Fuzzy Scheduling Problem of Vessels in One-Way Waterway

Effective use of port waterways is conducive to enhancing port competitiveness. To minimize the waiting time of ships, improve traffic efficiency, and enhance the applicability of the model to the presence of uncertain factors, a fuzzy scheduling optimization method for ships suitable for one-way waterways is proposed based on fuzzy theory. Considering the ambiguity of the speed of ships entering and exiting the port or the time it takes to cross the channel, the previous research on vessel scheduling on one-way waterways has been extended by introducing a triangular fuzzy number and a method for determining the feasible navigable time window of a ship subject to the tide height constraint was proposed. In this study, the genetic algorithm is used to construct the mathematical model for solving fuzzy vessel scheduling problems based on time optimization, and the minimum delay strategy is used to determine the service sequence. Then, the parameters setting are discussed in detail to find the optimal settings. Finally, an experimental comparative analysis of the randomly generated cases was conducted based on the simulated data. The results show that the designed fuzzy vessel scheduling algorithm reduces the dependence on the port environment, is versatile, and can effectively improve the efficiency of ship schedules and traffic safety compared to other methods. Moreover, it can avoid the problem of the illegal solution occurring in the manual scheduling method.

Optimal Fuzzy Scheduling and Sequencing of Work-Intensive Multiple Projects Under Normal and Unexpected Events

This research proposed optimization models for task scheduling and sequencing in work-intensive multiple projects under normal and unexpected events. The objectives of scheduling model were minimizing the total overtime/under-time costs and maximizing satisfaction values on tasks due dates and processing standard times. Further, the sequencing model aimed to minimize the sum of tasks' start times, maximize resource utilization, and maximize satisfaction on project completion times. Illustrations of the proposed scheduling and sequencing optimization models were provided where the results showed effective scheduling and sequencing of project tasks at minimal costs and achieved the desired satisfaction levels on tasks and projects and significantly enhanced resource efficiency at minimal overtime and under-time costs. Further, optimization models were modified to deal with unexpected events. In conclusion, the proposed models may support project managers in planning project tasks in a cost-effective manner under normal and unexpected events.

Real-Time Data Scheduling of Flexible Job in Papermaking Workshop Based on Deep Learning and Improved Fuzzy Algorithm

The traditional real-time data scheduling method ignores the optimization process of job data that leads to delayed delivery, high inventory cost, and low utilization rate of equipment. This paper proposes a novel real-time data scheduling method based on deep learning and an improved fuzzy algorithm for flexible operations in the papermaking workshop. The algorithm is divided into three parts: the first part describes the flexible job shop scheduling problem; the second part constructs the fuzzy scheduling model of flexible job data in papermaking workshop; and finally the third part uses a genetic algorithm to obtain the optimal solution of fuzzy scheduling of flexible job data in papermaking workshop. The results show that the optimal solution is obtained in 48 seconds at the 23rd attempt (iteration) under the application of the proposed method. This result is much better than the three traditional scheduling methods with which we compared our results. Hence, this paper improves the work efficiency and quality of papermaking workshop and reduces the operating cost of the papermaking enterprise.

Control-Scheduling Codesign for NCS based Fuzzy Systems

In the present paper, a fuzzy codesign approach is proposed to deal with the controller and scheduler design for a networked control system which is physically distributed with a shared communication network. The proposed fuzzy controller is applied to generate the control with different sampling-actuation periods, the configuration supposes a strict actuation period disappears the jitter. The proposed fuzzy scheduling is designed to select the sampling-actuation period. So, the fuzzy codesign reduces the rate of transmission when the system is stable through the scheduler while the controller adjusts the control signal. The fuzzy codesign guarantees the stability of all the system if the network uncertainties do not exceed an upper bound and is a low computational cost method implemented with an embedded system. An unstable, nonlinear system is used to evaluate the proposed approach and compared to a hybrid control, the results show greater robustness to multiple lost packets and time delays much larger than the sampling period. (This paper is an extension of [20]. Reprinted (partial) and extended, with permission based on License Number 4275590998661 IEEE, from "Electrical Engineering, Computing Science and Automatic Control, 2017 14th International Conference on")