This manuscript proposes a hybrid method to solve the job shop scheduling problem (JSP). Here, the machine consumes different amounts of energy for processing the tasks. The proposed method is the joint execution of Feedback Artificial Tree (FAT) and Atomic Orbital Search (AOS), hence it is called the FAT-AOS method. The aim of the proposed multi-objective method is to lessen the non-processing energy consumption (NEC), total weighted tardiness and earliness (TWET), and makespan (Cmax). Depending on the machine’s operating status, such as working, standby, off, or idle, the energy-consumption model of the machine is constructed. The NEC is the essential metric and the Cmax and TWET are the classical performance metrics used to predict the effects of energy effectiveness in JSP. The proposed AOS technique optimizes the objective of the system and FAT is used to predict the optimal outcome. The proposed method’s performance is implemented in MATLAB and is compared with various existing methods. From this simulation, under the 15x15_1 instance, the proposed method makes the span the best value of 1370, the median is 1720, and the worst value become 2268 is obtained.

This paper presents the design and implementation of wireless-based DC motor controlling and monitoring system. The details of controlling the direction and speed of the DC motor wirelessly are furnished. The directions of the DC motor are controlled using the power semiconductor switches and the speed is controlled wirelessly at the transmitter. In addition to this, the overload factor of the DC motor is also determined to avoid the functional impairments of the DC motor. The implementation of the above method includes amplitude shift keying, encoder, decoder, MSP430 controller and H-bridge converter.