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Data analytics provides important tools and methods for processing the data generated during legal services. This paper aims to provide a systematic survey of the research papers on the application of quantitative data analytics algorithms in the legal domain. To this end, relevant research papers were collected and used to analyze topics and trends of research on data analytics-based Legal Tech. The key findings of this paper are as follows. Firstly, the number of research papers about Legal Tech has increased dramatically recently. Secondly, the application of supervised learning techniques to legal judgment data is a very popular approach in this research area. Thirdly, preprocessing legal documents is a very important procedure as many legal documents exist in text form. Fourthly, artificial neural networks and their variations are widely used in research on data analytics-based Legal Tech. Fifthly, data analytics-based Legal Tech is a multidisciplinary research topic related to computer science and social science, etc.
So-Hui Park; Dong-Gu Lee; Jin-Sung Park; Jun-Woo Kim. A Survey of Research on Data Analytics-Based Legal Tech. Sustainability 2021, 13, 8085 .
AMA StyleSo-Hui Park, Dong-Gu Lee, Jin-Sung Park, Jun-Woo Kim. A Survey of Research on Data Analytics-Based Legal Tech. Sustainability. 2021; 13 (14):8085.
Chicago/Turabian StyleSo-Hui Park; Dong-Gu Lee; Jin-Sung Park; Jun-Woo Kim. 2021. "A Survey of Research on Data Analytics-Based Legal Tech." Sustainability 13, no. 14: 8085.
This paper proposes a novel genetic algorithm (GA) approach that utilizes a multichromosome to solve the flexible job-shop scheduling problem (FJSP), which involves two kinds of decisions: machine selection and operation sequencing. Typically, the former is represented by a string of categorical values, whereas the latter forms a sequence of operations. Consequently, the chromosome of conventional GAs for solving FJSP consists of a categorical part and a sequential part. Since these two parts are different from each other, different kinds of genetic operators are required to solve the FJSP using conventional GAs. In contrast, this paper proposes a unified GA approach that enables the application of an identical crossover strategy in both the categorical and sequential parts. In order to implement the unified approach, the sequential part is evolved by applying a candidate order-based GA (COGA), which can use traditional crossover strategies such as one-point or two-point crossovers. Such crossover strategies can also be used to evolve the categorical part. Thus, we can handle the categorical and sequential parts in an identical manner if identical crossover points are used for both. In this study, the unified approach was used to extend the existing COGA to a unified COGA (u-COGA), which can be used to solve FJSPs. Numerical experiments reveal that the u-COGA is useful for solving FJSPs with complex structures.
Jin-Sung Park; Huey-Yuen Ng; Tay-Jin Chua; Yen-Ting Ng; Jun-Woo Kim. Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem. Applied Sciences 2021, 11, 6454 .
AMA StyleJin-Sung Park, Huey-Yuen Ng, Tay-Jin Chua, Yen-Ting Ng, Jun-Woo Kim. Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem. Applied Sciences. 2021; 11 (14):6454.
Chicago/Turabian StyleJin-Sung Park; Huey-Yuen Ng; Tay-Jin Chua; Yen-Ting Ng; Jun-Woo Kim. 2021. "Unified Genetic Algorithm Approach for Solving Flexible Job-Shop Scheduling Problem." Applied Sciences 11, no. 14: 6454.
Most large distribution centers’ order picking processes are highly labor-intensive. Increasing the efficiency of order picking allows these facilities to move higher volumes of products. The application of data mining in distribution centers has the capability of generating efficiency improvements, mainly if these techniques are used to analyze the large amount of data generated by orders received by distribution centers and determine correlations in ordering patterns. This paper proposes a heuristic method to optimize the order picking distance based on frequent itemset grouping and nonuniform product weights. The proposed heuristic uses association rule mining (ARM) to create families of products based on the similarities between the stock keeping units (SKUs). SKUs with higher similarities are located near the rest of the members of the family. This heuristic is applied to a numerical case using data obtained from a real distribution center in the food retail industry. The experiment results show that data mining-driven developed layouts can reduce the traveling distance required to pick orders.
Yue Li; Francis Méndez-Mediavilla; Cecilia Temponi; Junwoo Kim; Jesus Jimenez. A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations. Applied Sciences 2021, 11, 1839 .
AMA StyleYue Li, Francis Méndez-Mediavilla, Cecilia Temponi, Junwoo Kim, Jesus Jimenez. A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations. Applied Sciences. 2021; 11 (4):1839.
