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Human life has become smarter by utilizing big data, telecommunication technologies, and wearable sensors over pervasive computing to give better healthcare services. Big data is built with the possibility to improve the healthcare industry. Big data makes the interconnection between patients, wearable sensors, healthcare caregivers, and providers through the utilization of Information and Communication Technology (ICT) and software. Most of the economic challenges in developing countries are caused by the healthcare sector, which occurs predominantly due to the increasing population requiring more quality of care concerning older people. Older people need great attention and care as they lead with irreparable damages when a minor accident or insignificant disease occurs. Therefore, the necessity of implementing new technologies and tools has arisen to support senior citizens regarding their healthcare. Various advancements in wireless technology, miniaturization, computing power, and processing made diverse healthcare innovations that led to developing the connected medical devices. Hence, this proposal develops a new healthcare monitoring system for tracking the activities of elderly people, where the Hadoop MapReduce technique for parallel processing the large-sized data. The data collected as mentioned in the available datasets is performed by using the numerous wearable sensors fixed on the “subject’s left ankle, right arm, and chest” that are transformed to the cloud platform and also to the data analytics layer according to the Internet of Medical Things (IoMT) devices. The given input undergoes data splitting to produce tiny chunks. These small chunks of the input files are then considered as Map tasks. Here, in the map phase, the features are optimally selected by the Hybrid Dingo Coyote Optimization (HDCO). The combiner phase classifies the physical activities using the developed Deep Ensemble Learning (DEL) consisting of classifiers such as “Extreme Learning Machine (ELM), deep Convolutional Neural Network (CNN), Long short-term memory (LSTM), Deep Belief Network (DBN), and Deep Neural Network (DNN)”. The parameter tuning in these classifiers is done by the same HDCO. The reducer phase extracts data from different chunks by merging the same classes. The developed HDCO-DEL has secured 13.66%, 16.01%, 17.33%, 13.6%, and 14.01% better accuracy than ELM, CNN, LSTM, DBN, DNN, and HealthFog, respectively on second dataset. The comparison with existing methods shows its better performance and also predicts physical activities with overall high accuracy.

One of the problems encountered in the design and implementation of a serial production line (SPL) is the buffer size between the machine tools. The buffer size of the SPL has an important impact on the productivity of the whole production system. The machine tools’ characteristics including their uptimes and downtimes and the process parameters are the main factors that affect the decision regarding the buffer size, and thus the productivity of the SPL. Due to the dynamic nature of this problem, it is complex to find the optimal buffer size in SPL. Thus, in this paper, an Efficient Prediction Model (EPM) is developed using Artificial Neural Network (ANN). The purpose of the developed EPM is to find the buffer size between each succeeding pair of machine tools in SPL at any given uptimes and downtimes of machine tools. An optimization model based on genetic algorithms (GA) is used to generate the learning data for the prediction model to find the optimal or near optimal buffer size of the bay of each machine tool in SPL. The proposed approach integrates the optimization and prediction methodologies to evaluate, and predict the optimal buffer sizes for maximum productivity. Including uptime and downtime parameters enable the proposed method to be used to improve the design of running SPL as well as to design a new SPL. Numerical examples for five and fifteen machine tools were conducted independently in this research and the results show the ability of the proposed method to determine the optimal buffer sizes in a reasonable amount of time. In particular, the results of case studies show that the developed model accurately predict the optimal buffer size, especially for the case of five machines and even for a higher number of machine tools yet with acceptable but less accuracy. Finally, the performance of the proposed approach was compared with some results of the state of the art methods reported in the literature. The comparison shows the superiority of the present approach to identify buffer sizes for higher throughput under the same uptimes and downtimes.

