A new method to improve passenger vehicle safety using intelligent functions in active suspension system

In this research a new electronic based mechanism for vehicle suspension system is designed. The aims are to improve passengers’ safety and comfort. The proposed system is developed for proactive rapid reaction of suspension system which can readjust the height of chassis while confronting with wrong conditions of driving such as unflatted road, rainy or snowy road profile. The results show that the proposed mechanism can successfully increase the stability of the car by readjusting the height of the the chassis and center of the gravity of vehicle while turning

Development of vertical movement stabilizer for vehicle active suspension system

An advanced suspension system recommends more handling, safety, comfortable ride and fast reaction. Such a system is capable to scan changing of road profile and its conditions and also monitor factors such as car speed and steering wheel angle. Application of different sensors such as laser sensor or stereo camera for scanning road profile and different types of mechanical mechanism such as hydro pneumatic or hydraulic system for readjusting axles are challenges between car manufacturers. In this research, a new method is proposed for improving the vertical movement mechanism in suspension system of vehicles. The proposed method is based on vertical movement mechanism which is equipped with a monitoring system to scan the road profile for stabilizing car chassis. This system will analyze steering wheel angle, weather conditions, the distance between cars during accident and car speed in order to adjust the height of the car’s axles accordingly. Such mechanism can increase comfortability, car handling stability and safety by reducing the vertical movements. Besides that, the proposed system will increase the safety of the passengers by reducing vertical movements that may cause car’s imbalance or rollover during driving. A prototype of a vertical movement model was developed to simulate its function during different driving profiles and conditions. The purpose of the simulation based on the half-car model is to utilize the function performance of the device in a laboratory scale. It is worth to note that the proposed method successfully reduces the vertical movement up to 33% compared to normal suspension system. While the model reduces the impact during an accident of 44% for contact between car bumpers. In summary the system successfully adjusts the height of axles according to car speed, weather condition and steering wheel angle to prevent of rollover of the car. This system can be used in the automotive industry for passenger cars and light trucks. Furthermore, with some modifications, this system can be applied in mass transit, aerospace industry and marine transportation

A supervised method for scheduling multi-objective job shop systems in the presence of market uncertainties

In real industries, managers usually consider more than one objective in scheduling process. Minimizing completion time, operational costs and average of machine loads are amongst the main concerns of managers during production scheduling in practice. The purpose of this research is to develop a new scheduling method for job-shop systems in the presence of uncertain demands while optimizing completion time, operational costs and machine load average are taken into account simultaneously. In this research a new multi-objective nonlinear mixed integer programming method is developed for job-shop scheduling in the presence of product demand uncertainty. The objectives of the proposed method are minimizing cost, production time and average of machine loads index. To solve the model, a hybrid NSGA-II and Simulated Annealing algorithms is proposed where the core of the solving algorithm is set based on weighting method. In continue a Taguchi method is set for design of experiments and also estimate the best initial parameters for small, medium and large scale case studies. Then comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approaches in terms of the quality of the solutions and the solving times. The outcomes are then compared with a classic Genetic Algorithm. The outcomes indicated that the proposed algorithm could successfully solve large-scale experiments less than 2 min (123 s) that is noticeable. While performance of the solving algorithm are taken into consideration, the proposed algorithm could improve the outcomes in a range between 9.07% and 64.96% depending on the input data. The results also showed that considering multi-objective simultaneously more reasonable results would be reached in practice. The results showed that the market demand uncertainty can significantly affect to the process of job shop scheduling and impose harms in manufacturing systems both in terms of completion time and machine load variation. Operational costs, however, did not reflect significantly to market demand changes. The algorithm is then applied for a manufacturing firm. The outcomes showed that the proposed algorithm is flexible enough to be used easily in real industries

A supervised method for scheduling multi-objective job shop systems in the presence of market uncertainties

In real industries, managers usually consider more than one objective in scheduling process. Minimizing completion time, operational costs and average of machine loads are amongst the main concerns of managers during production scheduling in practice. The purpose of this research is to develop a new scheduling method for job-shop systems in the presence of uncertain demands while optimizing completion time, operational costs and machine load average are taken into account simultaneously. In this research a new multi-objective nonlinear mixed integer programming method is developed for job-shop scheduling in the presence of product demand uncertainty. The objectives of the proposed method are minimizing cost, production time and average of machine loads index. To solve the model, a hybrid NSGA-II and Simulated Annealing algorithms is proposed where the core of the solving algorithm is set based on weighting method. In continue a Taguchi method is set for design of experiments and also estimate the best initial parameters for small, medium and large scale case studies. Then comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approaches in terms of the quality of the solutions and the solving times. The outcomes are then compared with a classic Genetic Algorithm. The outcomes indicated that the proposed algorithm could successfully solve large-scale experiments less than 2 min (123 s) that is noticeable. While performance of the solving algorithm are taken into consideration, the proposed algorithm could improve the outcomes in a range between 9.07% and 64.96% depending on the input data. The results also showed that considering multi-objective simultaneously more reasonable results would be reached in practice. The results showed that the market demand uncertainty can significantly affect to the process of job shop scheduling and impose harms in manufacturing systems both in terms of completion time and machine load variation. Operational costs, however, did not reflect significantly to market demand changes. The algorithm is then applied for a manufacturing firm. The outcomes showed that the proposed algorithm is flexible enough to be used easily in real industries