• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.7
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• pp.3413-3430
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• 2017

With the fast increasing popularity of mobile services, ubiquitous mobile devices with enhanced sensing capabilities collect and share local information towards a common goal. The recent Mobile Crowd Sensing (MCS) paradigm enables a broad range of mobile applications and undoubtedly revolutionizes many sectors of our life. A critical challenge for the MCS paradigm is to induce mobile devices to be workers providing sensing services. In this study, we examine the problem of sensing task assignment to maximize the overall performance in MCS system while ensuring reciprocal advantages among mobile devices. Based on the overlapping coalition game model, we propose a novel workload determination scheme for each individual device. The proposed scheme can effectively decompose the complex optimization problem and obtains an effective solution using the interactive learning process. Finally, we have conducted extensive simulations, and the results demonstrate that the proposed scheme achieves a fair tradeoff solution between the MCS performance and the profit of individual devices.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.111-117
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• 2007

Comparing with bar-code systems, RFID systems can supply more efficient work. Using RFID systems, logistic management systems could be helped effectively to gather real-time information. It's available to reduce the working time and object's delay time, and to deal with real-time information by using RFID system. Until now, based on how many pallets used, there is few study about best workload of RFID system. Therefore, in this study, both bar-code and RFID system simulations were executed for inspection process in distribution center. As a result, following the ware pallet quantity, the total cost of both working time and other delay times were calculated and the sensitivity analysis of total cost trend was executed.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.637-638
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• 2015

There has been growing interests in life-cycle project management in the construction industry. A lot of attention is given to Building Information Modeling (BIM) which stores and uses a variety of construction information for the life cycle of project management. However, due to the additional workload arising from BIM, its expected effects versus its input costs are still under discussion in practice. As an attempt to address this issue, one of previous studies suggested an automated linking process by developing Standard Classification Numbering System (SCNS) and Geometry Breakdown Structure (GBS) to enhance the efficiency of integration process of BIM objects, cost, and schedule. Though SCNS and GBS facilitates identifying all different dataset, making object sets and linking schedule activities still needs to be manually done without having an automated tool. In this context, the purpose of this paper is to develop and validate a fully automated integration system for 3D-objects, cost, and schedule. A prototype system for single family homes (Hanok) was developed and tested in order to verify its efficiency.

• IE interfaces
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• v.17 no.2
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• pp.208-217
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• 2004

This paper deals with a process planning problem in the flexible assembly system (FAS). The problem is to assign assembly tasks to stations with limited working space and to determine assembly routing with the objective of minimizing transfer time of the products among stations, while satisfying precedence relations among the tasks and upper-bound workload constraints for each station. In the process planning of FAS, the optimality of assembly routing depends on tasks loading. The integration of tasks loading and assembly routing is therefore important for an efficient utilization of FAS. To solve the integrated problem at the same time, in this paper we propose a new method using an artificial intelligent search technique, named 2-leveled symbiotic evolutionary algorithm. Through computational experiments, the performance of the proposed algorithm is compared with those of a traditional evolutionary algorithm and a symbiotic evolutionary algorithm. The experimental results show that the proposed algorithm outperforms the algorithms compared.

• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.270-278
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• 2023

We propose a container orchestration system for process workloads that combines the potential of big data and machine learning technologies to integrate enterprise process-centric workloads. This proposed system analyzes big data generated from industrial automation to identify hidden patterns and build a machine learning prediction model. For each machine learning case, training data is loaded into a data store and preprocessed for model training. In the next step, you can use the training data to select and apply an appropriate model. Then evaluate the model using the following test data: This step is called model construction and can be performed in a deployment framework. Additionally, a visual hierarchy is constructed to display prediction results and facilitate big data analysis. In order to implement parallel computing of PCA in the proposed system, several virtual systems were implemented to build the cluster required for the big data cluster. The implementation for evaluation and analysis built the necessary clusters by creating multiple virtual machines in a big data cluster to implement parallel computation of PCA. The proposed system is modeled as layers of individual components that can be connected together. The advantage of a system is that components can be added, replaced, or reused without affecting the rest of the system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.9
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• pp.4063-4086
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• 2016

