Machine Utilization Research Articles

Energy-Aware VM Scheduler

Cost is the backbone of any business model to sustain and growth in the competitive business market. Considering this, higher energy consumption may affect the operational cost of any industry. Cloud computing is one of the most popular IT business model for service provider and its users. It is observed in many research, that the average cost of the services are highly dependent upon the run time and power consumption. More power consumption effect the environmental things as well. In this review, more than 100 research articles are considered to evaluate the energy consumption methods, issues and challenges from 2009 to 2021. Based on the study, the energy-aware load balancing methods are classified into four major clusters. The most cited methods are compared based on method used, platform where it is deployed, different evaluation parameters like cost, run time, Virtual Machine utilization, Service-level-agreement violation rate, and Quality-of-service QoS. The result of this review is presented as open research issues and challenges for forthcoming research.

Are Biometric Attendance Registers a Panacea for Workplace Absenteeism in Tanzania? A Lesson from Public Secondary Schools in Nyamagana Municipality

Teachers’ workplace absenteeism is widely reported in countries like Tanzania. Left unaddressed, the problem negatively affects institutions and students’ academic excellence. In response, use of digital technology, including deployment of biometric fingerprint attendance registers is thought to bring a novel opportunity for mitigating the problem. However, the extent to which biometrics is effective in managing employees’ attendance in countries like Tanzania is yet to be known. This study draws experience from Nyamagana Municipality to ascertain the effectiveness of biometric fingerprint attendance registers in addressing teachers’ workplace absenteeism. The findings unveil challenges that blur the effectiveness of biometric fingerprint attendance registers in the studied district. With ICT, teachers can still falsify their workplace attendance. More so, unreliable power supply and COVID-19 pandemic have heightened the challenges related to effective utilisation of the fingerprint machines in managing teacher absenteeism. The study recommends that an effective design and use of digital technology in managing teachers absenteeism should take into account the sociological aspects in educational industry.

Risk Treatment for Energy-Oriented Production Plans through the Selection, Classification, and Integration of Suitable Measures

With rising electricity prices, industries are trying to exploit opportunities to reduce electricity costs. Adapting to fluctuating energy prices offers the possibility to save electricity costs without reducing the performance of the production system. Production planning and control play key roles in the implementation of the adjustments. By taking into account the price forecasts for the electricity markets in addition to machine utilization, work in process, and throughput time, an energy-oriented production plan is set up. The electrical energy is procured based on this plan and the associated load profile. Deviations from the forecast and the purchased amount of electricity lead to high penalties, as they can destabilize the energy system. For manufacturing companies, this means that machine failures and other unexpected events must be dealt with in a structured manner to avoid these penalty costs. This paper presents an approach to selecting, classifying, and integrating suitable measures from existing risk treatment paths into the production schedule. The selection of measures is based on a hybrid multi-criteria decision-making method in which the three relevant criteria, namely, cost, energy flexibility, and risk reduction, are weighted by applying both an analytic hierarchy process and entropy, and they are then prioritized according to multi-attribute utility theory. In the following, the subdivision into preventive and reactive measures is made in order to choose between the modification of the original plan or the creation of backup plans. With the help of mathematical optimization, the measures are integrated into the production schedule by minimizing the cost of balancing energy. The approach was implemented in MATLAB® and validated using a case study in the foundry industry.

An Enhanced Tabu Search Cell Formation Algorithm for a Cellular Manufacturing System

The major benefit of using Cellular manufacturing systems (CMS) is the improvement in efficiency and reduction in the production time. In a CMS the part families and machine parts are identified to minimise the inter and intracellular movement and maximise the utilisation of machines within each cell. Many scholars have proposed methods for the evaluation of machine cell part layouts with single routes; this paper introduces a modified Hybrid Tabu Search Algorithm (HTSA) referred to as Hybrid Algorithm in this study for machine cell part layouts having multiple routes as well. The primary objective of this paper is to minimise the inter and intracellular movement using a hybrid algorithm. The paper presents a comparative analysis of the existing and the proposed algorithms, proving that the proposed hybrid algorithm is simple, easy to understand, and has a remarkable efficiency with a runtime of 5.6 seconds.

