Machine Utilization Research Articles

Enhanced Resource Allocation and Workload Management using Reinforcement Learning Method for Cloud Environment

Cloud computing is a delivery model of IT resources such as computing servers, storage, databases, networking and software over the Internet. It offers the resources as services based on demand with more faster, flexible and economies of scale. The major challenges in the cloud computing are resource allocation and workload management due to the scalability of the cloud users and the services deployed in it. Even though there are various approaches available to manage workload and resource allocation, unfortunately most of them fail to mange it properly. This paper proposes a Reinforcement Learning based Enhanced Resource Allocation and Workload Management (RL-ERAWM) approach to increase the performance of cloud with large number of tasks and users. It implements the Q-Learning approach which effectively considers arrival rate of the requests and workload of the virtual machine. Experimental results prove that the proposed method alleviates the performance of task scheduling and workload management process compared with other approaches in terms of response time, makespan and virtual machine utilization.

Ranking dispatching rules in multi-objective dynamic flow shop scheduling: a multi-faceted perspective

In this paper, we present a multi-faceted approach for ranking dispatching rules (DRs) in multi-objective dynamic flow shop scheduling systems using data envelopment analysis (DEA). The merits of the proposed DEA-based approach stem in its ability to (1) integrate explicitly, under the same DEA framework, desirable and undesirable performance criteria of DRs without a priori normalisation or aggregation; (2) guarantee that the best DR preserves its benchmarking status regardless of the production scenario; (3) circumvent potential occurrence of multiple efficient DRs through embedding ordered weighted averaging (OWA) under DEA cross evaluation to produce aggregate ranking scores for the DRs. The evaluation of the new ranking approach is conducted using 18 data instances of 20 DRs each. The results reveal that, whatever the OWA optimism level, the preferred DR shifts away from the Shortest Processing Time (SPT) rule to the Cost Over Time (COVERT) rule as due-date tightness becomes relaxed, which appears consistent with known performance expectations of these DRs under such settings. To demonstrate a possible implementation of these results to support decision making in operations scheduling, we present a basic adaptive rule that switches automatically between the preferred rules based on real-time due-date tightness and machine utilisation levels.

Investigation in machining parameter of micro profile and surface characteristics of Al7475 with SiC alloy in LBM

Abstract In today’s market utilization of suitable machine for machining of composites for the application of aircraft and automotive parts was crucial one, because of their high strength with low weight ratio. Also, the profile such as micro elliptical shape was machined to reduce stress concentration of the area which is in contact with loads. Since the regular circular profile has affected high amount stress concentration than that of elliptical profile for the same loading conditions, an attempt was made in machining of micro elliptical profile on the Al 7475 composite material by using the laser beam machining (LBM). For betterment of profile (dimension and good surface) an analyze was done on the parameters such as scanning speed and laser power of the LBM, The dimensional stability as well as surface qualities were determined and the suitable parameter was suggested for the future work in Al 7475 composite with LBM.

Development of a resource-saving, small-sized downhole hydraulic machine for well drilling

Our analysis of the structural features and technological capabilities of serial downhole hydraulic motors (hydraulic machines), designed for drilling wells, has revealed their shortcomings. These include the limited resources due to the complexity of their design, the consumption of a working fluid, significant dimensions in length, mass, low rotation frequencies of the shaft, which do not correspond to the technological modes of wells diamond drilling. Based on the analysis of structural schemes of downhole hydraulic motors it has been concluded that the greatest opportunities for maximum use of the potential energy of a working fluid are demonstrated by rotary-type hydraulic machines.We have proposed, as the object of the study, a structural scheme of the hydraulic machine, which applies the physical principles of converting the weight (energy) of the column of a working fluid at the time of stator rotation around the non-rotating rotor. The principles of separation in the direction of movement of the incoming flow from the reverse spreading have been taken into consideration, as well as the exclusion of a stagnant zone and the creation of a multilevel momentum of reactive forces by the flow of a fluid.Based on the devised procedure, we have performed theoretical calculations of energy characteristics of a two-chamber deep hydraulic machine, defined technical specifications; the calculation scheme of force interaction between a working liquid and the elements of a hydraulic machine is given.Based on the results from theoretical calculations, the structural-technological documentation for a downhole hydraulic machine has been developed; a prototype was made and experiments were conducted to determine operability of the scheme, as well as the boundary values of its operation. The quantitative values for its energy characteristics were determined based on the readings from control-measurement instrumentation.We have conducted a comparative analysis of the technical and energy characteristics of a rotary-type downhole hydraulic machine with industrially produced hydraulic engines, the turbodrill TG-124, and the screw engine D1-124, of the same diameter.Conditions for the utilization of a downhole hydraulic machine have been proposed, as well promising directions for the further research and development to improve it and expand the scope of its application