Cloud computing is a robust technology, which facilitate to resolve many parallel distributed computing issues in the modern Big Data environment. Hadoop is an ecosystem, which process large data-sets in distributed computing environment. The HDFS is a filesystem of Hadoop, which process data blocks to the cluster nodes. The data block placement has become a bottleneck to overall performance in a Hadoop cluster. The current placement policy assumes that, all Datanodes have equal computing capacity to process data blocks. This computing capacity includes availability of same storage media and same processing performances of a node. As a result, Hadoop cluster performance gets effected with unbalanced workloads, inefficient storage-tier, network traffic congestion and HDFS integrity issues. This paper proposes a storage-tier-aware Robust Data Placement (RDP) scheme, which systematically resolves unbalanced workloads, reduces network congestion to an optimal state, utilizes storage-tier in a useful manner and minimizes the HDFS integrity issues. The experimental results show that the proposed approach reduced unbalanced workload issue to 72%. Moreover, the presented approach resolve storage-tier compatibility problem to 81% by predicting storage for block jobs and improved overall data block placement by 78% through pre-calculated computing capacity allocations and execution of map files over respective Namenode and Datanodes.

• Journal of Korean Institute of Industrial Engineers
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• v.41 no.3
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• pp.275-286
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• 2015

In this paper, we propose a practical method for setting up a daily production plan which can operate semiconductor fabrication factories more stably and linearly by determining work in process (WIP) targets and movement targets. We first adjust cycle times of the operations to satisfy the monthly production plan. Second, work in process (WIP) targets are determined to control the production progress of operations: earliness and tardiness. Third, movement targets are determined to reduce cumulated differences between WIP targets and actual WIPs. Finally, the determined movement targets are modified through a simulation model which considers capacities of the equipments and allocations of the WIPs in the fab. The proposed daily production planning method can be easily adapted to the memory semiconductor fabs because the method is very simple and has straightforward logics. Although the proposed method is simple and straightforward, the power of the method is very strong. Results from the shop floor in past few periods showed that the proposed methodology gives a good performance with respect to the productivity, workload balance, and machine utilization. We can expect that the proposed daily production planning method will be used as a useful tool for operating semiconductor fabrication factories more efficiently and effectively.

• Journal of Korea Society of Industrial Information Systems
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• v.23 no.1
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• pp.53-63
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• 2018

In this paper, we analyze the waiting time of a queueing system with D-BMAP (discrete-time batch Markovian arrival process) and D-policy. Customer group or packets arrives at the system according to discrete-time Markovian arrival process, and an idle single server becomes busy when the total service time of waiting customer group exceeds the predetermined workload threshold D. Once the server starts busy period, the server provides service until there is no customer in the system. The steady-state waiting time distribution is derived in the form of a generating function. Mean waiting time is derived as a performance measure. Simulation is also performed for the purpose of verification and validation. Two simple numerical examples are shown.

• Journal of Korean Institute of Industrial Engineers
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• v.31 no.3
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• pp.180-193
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• 2005

This paper deals with a multiobjective process planning problem of flexible assembly systems(FASs). The FAS planning problem addressed in this paper is an integrated one of the assignment of assembly tasks to stations and the determination of assembly routing, while satisfying precedence relations among the tasks and flexibility capacity for each station. In this research, we consider two objectives: minimizing transfer time of the products among stations and absolute deviation of workstation workload(ADWW). We place emphasis on finding a set of diverse near Pareto or true Pareto optimal solutions. To achieve this, we present a new multiobjective coevolutionary algorithm for the integrated problem here, named a multiobjective symbiotic evolutionary algorithm(MOSEA). The structure of the algorithm and the strategies of evolution are devised in this paper to enhance the search ability. Extensive computational experiments are carried out to demonstrate the performance of the proposed algorithm. The experimental results show that the proposed algorithm is a promising method for the integrated and multiobjective problem.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.4
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• pp.141-154
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• 2007

Nowadays, customer-oriented manufacturing enterprises such as airplane, ship, ship engine, etc are knowledge-intensive and higher added value industries. In these companies, to quickly respond customer's order, a quotation management is a very important task. But, this task is very complex, time-consuming, resource- consuming, and difficult process because it is related with many departments within a company. In this paper, for the effective and efficient quotation management, the concept of product specification framework is introduced because a quotation BOM (bills of material) can be created from product specification. Also, this paper presents the product specification framework based quotation management process and implements the quotation management system for the ship engine division of 'H' company, one of customer-oriented manufacturing enterprises. As a result, the proposed quotation management concept reduced a lead time of drawing out quotations from $3{\sim}10$ days to 1 hour. And, the constructed quotation management system achieved a rapidity, accuracy, quality, and workload reduction of the quotation management process.