretization method are adopted for the parametric design of the nose. For the 12 key design parameters extracted, combined with computational fluid dynamics (CFD), support vector machine (SVR) model and multi-objective particle swarm optimization (MPSO) algorithm, the multi-objective aerodynamic optimization design of high-speed maglev train nose and the sensitivity analysis of design parameters are carried out with aerodynamic drag coefficient of the whole vehicle and the aerodynamic lift coefficient of the trailing car as the optimization objectives and the aerodynamic lift coefficient of the leading car as the constraint. The engineering improvement and wind tunnel test verification of the optimized shape are done.

Results show that the parametric design method can use less design parameters to describe the nose shape of high-speed maglev train. The prediction accuracy of the SVR model with the reduced amount of calculation and improved optimization efficiency meets the design requirements.

Compared with the original shape, the aerodynamic drag coefficient of the whole vehicle is reduced by 19.2%, and the aerodynamic lift coefficients of the leading and trailing cars are reduced by 24.8 and 51.3%, respectively, after adopting the optimized shape modified according to engineering design requirements.

Printed wiring board placement optimization of high‐speed placement machines in a high volume surface mount line was studied based on more careful analysis of the board layout beforehand. In this method, the target is to get the board area to be assembled as small as possible in each successive placement machine, paying the most attention on component coordinates already during preliminary line balancing phase. Optimization and line balancing principles, that had showed promising results already in the first studies when compared to globally used well‐known commercial optimization systems, were now further developed. Results presented and illustrated in this paper show remarkable, comparable, improvement in both cycle time reduction and in balance of the whole surface mount line. The main difference between this method and the advanced commercial solutions is explained in detail. Board layout, feeder arrangement, sequence of fiducial registration, and XY‐table movement were taken into deeper consideration in the method.

With the increasing awareness of global warming and the important role of last mile distribution in logistics activities, the purpose of this paper is to build an environmental and effective last mile distribution model considering fuel consumption and greenhouse gas emission, vehicle capacity and two practical delivery service options: home delivery (HD) and pickup site service (PS). This paper calls the problem as the capacitated pollution-routing problem with pickup and delivery (CPRPPD). The goal is to find an optimal route to minimize operational and environmental costs, as well as a set of optimal speeds over each arc, while respecting capacity constraints of vehicles and pickup sites.

To solve this problem, this research proposes a two-phase heuristic algorithm by combining a hybrid ant colony optimization (HACO) in the first stage and a multiple population genetic algorithm in the second stage. First, the HACO is presented to find the minimal route solution and reduce distribution cost based on optimizing the speed over each arc.

To verify the proposed CPRPPD model and algorithm, a real-world instance is conducted. Comparing with the scenario including HD service only, the scenario including both HD and PS option is more economical, which indicates that the CPRPPD model is more efficient. Besides, the results of speed optimization are significantly better than before.

The developed CPRPPD model not only minimizes delivery time and reduces the total emission cost, but also helps logistics enterprises to establish a more complete distribution system and increases customer satisfaction. The model and algorithm of this paper provide optimal support for the actual distribution activities of logistics enterprises in low-carbon environment, and also provide reference for the government to formulate energy-saving and emission reduction policies.

This paper provides a great space for the improvement of carbon emissions in the last mile distribution. The results show that the distribution arrangement including HD and PS services in the last mile adopting speed optimization can significantly reduce the carbon emission. Additionally, an integrated real-world instance is applied in this paper to illustrate the validity of the model and the effectiveness of this method.

This paper aims to minimize aircraft fuel consumption during the cruise phase when the flight is subjected to a specific time of arrival for different weights and distances.

The approach adopted herein uses sequential quadratic programming algorithm from MATLAB optimization toolbox, which includes a mathematical model of a jet airliner based on the Base of Aircraft Data as a function evaluator, to find out the impact of meet-time of arrival constraints on fuel consumption. The cruising speeds at predefined segments and the altitude are defined as the design variables.