Optimized Utilization of Post-Harvest Coffee Agricultural Equipment and Machines

Indonesian coffee plantations, which dominate by smallholder plantations with limitary production, technology, and coffee quality, concern the government. One of the Directorate General of Estate Crops interventions to increase market demand is post-harvest equipment and machines for coffee. This study aimed to analyze the combination of available production resources to obtain maximum benefits from assisted machinery. The resource optimization method in this study uses linear programming and is processed using POM-QM software for windows. Respondents in this study were four farmers associations who received assistance in 2017-2020 with the results of their business sustainability studies in the excellent category. The optimal production solution for farmers’ associations is from a combination of products, resources, and sensitivity analysis. The optimization results for green bean farmers association 1, 2, 3, and 4 are 150 kg/month, 300 kg/month, 200 kg/month, and 759 kg/month. As for ground coffee, it is 81.3 kg/month, 83.8 kg/month, 143.3 kg/month and 272 kg/month. With this optimum level of production, it provides a profit of IDR 4,750,000 per month, IDR 2,456,250 per month, IDR 6,333,334 per month, and IDR 9,235,000 per month for farmers association 1, 2, 3, and 4 current levels of production.

Enhanced time-constraint aware tasks scheduling mechanism based on predictive optimization for efficient load balancing in smart manufacturing

Smart manufacturing has great potential for developing customized products to meet the dynamic needs of customers and a collaborative network to enhance production efficiency. It is an emerging area with the revolution of business strategies, such as Industry 4.0 and Industrial Internet of the United States of America. IIoT and data-driven technologies, such as AI and ML, have leveraged the production environment by facilitating mass personalized customization of products and improving manufacturing processes collaboratively. However, these technologies are segregated and dispersed in the digitization of manufacturing products and automation of machines in existing manufacturing processes. Therefore, it is a leverage challenge to develop an integrated solution based on IIoT and data-driven technologies for an autonomous manufacturing environment. This research study presents an integrated solution using an enhanced TCA tasks scheduling mechanism based on a predictive optimization approach to improve smart manufacturing production efficiency. The proposed enhanced TCA tasks scheduling mechanism is an improved variant of FEF scheduling that considers accurate decision (prediction) measures and tasks’ minimal (optimal) time to schedule tasks efficiently. This study aims to efficiently plan task execution sequence to increase smart manufacturing productions and efficiency of resource utilization in real-time by maximizing utilization of smart machines, minimizing tasks idle time, and autonomously controlling the smart manufacturing environment through installed sensors and actuators. Furthermore, different evaluation methods are used to analyze the significance of the proposed PO-TCA scheduling mechanism, such as response time, tasks drop rate, tasks starvation rate, and machine utilization rate. The experimental analysis shows that the proposed enhanced PO-TCA scheduling mechanism reduces dropout and starvation rates by 21 % and 17 %, respectively. Our proposed mechanism also improves machine utilization by an average of 18 % compared to the baseline scheduling strategy. Moreover, experimental results signify that the proposed PO-TCA scheduling mechanism improves the utilization of smart machines and minimizes the task’s idle time to achieve a trade-off between tasks response and waiting time.

Minimizing saw cycles on the cutting stock problem with processing times depending on the cutting pattern

In this paper the cutting stock problem minimizing saw cycles is considered. In this context where objects can be cut simultaneously the trade-off between raw material and machine utilization costs is of major interest. Different processing times for each cutting pattern are considered, according to the number of items on it, different from previous works of the literature. The optimization aims to minimize both machine utilization costs due to the saw cycles and raw material usage costs. An integer mathematical formulation is proposed, and a solution method based on a column generation procedure is used. Computational tests were performed to assess the impact on costs for illustrative and real industry instances. A set of instances were also randomly generated to analyse the behaviour of the outcome of the problem in face of different technical parameters.