Chicago/Turabian StyleYue Li; Francis Méndez-Mediavilla; Cecilia Temponi; Junwoo Kim; Jesus Jimenez. 2021. "A Heuristic Storage Location Assignment Based on Frequent Itemset Classes to Improve Order Picking Operations." Applied Sciences 11, no. 4: 1839.
Rework for defective items is very common in practical shopfloors; however, it generally causes unnecessary energy consumptions and operational costs. In order to address this problem, we propose a novel approach called the intelligent rework process management (i-RPM) system. The proposed system is based on intelligent rework policy, which provides a preventive rework procedure for items with latent defects. Such items can be detected before quality tests by applying conventional classification techniques. Moreover, training sets for the classification algorithms can be collected by using modern information and communications technology (ICT) infrastructures. Items with latent defects are not allowed to proceed to the following processes under intelligent rework policy. Instead, they are returned to the preceding processes for rework in order to avoid unnecessary losses on the shopfloor. Consequently, the proposed system helps to achieve a sustainable manufacturing system. Nevertheless, misclassification by the classification model can degrade the performance of intelligent rework policy. Therefore, the i-RPM system is designed to compare rework policies based on classification accuracy and choose the best one of them. For illustration, we applied the i-RPM system to the rework procedure of a steel manufacturer located in Busan, South Korea, and our experiment results revealed that the cost reduction effect of the intelligent rework policy is affected by several input parameters.
Da-Seol Jo; Tae-Woong Kim; Jun-Woo Kim. Intelligent Rework Process Management System under Smart Factory Environment. Sustainability 2020, 12, 9883 .
AMA StyleDa-Seol Jo, Tae-Woong Kim, Jun-Woo Kim. Intelligent Rework Process Management System under Smart Factory Environment. Sustainability. 2020; 12 (23):9883.
Chicago/Turabian StyleDa-Seol Jo; Tae-Woong Kim; Jun-Woo Kim. 2020. "Intelligent Rework Process Management System under Smart Factory Environment." Sustainability 12, no. 23: 9883.
This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.
Junwoo Kim; Francis Mendez; Jesus Jimenez. Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers. IEEE Access 2020, 8, 189025 -189035.
AMA StyleJunwoo Kim, Francis Mendez, Jesus Jimenez. Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers. IEEE Access. 2020; 8 ():189025-189035.
Chicago/Turabian StyleJunwoo Kim; Francis Mendez; Jesus Jimenez. 2020. "Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers." IEEE Access 8, no. : 189025-189035.
The primary role of modern information and communication technology infrastructures in smart factory is to collect a wide range of digital data from manufacturing resources; however, many manufacturing companies still have significant trouble in collecting data relevant for operations management. Especially, it is difficult to collect data about movement of mobile resources, such as human operators and material handling equipment. Moreover, managers of manufacturing companies often have significant troubles in utilizing the raw data collected by information and communication technology infrastructures due to its complexities and vast amount. To fill these gaps, this article proposes indoor positioning-based mobile resource movement data management system, which can be used to collect and process the mobile resource movement data flexibly. The indoor positioning technologies enable to track the positions of physical objects in real time; however, they generate time series data for 3D coordinates of object position not suitable for practical use. Therefore, this article aims to integrate the indoor positioning technology with a specialized user application, which allows the users to define what kinds of data should be collected and how the raw data should be transformed.
Jin Sung Park; Sung Jin Lee; Jesus Jimenez; Soo Kyun Kim; Jun Woo Kim. Indoor positioning-based mobile resource movement data management system for smart factory operations management. International Journal of Distributed Sensor Networks 2020, 16, 1 .
AMA StyleJin Sung Park, Sung Jin Lee, Jesus Jimenez, Soo Kyun Kim, Jun Woo Kim. Indoor positioning-based mobile resource movement data management system for smart factory operations management. International Journal of Distributed Sensor Networks. 2020; 16 (3):1.
Chicago/Turabian StyleJin Sung Park; Sung Jin Lee; Jesus Jimenez; Soo Kyun Kim; Jun Woo Kim. 2020. "Indoor positioning-based mobile resource movement data management system for smart factory operations management." International Journal of Distributed Sensor Networks 16, no. 3: 1.
SAM provides enhanced visual aids for representing the many-to-many association rules in large transaction data.The performance of SAM can be numerically evaluated by using S2C measure.SAM enables users to conveniently identify the interesting areas that might contain interesting association rules.SAMs with higher S2C values are more useful for visual exploration of association rules. The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules.