The algorithm determines the optimum cruise altitudes and speeds for minimum fuel consumption in the case of no time constraints, also, for different time constraints where the flight time shall be reduced by increasing speed and lowering the altitude in most of the investigated cases.

The algorithm computes the optimum speed and the altitude according to different flight scenarios with the meet-time of arrival constraints for minimum fuel consumption which affects the direct operating cost of the flight. The algorithm might greatly help in decision-making for the meet-time of arrival operations.

Developing an algorithm to optimize the speed and the altitude of an aircraft based on weight and range for minimization of fuel consumption. It is a pioneer study in the literature that deals with the effect of meet-time constraints on fuel consumption.

Regenerative braking is an efficient energy saving technology in urban rail system, in which the recovery energy from braking trains is collected by some equipments and released to accelerating trains when needed. However, the high cost and low lifetime of storage devices prevent the widespread use of this technology. The purpose of this paper is to conduct thorough cost-benefit analysis to facilitate China’s urban rail companies to make decisions on the use of such technology.

To evaluate the benefit from regenerative energy storage, the authors formulate an improved integrated scheduling and speed control model to calculate the net energy consumption associated with different energy recovery rates and then define the benefit as the amount of energy saving arising from the usage of storage equipments. With the frequent charge/discharge operations on storage equipments, the energy recovery rate generally decreases which lowers the benefit, but the maintenance cost increases. By trading-off benefit and cost, the authors derive the optimal scrapping time, the maximum profit and the profitability condition for storage devices.

Simulation studies based on the Beijing Metro Yizhuang Line of China are given. The results show that compared with the current timetable and speed profile, the integrated scheduling and speed control approach with energy recovery rate of 0.5 can reduce the net energy consumption by 12.69 per cent; the net energy consumption can be well approximated as a linear function of energy recovery rate; and the maximum profit and the optimal scrapping time on regenerative energy storage devices are both positively related to the electricity price. The allowance proportion and the number of service trains such that busy lines with higher electricity price or allowance proportion have advantages to use the regenerative energy storage devices.

In this work, a linear energy recovery rate and a linear maintenance cost are used in the cost-benefit analysis process. In future research, the more accurate expressions on energy recovery rate and maintenance cost should be considered if more data on recovery rate and maintenance cost can be gathered.

The main values of this paper are to develop the integrated optimization approaches for train scheduling and speed control and, on this basis, make thorough cost-benefit analysis for regenerative energy storage to improve the operations management of urban rail transit.

The paper aims at optimizing magnetic thrusts in the framework of the design of high‐dynamics linear actuators. The goal is to find the optimal topology of the permanent magnets in order to maximize the velocity of the actuator.

The optimization is performed by a topology optimization method. The design space is divided in cells in which the method have to distribute permanent magnets and determine their magnetization directions.

Several aspects of the optimization are discussed in the paper, such as the effect of the introduction of a weight constraint on the thrust. Some issues are highlighted regarding the length of design space for the moving part and the presence of local minimizers in the optimization problem.

Having different magnetization directions in each cell makes the manufacturing harder. The results could thus be completed either by the design of a system able to create such permanent magnets or by the introduction of a constraint limiting the number of magnetization directions.

Finding the optimal topology of magnetic thrusts is motivated by the interest in avoiding the shocks related to mechanical thrusts.

This paper applies the topology optimization approach for the design of magnetic thrusts in order to increase the performances of high‐dynamics linear actuators.

This paper aims to identify the interplay of standard Capesize optimal speeds for time charter equivalent (TCE) maximization in the Australia–China iron ore route and the optimal speeds as an operational tool for compliance with the International Maritime Organization (IMO) carbon intensity indicator (CII).

The TCE at different speeds have been calculated for four standard Capesize specifications: (1) standard Capesize with ecoelectronic engine; (2) standard Capesize with non-eco engine (3) standard Capesize vessel with an eco-electronic engine fitted with scrubber and (4) standard Capesize with non-eco engine and no scrubber fitted.