Prediction-based Resource Provision and Virtual Machines Placement in Cloud Data Center

With the rapid development of virtualization, cloud servers provide the power and flexibility that single servers struggle to provide. However, low utilization of physical machines and high energy consumption are the main concerns for cloud service providers. In this paper, we propose a predict-and-place framework to decrease the number of active physical machines in cloud centers. We analyze the historical VM (virtual machine) request records to predict the VM demands in the next several days and then use an offline VM placement strategy to keep the number of active servers as less as possible while satisfying the SLA (Service-Level Agreement) requirements. In the prediction process, we enhanced the classical Holt’s linear prediction method on account of inevitable outliers to increase the prediction accuracy. We conduct experiments to compare the prediction accuracy of Perceptron, classical Holt-Winters, and our refined, robust Holt-Winters method. The experiment based on real data shows the simple perceptron has an inferior result. Our improved prediction method reduces the MAPE (Mean Absolute Percentage Error) by about 50% compared with the classical one, and the predict-and-place framework decreases the average number of active physical machines effectively in the cloud data center.

Development of a Method to Compute the Overall Key Performance Index for a Spinning Mill to Aid Supply Chain Management

This paper aims to provide a convenient measure of evaluating the performance of spinning mills using the overall Key Performance Index. Although many authors have advocated the concept of the key performance index, their studies were confined to departments other than spinning. The methodology followed in this paper is based on providing weightages to machine utilisation, spindle production, end breaks, HOK (number of operative hours required to produce 100 kg of yarn), roving production, yarn realisation, CSP (count strength product), units/kg of Yarn and to compute the overall Key Performance Index in a logical manner. It has come to light that some mills have achieved an overall Key Performance Index (OKPI) of more than 100 and less than 100 in certain other mills. Also the important factors which are significant for supply chain management have been highlighted. A very useful source of information and advice for various spinning mills to develop their own supply chain strategies is provided. Especially with the introduction of technical textiles, supply chain management has become a critical area.

Dynamic dispatching system using a deep denoising autoencoder for semiconductor manufacturing

Deep denoising autoencoders (DDAE), which are variants of the autoencoder, have shown outstanding performance in various machine learning tasks. In this study, we propose using a DDAE to address a dispatching rule selection problem that represents a major problem in semiconductor manufacturing. Recently, the significance of dispatching systems for storage allocation has become more apparent because operational issues lead to transfer inefficiency, resulting in production losses. Further, recent approaches have overlooked the possibility of a class imbalance problem in predicting the best dispatching rule. The main purpose of this study is to examine DDAE-based predictive control of the storage dispatching systems to reduce idle machines and production losses. We conducted an experimental evaluation to compare the predictive performance of DDAE with those of five other novelty detection algorithms. Finally, we compared our adaptive approach with the optimization and existing heuristic approaches to demonstrate the effectiveness and efficiency of the proposed method. The experimental results demonstrated that the proposed method outperformed the existing methods in terms of machine utilizations and throughputs.

Laboratory S et up P rocess and K ey F actors I nvolved to Set Bench M arking for University

Laboratory set-up plays a significant role in the college in light of the fact that the greater part of the mishaps occurred because of carelessness of faculty defensive gear (PPE), poor wellbeing standards and absence of legitimate direction to understudies about utilization of machines and synthetic concoctions so there are many factors which are to be considered like health and safety for users, maintain OSHA standards[1-5], set bench marking among university, cost cutting, maximum utilization of lab’s area, power saving etc. In this paper we have done complete study on lab infrastructure and also implemented in our university. As Dubai is very strict on health and safety so one more important thing is that we have followed all compliances of Dubai monitoring authorities like Dubai municipality, education department, police department and health department.