Jun Woo Kim. Construction and evaluation of structured association map for visual exploration of association rules. Expert Systems with Applications 2017, 74, 70 -81.
AMA StyleJun Woo Kim. Construction and evaluation of structured association map for visual exploration of association rules. Expert Systems with Applications. 2017; 74 ():70-81.
Chicago/Turabian StyleJun Woo Kim. 2017. "Construction and evaluation of structured association map for visual exploration of association rules." Expert Systems with Applications 74, no. : 70-81.
The goal of meta-heuristic algorithms such as genetic algorithm is to explore the search space of the combinatorial optimization problems efficiently to locate the optimal solutions, the feasible solutions with the best output values. However, typical meta-heuristic algorithms implicitly assume that the feasible solutions for the given problems can be generated easily, and they can fail to solve the problems with rare feasible solutions in effective manner. In this context, this paper aims to introduce the maze-type shortest path problem as an example of the combinatorial optimization problem with rare feasible solutions and to propose the fitness switching genetic algorithm for solving it. The maze-type shortest path problem is characterized by the maze-type network that contains many dead-ends, and the conventional genetic algorithms based on the population of feasible paths are not appropriate for finding the optimal path in such networks. On the contrary, this paper introduces the fitness switching and fitness leveling operations for maintaining the population of both feasible and infeasible paths during the search procedure. In addition, the infeasible paths are randomly modified by the simple local search of the proposed algorithm to find the feasible paths more quickly. The experiment results show that the proposed algorithm can address the issues in the combinatorial optimization problems with rare feasible solutions very effectively.
Jun Woo Kim; Soo Kyun Kim. Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions. The Journal of Supercomputing 2016, 72, 3549 -3571.
AMA StyleJun Woo Kim, Soo Kyun Kim. Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions. The Journal of Supercomputing. 2016; 72 (9):3549-3571.
Chicago/Turabian StyleJun Woo Kim; Soo Kyun Kim. 2016. "Fitness switching genetic algorithm for solving combinatorial optimization problems with rare feasible solutions." The Journal of Supercomputing 72, no. 9: 3549-3571.
Because almost 60–80% of the total costs for operating a contact centre involve wage and benefit expenses for personnel, determining the optimal number of agents available is of great importance in call centre management. In modern call centres, working hours are divided into planning intervals with identical lengths. Each planning interval is typically assumed to be a homogeneous Poisson process in a steady state, and simple queuing models, such as Erlang-C (M/M/c), are often applied to determine the optimal staffing levels of the planning intervals. However, since the actual length of the planning interval in practice is relatively short, the basic assumption of staffing analysis could be violated. In this paper, we numerically analyze an M/M/c+M call centre’s behavior in a transient state. As a result, we can determine appropriate staffing levels of a call centre with short planning intervals which do not assume to be in a steady state.
Jun Woo Kim; Sung Ho Ha. Advanced workforce management for effective customer services. Quality & Quantity 2011, 46, 1715 -1726.
AMA StyleJun Woo Kim, Sung Ho Ha. Advanced workforce management for effective customer services. Quality & Quantity. 2011; 46 (6):1715-1726.
Chicago/Turabian StyleJun Woo Kim; Sung Ho Ha. 2011. "Advanced workforce management for effective customer services." Quality & Quantity 46, no. 6: 1715-1726.
This paper considers ‘two-stage’ call centers where some incoming calls are completed by first service while others require an additional second service. Although this type of call center is not uncommon, it has not been dealt with, if any, in the call center literature. In this paper, we introduce the concept of the ‘two-stage’ call center and discuss its features. Furthermore, we develop an effective outsourcing strategy in ‘two-stage’ call centers. To this end, we model ‘two-stage’ service system and propose several call routing structures. The structures are compared through numerical test and conventional queueing theories form the theoretical basis of our study.
Jun Woo Kim; Sang Chan Park. Outsourcing strategy in two-stage call centers. Computers & Operations Research 2010, 37, 790 -805.
AMA StyleJun Woo Kim, Sang Chan Park. Outsourcing strategy in two-stage call centers. Computers & Operations Research. 2010; 37 (4):790-805.
Chicago/Turabian StyleJun Woo Kim; Sang Chan Park. 2010. "Outsourcing strategy in two-stage call centers." Computers & Operations Research 37, no. 4: 790-805.