Introducing Tree-Based-Regression Models for Prediction of Hard Rock TBM Performance with Consideration of Rock Type

Prediction of machine performance is a fundamental step for planning, cost estimation/control and selection of the machine type when using a tunnel boring machine (TBM). Penetration rate (PR) and machine utilization (U) are the two principal measures of TBM performance for evaluating the feasibility of using a machine in a given ground condition. However, despite the widespread use of TBMs and established track records, accurate estimation of machine performance could still be a challenge, particularly in complex geological conditions. Since different types of rocks have varied texture (cementation and grain size), and respond differently to cutting forces in the TBM tunnelling, incorporating the effects of rock type in performance prediction models can improve the accuracy of the estimates. The aim of this study was to develop models for predicting penetration rate of hard rock TBMs in different types of rock based on field penetration index (FPI), using multivariable regression analysis and machine learning algorithm, including classification and regression tree (CART). The proposed models offer estimated FPIs in different rock types, rock strength, and rock mass properties in the form of graphs (diagrams), which can be used to estimate TBM penetration rate. The proposed models have been developed based on the analysis of a comprehensive database of TBM performance in various rock types and offers more accurate estimates of machine performance by incorporating many of the key parameters available in typical geotechnical reports and contract documents. The models also exhibit sensitivity to rock mass parameters for predicting the penetration rate.

Statistical-Based Heuristic for Scheduling of Independent Tasks in Cloud Computing

Cloud computing is an emerging and innovative technology that is used for solving large-scale complex problems. It considers as an extension to distributed and parallel computing. Additionally, it enables sharing, organizing and aggregation of computational machines to satisfy the user demands. One of the main goals of the task scheduling is to minimize the makespan (i.e. the overall processing time) and maximize the machine utilization. This paper addresses the problem of how to schedule many independent tasks when using different machines. It introduces two batch mode heuristics algorithms for scheduling independent task in the computational cloud environment, high mean absolute deviation first heuristic and QoS Guided Sufferage-HMADF heuristic. Besides, the paper presented other existing batch mode heuristics such as, Min-Min, Max-Min and Sufferage. The four heuristic modes are simulated and the experimental results are discussed using two performance measures, makespan and machine resource utilization.

Introducing uniform discrete event simulation (CSM2020) for modeling the TBM tunneling process

Several models have been developed to predict utilization of Tunnel Boring Machines (TBMs) based on statistical analysis of available project databases. Apart from the existing models, use of discrete event simulation (DES) for estimation has also been examined for utilization prediction. The results of these models demonstrate strong sensitivity to the influence of geological conditions and site logistics and offer the ability to take the site specific conditions into account for estimation of machine utilization. The present study proposes the stochastic simulation model CSM2020 using Arena© software that encompasses all major tunneling activities that impact machine utilization. CSM2020 model offers more accurate estimation of machine utilization and higher flexibility to customize the estimates based on equipment, logistics, and ground conditions. Given the observed limitations in various commercial software packages including Arena©, and lack of access/experience with such software in the typical tunneling projects, a DES model was also developed by using MATLAB. The accuracy of both the CSM2020 and MATLAB DES models depends on the availability of data to incorporate the complex interdependencies of various subsystems and different tunneling activities.The utilizations estimated for selected projects using the two simulation models were within 1% of each other and the results were verified by comparing the estimated values with those from field observation. The MATLAB code did not require a license, and it offers a better opportunity to keep track of the activities in a database that can be subsequently used for data visualization and analyzed to evaluate utilization and availability of individual subsystems and resources.