Strategic Outcome Using Fuzzy-AHP-Based Decision Approach for Sustainable Manufacturing

The manifestation of new part designs and continuously changing market demands as well as the requirements of new functions and technologies often results in higher material cost, lesser machine utilization, and extensive wastage of energy. As a consequence, companies across the world are striving for sustainable manufacturing, which can ensure flexibility as well as adaptability, with higher productivity and lesser wastage of resources. The dynamic and competitive nature of the world market emphasizes the importance of economically sustainable setups, such as reconfigurable cellular manufacturing systems (RCMSs). Indeed, among several cutting-edge strategies, the RCMS is the most prominent owing to its versatility, rationality, and resilience. However, one of the limitations associated with RCMS is the evaluation and selection of the best configuration that can meet abrupt changes and achieve manufacturing sustainability. Therefore, organizations intending to reconfigure have to address the issue of evaluating all possible alternatives and selecting the best one using a well-defined methodology. This paper focuses on evaluating and finding the best configuration using multi-criteria decision-making (MCDM) approaches depending on PROMETHEE and VIKOR. The fuzzy analytic hierarchy process (FAHP) is utilized to compute the weights of various criteria because it also considers any uncertainty and vagueness existing in the problem. The assessing attributes in this study are selected by keeping in mind the objective of sustainable manufacturing. The two MCDM methods are utilized for ranking different configurations to ascertain the results obtained from the other. The study accomplished in this paper is related to manufacturing setups that need to be reconfigured. The number of manufacturing configurations is determined, and simulation models are established for each configuration. The simulation outcomes are examined using the FAHP-PROMETHEE and FAHP-VIKOR to assess the appropriateness of each configuration depending on the identified performance measures. The results of the experiments show the importance of employing MCDM in RCMS to achieve sustainable manufacturing and determining the most effective setup.

Resource Utilization Scheme of Idle Virtual Machines for Multiple Large-Scale Jobs Based on OpenStack

Cloud computing services that provide computing resources to users through the Internet also provide computing resources in a virtual machine form based on virtualization techniques. In general, supercomputing and grid computing have mainly been used to process large-scale jobs occurring in scientific, technical, and engineering application domains. However, services that process large-scale jobs in parallel using idle virtual machines are not provided in cloud computing at present. Generally, users do not use virtual machines anymore, or they do not use them for a long period of time, because existing cloud computing assigns all of the use rights of virtual machines to users, resulting in the low use of computing resources. This study proposes a scheme to process large-scale jobs in parallel, using idle virtual machines and increasing the resource utilization of idle virtual machines. Idle virtual machines are basically identified through specific determination criteria out of virtual machines created using OpenStack, and then they are used in computing services. This is called the idle virtual machine–resource utilization (IVM–ReU), which is proposed in this study.

Resource Scheduling Problem in Distribution Center

Due to the rapid growth of the retail shop trend recently, the Distribution Center (DC) or warehouse had become to be a vital part in the supply chain in order to store goods from multiple suppliers and deliver to multiple retailers. Cross docking is a relatively the logistics technique used in the DC in order to move materials from inbound trailers to outbound trailers as quickly as possible. From the following problem, an ineffectual DC management leads to the resources utilization problem such as machine, forklift, industrial trucks, etc. The main objective of this research is to study and solve the scheduling problem of the usage of the forklift in the Cross docking of DC and to manage the inbound and outbound docks in order to minimize Tardiness jobs, and also to maximize the efficiency of machine utilization. This study has provided the scheduling, comparison from genetic algorithm and heuristic method. As the result came out we found that the forklift scheduling by the genetic algorithm method is given the better management result better than heuristic method.

Plant system simulation for engineering training workshops

AbstractPlant system engineering (PSE) is a method of efficiently integrating machinery and human labor. Studies in PSE have been mainly concerned with production facilities rather than teaching facilities. Conventional simulation methods assume that the product moves through the workstations. In the more complex situation of the training workshop, both the student and the artifact being produced move through the workstations. Hence a training workshop requires a fundamentally different way of approaching the simulation. Also, the waiting time experience of the students needs consideration, from a teaching perspective. We adapted the system simulation methodology by including multiple work‐streams through the workshop, and by adding decision stages into the model. The method was applied to a training workshop. The results identified specific changes in the way the students were assigned to machines, and the number of different types of machines, that would improve the operation of the facility. The improvement measures were reduction in waiting time by students, and greater machine utilization. Multiple different class sizes were explored. The approach is broadly applicable to other situations where the people move through a facility along with a partially completed physical product. This work develops an approach to optimize the performance of a manufacturing system, for the unusual class situations where the product moving through the simulation is not merely a physical product as in conventional simulation approaches, but rather the combination of people (students) and their partially completed physical product.