Dispatching rules are generally useful for scheduling jobs in flexible manufacturing systems (FMSs). However, the appropriateness of these rules relies heavily on the condition of the system; thus, there is no single rule that always outperforms others. In this state of affairs, diverse machine-learning technology offers an effective approach for dynamic scheduling, as it allows managers to identify the most suitable rule at each moment. Nonetheless, various machine-learning algorithms may provide diverse recommendations. The main objective of this study is to implement FMS scheduling using intelligent hybrid learning algorithms with metaheuristic improvements. The developed model involves three phases: feature extraction, optimal weighted feature extraction, and prediction. After the benchmark datasets for the FMS are gathered, feature extraction is performed using t-distributed stochastic neighbor embedding, linear discriminant analysis, linear square regression, and higher-order statistical features. Further, an optimal weighted feature extraction method is developed to select the optimal features with less correlation using the improved lion algorithm (LA), which is called the modified nomadic-based LA (MN-LA). Finally, the optimally selected weighted features are subjected to a hybrid learning algorithm with the integration of a fuzzy classifier and a deep belief network (DBN). For improving the prediction model, the membership function of the fuzzy classifier is optimized using the proposed MN-LA. Moreover, the activation function and the number of hidden neurons in the DBN are optimized using the MN-LA. The main objective of the optimized hybrid classifier is to enhance prediction accuracy. The experimental results indicate the effectiveness of the proposed heuristic-based scheduling method for FMSs.

This paper derives an iterative deadlock prevention policy for systems of simple sequential processes with resources (S3PRs) based on structural analysis, which consists of two stages. The first stage is called siphons control. Strict minimal siphons (SMSs) in an S3PR net are computed and control places are added by imposing P-invariants associated with the complementary sets of the SMSs, which restricts no legal system behavior. The original resource places are removed and the newly added control places are regarded as resource places, resulting in a new net, which needs to add control places for its SMSs if deadlocks persist. Repeat this step until a new net without SMSs is obtained. Then, an S4PR, called the first-controlled net, is obtained by integrating all added control places into the original net. The second stage, called non-max-marked siphons control, is performed in an iterative way if the system is not live yet. At each iteration, a mixed integer linear programming (MILP) problem is formulated to compute a non-max-marked siphon, and a control place is added for the siphon to the first-controlled net, resulting in an augmented net. The iteration is executed until a final-augmented net generates no new non-max-marked siphon. In general, based on the above two stages, this paper can obtain a supervisor with more behavior permissiveness compared to the previous studies. Moreover, an optimal supervisor can be found if a first-controlled net has no non-max-marked siphon, implying that the second stage is not necessary. Finally, some examples are provided to demonstrate the proposed policy.

A deluge of approaches has been proposed to implement and deploy a computationally effective and maximally permissive liveness-enforcing supervisor for sequential resource allocation systems (RASs). However, they are stalled by the computational complexity of a large state space that grows exponentially with respect to the size of an underlying RAS. The attention of this work is restricted to a special class of RASs: Disjunctive/Single-unit (D/SU)-RASs. Given an initial resource configuration of a D/SU-RAS, a complete state enumeration can be obtained through pure algebraic operations on this configuration with the pre-computed initial basis state space. When an explicit and complete enumeration of reachable states is impossible (due to the limited memory and storage space), we propose the exact number of reachable states. In this case, given a state vector, its reachability can be decided by an algebraic method. Experimental studies demonstrate the efficiency of the proposed approach.

Worldwide manufacturing and or service organizations aim for continuous elimination of waste of all types. But, these organizations lack in waste management and its evaluation. The paper addresses a case study of medium scale manufacturing organization in Saudi Arabia, which emphasizes the implementation of lean principles to improvise waste in the manufacturing plant. A multi criteria lean performance score (MCLPS) approach is proposed. The MCLPS approach analyses effectiveness of the manufacturing strategies, and identifies the potential opportunities for future improvement. It is observed that implementation of lean strategies improve plant utilization from 62.72% to 67%; inventory turnover ratio from 22.2 to 25.78 times per six months; wastage of material dropped by 536 bottles of material per startup activity; and non-value added time reduced by 5.84%. Similarly with help of MCLPS approach the management measures leanness of a manufacturing organization, which helps to track and monitor step by step desired improvements in the present manufacturing systems. For the improved manufacturing system it is evident that multi-criteria lean performance score is improved by 34%.