Machine learning-based evaluation of the damage caused by cracks on concrete structures

Identification through image processing has been used in various fields, including product examination and more recently infrastructure inspection, such as identifying cracks in structures. The conventional method, in which a human inspector uses a crack gauge or determines the size through a visual evaluation, results in a subjective evaluation of the extent of concrete life. Currently, an operator inspects the concrete structure by visually examining the concrete surface. However, this method presents numerous problems, including when the inspector has to work in dangerously high places. Therefore, we used machine learning to extract the features of cracks in image processing. We identified that non-arbitrary features, such as color-related features, are also important. Because concrete appears monochromatic, it is difficult for humans to analyze this color-related feature. We compared these newly obtained machine learning features with the previously used arbitrary features and confirmed that the machine learning features were more accurate in terms of detection. We also compared the generation of discriminators based on these features with a fixed threshold for discrimination and the utilization of support vector machine (SVM) and other machine learning methods. In this paper, two issues are discussed: an analysis of the effectiveness of machine learning and SVM-based discriminant generation in detecting cracks, and the classification results based on crack width. Crack width classification using machine learning is useful when sufficient image resolution is not available. The final detection accuracy of the new method was 11.7% better than that of the method using arbitrary features; moreover, the false detection rate was also higher in the proposed method. We further attempted to classify these cracks in accordance with their damage levels. To evaluate the degree of damage, we focused on the difference in the width of the cracks and extracted different features in three classes on the basis of the different crack widths, and were able to classify these clacks.

An innovative approach for resource sharing and scheduling in a sustainable distributed manufacturing system

Secure, transparent, and sustainable distributed manufacturing system (DMS) is a pressing need for current Industry 4.0. In this paper, exchange of highly sensitive information in a more transparent and secure way and to avoid the misunderstandings and trust issues between the enterprises a smart contractbased on blockchain technology has been proposed in case of a distributed manufacturingenvironment. Here, we used a public-permission less Ethereum platform to execute the smart contracts in the Blockchain to process the customer orders and to identify the right enterprise. Later, a multi-objective mixed-integer linear programming (MILP) model is formulated for optimal resource sharing and scheduling in a considered sustainable DMS. The objectives of the proposed model consist of simultaneously improvement of the performance measures such as makespan, machine utilization, energy consumption, and reliability. To solve this MILP model, a new Multi-objective-based Hybridized Moth Flame Evolutionary Optimization Algorithm (HMFEO) is developed and then the effectiveness of the proposed algorithm is validated with the Non-dominated Sorting Genetic Algorithm (NSGA-III). The results obtained from implementing the model using experimental data along with different cases show the efficiency and the validity of the proposed model and solution approach. Moreover, several performance indicators like hyper volume are increased by nearly 15–20 % that shows the superiority of the proposed algorithm with the NSGA-III.

Sustainable negotiation-based nesting and scheduling in additive manufacturing systems: A case study and multi-objective meta-heuristic algorithms

This paper proposes a novel integrated framework for nesting (i.e., part orientation selection and two-dimensional packing) and scheduling parts assigned to batches (or jobs) on additive manufacturing machines. For the first time, a tri-objective optimization model is designed that interprets profit, energy utilization of machines, and goodwill losses (i.e., tardiness, negotiation, and increasing due date) as three sustainability criteria. Besides, considered negotiation plans may reduce the prices of ordered parts for a possible increase in the initial due date set by customers. The model’s scalability is supported by tailoring three algorithms: robust improved ɛ-constraint method, non-dominated sorting genetic algorithm (NSGA-II), and multi-objective grey wolf optimizer (MOGWO). Several insights are derived by analyzing the model’s sensitivity to its key parameters and validating its applicability by a case study of Amazon’s last-mile delivery process. The results confirm the conflicting objectives, suggested action plans, and proposed algorithms.

Utilization of machine and their elements is actual science and practical task

Basic physical and mechanical properties of heatcover composition material that is used for making of protective screens are experimentally investigated. For this pur-pose on the example of flowed plastic STEF executed (with the use of traditional charts) research at small (less than 1 m/s) and high (more than 3 m/s) rate-of-change of lad-ening. At small speeds of ladening durability of material was investigated at superficial deformation, tension, bend and change. At high speeds work of destruction and shock viscidity was determined. Organization and realization of experiments were done ac-cording to rules of planning experiments. All investigations were made with the using kinds of experimental equipment which had according certifications of control their quality and accuracy. Obtained results were treated by methods of mathematical statis-tics and researches of their dispersions. The results of experimental researches drawn on at the decision of concrete de-sign-engineering tasks, in particular, at development of equipment and choice of the modes of crushing of composition materials during their utilization (although, undoubt-edly, the obtained data have more wide value). For example, data of intruding conical elements in material were used for selecting optimal angles for working elements of equipment. Correlations between power parameters of intruding process were used for selecting power parameters of equipment for crushing of composition materials. Equipment and technology for crushing of composition materials during their uti-lization, which are designed on the base of these investigations, have new useful tech-nical and technological specialties, that is why, authors obtained document of intellec-tual property on this object.

