In this study, a systematic review on production scheduling based on reinforcement learning (RL) techniques using especially bibliometric analysis has been carried out. The aim of this work is, among other things, to point out the growing interest in this domain and to outline the influence of RL as a type of machine learning on production scheduling. To achieve this, the paper explores production scheduling using RL by investigating the descriptive metadata of pertinent publications contained in Scopus, ScienceDirect, and Google Scholar databases. The study focuses on a wide spectrum of publications spanning the years between 1996 and 2024. The findings of this study can serve as new insights for future research endeavors in the realm of production scheduling using RL techniques.

In this study, powder metallurgy process has been utilized to produce the composite materials. Pure aluminium (Al) has been selected as the matrix material for the preparation of composite materials. 4% of magnesium (Mg) has been added as the alloying element, whilst molybdenum disulphide (MoS₂) is reinforced with a varying wt.% (2, 4 & 6). The intent of this work is, to evaluate some basic mechanical properties (density, microhardness, compression strength and toughness), surface degradation properties (corrosion and wear), and electrical conductivity of the fabricated composite materials. Moreover, SEM mapping and EDAX analysis have been conducted to confirm the presence of reinforcement particles with homogenous distribution in the matrix material and to do the fractographic study of the compressive strength tested samples. The density of the composite material Al-4% Mg-6%MoS₂ has been increased compared to the density of pure Al material. Micro Vickers hardness test shows that Al-4% Mg-6%MoS₂ composite has 32.86% more hardness as compared to that of pure Al material. Compressive strength of the composite material with the higher wt.% of MoS₂ is found to be 181.81 N/mm², while Al material has only 167.52 N/mm². The buckling formation during the compression test is avoided in the composite material owing to the existence of MoS₂ particulates. The wear loss of composite materials is found low as compared with the unreinforced Al material, owing to the solid lubricant property of MoS₂ particles, and hence, coefficient of friction (COF) is also lessened with the increase in the MoS₂ wt.%. Also, the MoS₂ reinforced materials show good resistance to corrosion due to the presence of molybdenum, which acts as a consistent layer obstacle to prevent the surface from further degradation.

This paper presents an investigation into the enhancement of availability of a curing machine deployed in the rubber industry, located in Tamilnadu in India. Machine maintenance is a major task in the rubber industry, due to the demand for product. Critical component identification in curing machines is necessary to prevent rapid failure followed by subsequent repairs that extend curing machine downtime. A reward in the Reinforcement Learning Algorithm (RLA) prevents frequent downtime by improving the availability of the curing machine at time when unscheduled long-term maintenance would interfere with operation, due to the occurrence of unspecified failure to a critical component. Over time, depreciation and degradation of components in a machine are unavoidable, as is shown in the present investigation through intelligent assessment of the lifespan of components. So far, no effective methodology has been implemented in a real-time maintenance environment. RLAs seem to be a more effective application when it is based on intelligent assessment, which encompasses the failure and repair rate used to calculate availability in an automated environment. Training of RLAs is performed to evaluate overall equipment efficiency (OEE) in terms of availability. The availability of a curing machine in the form of state probability is modeled in first-order differential-difference equations. RLAs maximize the rate of availability of the machine. Preventive maintenance (PM) rate for four modules of 16 curing machines is expressed in a transition diagram, using transition rate. Transition rate indicates the degree of PM and unplanned maintenance rates that defines the total availability of the four modules. OEE is expressed in terms of the availability of curing machines, which is related to performance and quality. The results obtained by RLA are promising regarding short-term and long-term efficiencies of OEE, which are 95.19% and 83.37%, respectively.