Calculations imply that in a highly inflationary bunker price context, the dollar per ton freight rates equilibrates at levels that may push optimal speeds below the speeds required for minimum CII compliance (C Rating) in the Australia–China trade. The highest deviation of optimal speeds from those required for minimum CII compliance is observed for non-eco standard Capesize vessels without scrubbers. Increased non-eco Capesize deployment would see optimal speeds structurally lower at levels that could offer CII ratings improvements.

While most of the studies have covered the use of speed as a tool to improve efficiency and emissions in the maritime sector, few have been identified in the literature to have examined the interplay between the commercial and operational performance in the dry bulk sector stemming from the freight market equilibrium. The originality of this paper lies in examining the above relation and the resulting optimal speed selection in the Capesize sector against mandatory environmental targets.

This paper aims to propose a train timetable rescheduling (TTR) approach from the perspective of multi-train tracking optimization based on the mutual spatiotemporal information in the high-speed railway signaling system.

Firstly, a single-train trajectory optimization (STTO) model is constructed based on train dynamics and operating conditions. The train kinematics parameters, including acceleration, speed and time at each position, are calculated to predict the arrival times in the train timetable. A STTO algorithm is developed to optimize a single-train time-efficient driving strategy. Then, a TTR approach based on multi-train tracking optimization (TTR-MTTO) is proposed with mutual information. The constraints of temporary speed restriction (TSR) and end of authority are decoupled to calculate the tracking trajectory of the backward tracking train. The multi-train trajectories at each position are optimized to generate a time-efficient train timetable.

The numerical experiment is performed on the Beijing-Tianjin high-speed railway line and CR400AF. The STTO algorithm predicts the train’s planned arrival time to calculate the total train delay (TTD). As for the TSR scenario, the proposed TTR-MTTO can reduce TTD by 60.60% compared with the traditional TTR approach with dispatchers’ experience. Moreover, TTR-MTTO can optimize a time-efficient train timetable to help dispatchers reschedule trains more reasonably.

With the cooperative relationship and mutual information between train rescheduling and control, the proposed TTR-MTTO approach can automatically generate a time-efficient train timetable to reduce the total train delay and the work intensity of dispatchers.

This study aims to decrease the aerodynamic drag of the body of revolution at supersonic speeds. Supersonic area rule is widely used in modern supersonic aircraft design. Further reduction of the aerodynamic drag is possible in the framework of Euler and Reynolds averaged Navier–Stokes (RANS) equations. Sears–Haack body of revolution shape variation, which decreased its aerodynamic drag in compressible inviscid and viscous gas flow at Mach number of 1.8 under constraint of the volume with lower bound equal to volume of initial body, was numerically investigated.

Calculations were carried out in two-dimensional axisymmetric mode in the framework of Euler and RANS with SST model with compressibility correction equations at structured multiblock meshes. Variation of the radius as function of the longitudinal coordinate was given as a polynomial third-order spline through five uniformly distributed points. Varied parameters were increments of the radius of the body at points that defined spline. Drag coefficient was selected as an objective function. Parameter combinations corresponding to the objective function minimum under volume constraint were obtained by mixed-integer sequential quadratic programming at second-order polynomial response surface and IOSO algorithm.

Improving variations make front part of the body become slightly blunted, transfer part of volume from front part of the body to back part and generate significant back face. In the framework of RANS, the best variation decreases aerodynamic drag by approximately 20 per cent in comparison with Sears–Haack body.

The results can be applied for the aerodynamic design of the bullets and projectiles. The second important application is knowledge of the significance of the difference between linearized slender body theory optimization results and optimization results obtained by modern computational fluid dynamics (CFD) optimization techniques.

Knowledge about the magnitude of the difference between linearized slender body theory optimization results and optimization results obtained by modern CFD optimization techniques can stimulate further research in related areas.

The optimization procedure and optimal shapes obtained in the present work are directly applicable to the design of small aerodynamic drag bodies.