Simulating energy consumption based on material addition rates for material extrusion of CFR-PEEK: a trade-off between energy costs and cycle time.

While many studies for material extrusion–based additive manufacturing (AM) of polymers focus on experimental approaches to evaluate relevant performance measures from process parameters, there is a lack of discussion to connect experimental results with useful applications. Also, one of the major deficiencies in the application literature is a trade-off analysis between energy costs and cycle time (time to produce an item from the beginning to the end) since improving these two measures simultaneously is challenging. Thus, this paper proposes an energy simulation method for performing a trade-off analysis between energy costs and cycle time using combinations of major AM process parameters for material extrusion. We conduct experiments using carbon fiber–reinforced poly-ether-ether-ketone (CFR-PEEK), which is increasingly used in material extrusion. From experimental results, we build a power model in which power (kW) is derived as a linear function of material addition rates (MAR). This MAR regression model is then used in a proposed simulation model that integrates discrete event simulation and numerical simulation. In our simulation case study of 50 machines and 40 scenarios, we investigate trade-offs between energy costs and cycle time with three control policies (P1, P25, and P50) that allow 1, 25, or 50 machines to start heating, respectively. The trade-off analysis results show that P25 can be preferred when a balance between cycle time and energy costs is pursued, while P1 or P50 can be chosen if either energy cost (with P1) or cycle time (with P50) is more important than the other measure. Moreover, we find that the machine utilization, variability, and product volume have significant effects on energy costs and cycle time.

A high-applicability heterogeneous cloud data centers resource management algorithm based on trusted virtual machine migration

With the continuous development and maturity of cloud computing technology, the scale and number of cloud data center (CDC) are also expanding. This increasingly draws attention to the problem of high energy consumption in CDCs. Dynamic virtual machine (VM) consolidation is a promising approach for reducing energy consumption. VM migration, as a VM consolidation technology, can effectively improve the utilization of physical machine (PM) and optimize the scheduling process of CDCs. However, most VM integration algorithms, in existing research, are aimed at improving the utilization of PMs. Excessive utilization of PMs may increase the competition for shared resources among the VMs running on them. As a result, the performance of these VMs deteriorates, and the execution time of cloud tasks is increased or even interrupted. This study systematically analyzes the overall architecture of CDCs. Subsequently, migration rules are established for the one-dimensional and multidimensional trusted VMs. A high- applicability heterogeneous CDC resource management algorithm based on trusted VM migration (HTVM2) is then proposed. The proposed algorithm not only solves the one-dimensional VM migration problem of homogeneous and heterogeneous CDCs but also those of multi-dimensional VMs. This improves the success rate of VM migration, reduces the energy consumption of the CDC, and improves load balancing while ensuring VM performance. Finally, the algorithm was compared with the other three algorithms outperforming them all, as demonstrated by experimental results.

Simultaneous Scheduling of Machines and Tools in a Multi-Machine FMS with Alternate Machines Using Crow Search Algorithm

Scheduling jobs and tools is a significant problem for manufacturing systems. Inefficient job scheduling and tool loading planning may result in under utilization of capital intensive machines and a high level of machine idle time. Therefore, efficient scheduling of jobs and tools enables a manufacturing system to increase machines’ utilization and decrease their idle times. This paper addresses machines’ and tools’ joint scheduling with alternate machines in a multimachine flexible manufacturing system (FMS) to minimize makespan (MSN). Only one copy of each type of tool is made available in FMS where tools are expensive. The tools are stored in the central tool magazine (CTM), which shares and serves them to several machines in order to reduce the cost of duplicating the tools in every machine. The problem is to select machines from alternate machines for job-operations, allocation of tools to the job-operations and job-operations’ sequencing on machines for MSN minimization. This paper presents nonlinear mixed integer programming (MIP) formulation to model this simultaneous scheduling problem and crow search algorithm (CSA) built on the crows’ intelligent behavior for solving this problem. The results show that CSA is providing better solutions than Jaya algorithm and the usage of alternate machines for the operations can reduce MSN.