Comparison of Productivity and Cost between Two Integrated Harvesting Systems in South Korea

Interest in the production of renewable energy using forest biomass is increasing in South Korea, and improved knowledge on operations logistics to lower biomass harvesting costs is needed. This study aimed to build a low-cost forest-biomass harvesting system by analyzing the costs of two integrated (cut-to-length and whole-tree) harvesting systems for logs and logging residues. Two integrated harvesting systems were carried out in the clear-cut mixed forest on a steep slope. Compared to the cut-to-length system that separately extracts logs and logging residues in a forest, the cable whole-tree harvesting system can save $8.8/green weight ton (Gwt) because it requires no additional yarding operation cost of logging residues. Moreover, a breakeven analysis shows that the required machine utilization rates that favor whole-tree harvesting systems over cut-to-length harvesting were more than 70% for cable harvesting systems. The introduction of the whole-tree harvesting system is, therefore, required to produce forest biomass at a low cost. In the future, studies on forest-biomass processing and transportation systems will be needed to provide a biomass feedstock supply cost from stump to biomass power plant.

Adaptive Neuro-Fuzzy Inference System for Prediction of Surgery Time for Ischemic Stroke Patients

With the advent of machine learning techniques, creation and utilization of prediction models for different medical procedures including prediction of diagnosis, treatment and recovery of different medical conditions has become the norm. Recent studies focus on the automation of infarction volume growth rate prediction by the utilization of machine learning techniques. These techniques when effectively applied, could significantly help in reducing the time needed to attend to stroke patients. We propose, in this proposal, a Fuzzy Inference System that can determine when a stroke patient should undergo Decompressive Hemicraniectomy. The second infarction volume growth rate and the decision whether a patient needs to undergo this procedure, both predicted outputs of two trained models, act as inputs to this system. While the initial prediction model, that which predicts the second infarction volume growth rate is adopted from an earlier model, we propose the later model in this paper. Three Machine Learning techniques - Support Vector Machine, Artificial Neural Network and Adaptive Neuro Fuzzy Inference System with and without the feature reduction technique of Principle Component Analysis were modelled and evaluated, the best of which was selected to model the proposed prediction model. We also defined the structure of Fuzzy Inference System along with its rules and obtained an overall accuracy of 95.7% with a precision of 1 showing promising results from the use of fuzzy logic.

Capacity Enhancement through Value Stream Mapping and Line Balancing Technique in Compressor Assembly Line

In competitive global market, manufacturers are urged to know the customer needs well in advance to serve the customer. Customer satisfaction and on time delivery of products play a vital role in establishing long and healthy relationship. When demand for a product is growing, manufacturer ought to plan their activity well in advance to ensure product availability to customer. Capacity planning is done considering dynamically varying demand of customers. Manufacturers are also continuously thriving to reduce cost by enhancing machine utilization. But, Capacity loss is unavoidable in any manufacturing facility. And also it is basic necessity that production capacity to be enhanced, with production cost in consideration. The study intends to increase the capacity of an assembly line and in parallel optimize the manpower (productivity improvement). It deals with identifying non value adding activities and handling them to enhance the capacity of an assembly plant to meet forecast demand. These improvements achieved by using the lean tool, “Value stream mapping (VSM)” which helps to segregate value addition and non-value addition activities for each machine or work station and also will help in continual improvement. Current and future state mapping of assembly line will showcase the lean waste involved in process, current issues, line imbalance, possibilities of further improvements, gap between takt and actual cycle time, inventory at each assembly points, WIP machines and lead time at each stations. These individual issues are sorted out by generating kaizens at each station, which involves brain storming and expert opinion, involving all members of team. These improvements will spread over the entire process flow chain as small pockets. These many numbers of small pockets of improvements is harvested by using another lean tool, “Line balancing”. Line balancing will improve the overall line efficiency and hence capacity enhancement & man power optimization is achieved. The benefits of value stream mapping followed by line balancing technique had yielded capacity enhancement by 86%, manpower reduction by 28%, productivity improvement by from 1.24 to 1.70 units per person per day, through put time reduced from 325.6 to 259.5 mins, line efficiency improved from 79.7% to 97.9% and VA ratio improved from 33 to 37%.

