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In a highly automated Flexible Manufacturing System (FMS), optimal utilization and scheduling of resources and equipment are paramount. This necessity underpins the full utilization of automation capabilities, leading to increased productivity and minimized downtime. Efficient resource allocation and scheduling also contribute to better overall performance, allowing the FMS to meet production demands effectively while maintaining a high level of operational efficiency. In this paper, the job-shop production scheduling problem is studied which involve with the concurrent scheduling of jobs processing and mobile robot assignment for job transportation within FMS environment. The hybrid Genetic Algorithm and Tabu Search algorithm is proposed to solve the combinatorial NP-hard job-shop and mobile robot scheduling problem. The primary objective is to search for the best scheduling plan that includes job allocation and mobile robot assignment, aiming to minimize the overall time necessary to complete all tasks, also known as makespan, to the minimum as possible. The developed algorithm has been evaluated and compared with the classical (or standard) genetic algorithm and other hybrid GA (with Simulated Annealing (SA) algorithm) using two job datasets spanning from small to large-scale problems adopted from renowned benchmark job instances. The results of computer experiments substantiate the effectiveness of the proposed hybrid algorithm, showcasing superior-quality solutions with an approximate up to 2.86% and 3.55% improvements compared to the hybrid GA – SA and standard algorithm, respectively. The developed algorithm has been run and tested in the Matlab software environment.

This article aims to design a model for forecasting the number of vehicles arriving at the battery swap station (BSS). In our case, we study the relevance of the proposed approach given the rapid increase in electric vehicle users in Indonesia. Due to the vehicle electrification program from the government of Indonesia and the lack of supporting infrastructure, forecasting battery swap demands is very important for charging schedules. Forecasting the number of vehicles is done using machine learning with the long short-term memory (LSTM) method. The method is used to predict sequential data because of its ability to review previous data in addition to the current input. The result of the forecasting using the LSTM method yields a prediction score using the root-mean-square error (RMSE) of 2.3079 x 10-6 . The forecasted data can be combined with the battery charging model to acquire predicted hourly battery availability that can be processed further for optimization and scheduling.

This paper presents the implementation of an adaptive control approach to the ball and beam system (BBS). The dynamics of a BBS are non-linear, and in the implementation, the uncertainty of the system's parameters may occur. In this research, the linear state-feedback model reference adaptive control (MRAC) is used to synchronize the states of the BBS with the states of the given reference model. This research investigates the performance of the MRAC method for a linear system that is applied to a non-linear system or BBS. In order to get a faster states convergence response, we define the initial condition of the feedback gains. In addition, the feedback gains are limited to get less oscillation response. The results show the error convergence is improved for the different sets of the sinusoidal reference signal for the MRAC with modified feedback gains. The ball position convergence improvement of MRAC with modified feedback gains for sinusoidal reference with an amplitude of 0.25, 0.5, and 0.75 are 35.1 %, 36 %, and 52.4 %, respectively.

Instructional media in control systems typically requires a real plant as an element to be controlled. However, this real plant, which is costly to be implemented, can be replaced by a virtual plant implemented in a computer and modelled in such a way that it resembles the behavior of a real plant. This kind of set-up is widely termed as hardware-in-the-loop (HIL) simulation. HIL simulation is an alternative way to reduce the development cost. A virtual plant is easy to adjust to represent various plants or processes that are widely used in industry. This paper proposes a simple HIL simulation set-up designed as instructional media for design and testing a simple control system. The experimental result on DC motor control shows that HIL simulation dynamical response is similar to the real hardware response with a small average error on measured transient response, represented in 0.5 seconds difference in settling time and 7.43 % difference in overshoot. This result shows the efficacy of our HIL simulation set-up.

Several works have presented the Hadith on different digital platforms, ranging from websites to mobile apps. These works were successful in presenting the text of the Hadith to users, but this does not help them to answer any particular questions about religious matters. Therefore, in this work we propose a question-answering system that was built on a Hadith knowledge graph. To interpret the user questions correctly, we used the Levenshtein distance function, and for storing the Hadith in graph format we used Neo4J as the graph database. Our main findings were: (i) a knowledge graph is suitable for representing the Hadith and also for doing the reasoning task, and (ii) our proposed approach achieved 95% for top-1 accuracy.