Multi-objective prediction-based optimization of power consumption for cloud data centers

The overall development of the cloud paradigm is dominating omnipresence in the industry 4.0 business world. Over the last decade, the control measures for power utilization among the proliferative Hyper-Scale Data Centers (HSDCs) have been elucidated. However, the lack of attention to regulating power in Small and Medium-Scale Data Centers (SMSDCs) has ensued in excessive power drainage in small and medium-scale cloud data centers. The crucial factor for excessive power utilization of SMSDCs encompasses providing excessive resources, high certainty tasks. Majority of the previously reported studies zeroed-in on problems associated with hyper-scale data centers, excluding probes of the issues prevalent in small and medium-scale cloud data centers. This paper proffers a framework for a predictive optimization approach for delivering the data center services to end-users. In the first phase, the Multi-Output (MO) Random Forest Regressor (RFR) (MO-RFR) concurrently predicts the multiple-resource utilization of Virtual Machines (VMs). The predictive framework outcome was utilized by the Multi-Objective Particle Swarm Optimization (MO-PSO) framework in the second phase to resolve the issue in virtual machine placement and to accomplish better physical machine consolidation. The proposed multi-prediction-based MO-PSO to escalate the resource usage, minimizes the power utilization, and curtail the carbon footprint. The efficacy of the proposed approach was appraised via performance metrics and actual workload traces. The acquired result from the proposed method outperforms the baseline approaches.

Markovian analysis of unreliable multi-machine flexible manufacturing cell.

In this paper, a Markovian model is constructed to test a flexible manufacturing cell’s (FMC) performance. The considered FMC includes a conveyer belt, robot, and n machines. The conveyer belt delivers the working part to the robot, and the robot picks it up and loads it onto the machines. The movement of a working part from one step to the next depends on the availability of the tool in the next step (i.e., conveyer belt, robot, and machine). Any machine is assumed to potentially fail during the processing time as a result of high loading stresses. First, a Markovian model is constructed for single-machine and double-machine FMCs. Then, a generalized FMC with an n-machine is constructed. The introduced model is illustrated with two numerical examples for both the single- and triple-machine. The Markov chain model can be used to estimate the FMC performance measures (i.e., overall utilization of machines and production rate). It is used to analyze the response of these measures under varying parameters (i.e., conveyor belt delivery rate, robot loading rate, processing rate of a machine, failure rate of a machine, and down machines’ repairing rate). Moreover, an economic model based on the Markov chain model is introduced to analyze the FMC’s net profit under these varying parameters.

An efficient virtual machine allocation using single stage weapon target assignment model in cloud software‐defined network environment

SummaryWith the rapid growth of the Internet, technological advancements, and the increasing number of cloud users, a paradigm transformation towards separate data and control planes with a centralized control point is taking place. As a result of this separation, software‐defined networks (SDNs) have distinct software controllers for routers and the capacity to manage network behavior. Virtual machine placement (VMP) and virtual machine migration (VMM) are two examples of SDN usage in data centers. The issue of VMP in cloud environments is a significant challenge that has been widely researched but not fully resolved. In this research, we used the single stage weapon target assignment (SSWTA) to model the virtual machine allocation (VMA) for jobs in order to maximize the resource utilization of the physical machines (PMs) by reducing idle time. Furthermore, this research proposes three algorithms: job assignment algorithms, PM allocation (PMA) using the least recently used (LRU) policy, and optimal VMA. The proposed VMA's performance is compared to other related VMA models such as first fit decreasing, and it is determined to be efficient in terms of resource utilization.