A Global Data Model for Electric Power Data Centers

With the development of large-scale regional interconnected power grid, smart electronic devices and phasor measurement units have been widely used. How to realize high-speed processing of global data in power enterprise data center has become the key to real-time computing. This paper presents a data center global data model (DCGDM) for power enterprises. According to the computational coupling between tasks, the global data model of data center is established. With the constraints of CPU utilization and memory utilization of virtual machine and the goal of energy saving, the optimal configuration of the global data model of power enterprise data center is established. The results show that DCGDM is superior to traditional single-machine multi-threaded parallel computing in terms of time and system consumption. The calculation time of IEEE118 nodes is shortened by 42.32%, and the actual calculation time of 1133 nodes is shortened by 75.8% with the increase of system scale.

Customer Satisfaction as Intervening Between Use Automatic Teller Machine (ATM), Internet Banking and Quality of Loyalty (Case in Indonesia)

This research aims to determine the effect of customer satisfaction as an intervening variable between utilization of Automatic Teller Machine (ATM) variable, Internet Banking variable and service quality variable to variable customer loyalty at PT. Bank Mandiri Tbk in Batam city, Indonesia. This research is collecting the data in the form of primary and secondary data. Collecting the data in this study using questionnaires in the form of respondents Involved in this research were 187 customers. The number is obtained by using the formula Slovin with random sampling. The collected the data were processed and Analyzed by using SPSS and SEM for normality, regression coefficient, and determination. From the result show that the variable utilization ATM, Internet Banking and service quality affect to customer satisfaction has 83%. That is the service quality variables of the most dominant influence on customer satisfaction and customer loyalty.

PATENTS AND GROWTH IN OLG ECONOMY WITH PHYSICAL CAPITAL

We study the implications of patents in an overlapping generations model with horizontal innovation of differentiated physical capital. We show that within this demographic structure of finitely lived agents, weakening patent protection generates two contradicting effects on innovation and growth. Weakening patent protection lowers the (average) price of patented machines, thereby increasing machine utilization, output, aggregate saving, and investment. However, a higher demand for machines shifts investment away from the R&D activity aimed at inventing new machine varieties toward the formation of physical capital. The growth-maximizing level of patent protection is incomplete. Shortening patent length is more effective than loosening patent breadth in spurring growth, due to an additional positive effect on growth, that is decreasing investment in old patents. Welfare can be improved by weakening patent protection beyond the growth-maximizing level.

Capacity based deadline aware dynamic load balancing (CPDALB) model in cloud computing environment

ABSTRACT Cloud computing is a promising infrastructure in which many activities are carried out in both the commercial and academic spheres. Non-uniform utilization of virtual machines (VMs) in cloud creates an imbalance of the load, resulting in the violation of Service Level Agreements (SLAs) and tends to decreases the rate of revenue. There is a huge concern in scaling the load balancing algorithm due to the heterogeneous nature of the cloud. In this paper, a Capacity based Deadline Aware Dynamic Load Balancing (CPDALB) algorithm is proposed to address the problem. CPDALB is a novel heuristic based load balancing approach which focuses on the selection of VM for allocation of tasks to ensure customer satisfaction in terms of meeting deadline constraints and cost of running applications. CPDALB uses the concept of deploying requests to VMs based on their processing capacities to minimize load imbalance with satisfying the deadline. Other QoS parameters viz. turnaround time, response time and utilization are also computed for performance evaluation. An extensive simulation study has been carried out for the comparison of CPDALB with other standard scheduling algorithms given so far in the literature. The experimental results reveal that the proposed algorithm outperforms its competitors on account of almost all parameters under study.

Application of Hybrid Simulation in production scheduling in job shop systems

This work seeks to study one of the most complex and important issues in production scheduling research: flexible job shop systems. These systems are extremely important for industry, which uses the make-to-order strategy and seeks mix and volume flexibility. The model system will use agents within discrete-event simulation models, generating a Hybrid Simulation model. The agent will sequence the production orders at the beginning of the process and re-sequence them, when necessary, in order to achieve a multi-objective optimization. For this, the agent will bring together two different logics that have opposing goals. This work consists of the comparison of the results of three scheduling methods: firstly, with the sequence of arrival; secondly, with the agent using one sequencing logic; and, finally, using the same logic, but with adjustments in the sequence during the batch production, seeking to improve the negative points generated by the logic. It also stresses that this schedule ensures that the Manager Agent reduces makespan and increases machine utilization while increasing its interference in the model. This is a quantitative study, using the modeling and simulation method and following an empirical model.