• International Journal of Advanced Culture Technology
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• 제9권3호
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• pp.142-151
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• 2021

The content industry was greatly affected by the pandemic that hit the world in 2020. The release and production of major Hollywood films were postponed. Performances, festivals, and mega-events such as tourism and the Olympic Games were also canceled or postponed. The innovation of the content industry became inevitable in line with these rapid environmental changes, and the industry had to undergo internal changes according to these external environmental changes. Representative examples are talent fostering and job creation for young people. This study conducts both SWOT analysis and PEST analysis on the South Korean content industry, thereby analyzing cases of talent fostering and job creation suitable for the new environment.

• 한국시뮬레이션학회논문지
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• 제8권4호
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• pp.73-87
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• 1999

Order Review/Release (ORR) System is the linkage between planning system and actual production. Reduction of the waiting time on the machines, work in process and lead time variation may be achieved by adopting ORR strategies. But researchers on the ORR do not agree on the effectiveness of ORR. Some say that the overall system flow time may be increased if ORR is adopted, but others say that ORR can reduce work in process, flow time and variation of flow time. The objective of this research is to clarify under what environments order release strategy is effective. Simulation study was conducted in a hypothetical job shop. The experimental results show that dispatching rule is much more important than ORR is in controlling the shop floor. But the results indicate that ORR can reduce mean shop flow time, average work in process and variation of shop flow time under such environments where utilization level is high and planned order is weekly released to the order pool. And the results also show that the effect of plan smoothing on the ORR is insignificant, which is inconsistent with the results of the previous researches.

• 한국조리학회지
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• 제6권1호
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• pp.225-252
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• 2000

This study aims to examine theoretical frame work of franchise restaurant, the characteristics of store manager's job and the level of their job satisfaction through an empirical investigation. Job satisfaction survey study shows that store managers consider important all work to be attended to as part of their duty with service management on top. It is also found that the majority of store managers consider their aptitude as most important job satisfaction factor and those, who are satisfied with their job content, advancement and the prospect, are more proactive in delivering qualify service and more than willing to commit themselves to their duties. Regrading demographical variables, store managers with scholarly competence and higher pay level are more likely to be satisfied with their job but married men are not satisfied with the work environment in general. Ergo, Businesses should correspond by capitalizing on those store managers content with their duty thus collecting additional information and providing opportunities to further contribute to the business. For those dissatisfied individuals, however, businesses should determine their demands and by educational training supply a motive therefore making possible the conversion of such individuals to satisfied store managers and their active participation in business management. But, as with any study, this one has a number of limitation which constraints the generalizability of the empirical findings. It has not been for long since franchise restaurants established in domestic market and has been few studies regarding this topic there. Furthermore, managers are not willing to release operation related data. Therefore, further study are urged to overcome this limitation and should examine other dimensions of job satisfaction such as relations between revenue and profit with the level of store manager's job satisfaction remain to be investigated.

• 대한불안의학회지
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• 제13권1호
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• pp.39-45
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• 2017

Objective : Korean employees tend to release their stress by alcohol drinking. This study aimed to investigate the association between job stress and problematic drinking in Korean employees. Methods : The study participants were 72,119 employees who attended health screenings and responded to the Alcohol Use Disorder Identification Test-Korea (AUDIT-K), Korean Occupational Stress Scale-Short Form (KOSS-SF), Center for Epidemiologic Study-Depression (CES-D), Beck Anxiety Inventory (BAI) and questionnaire on suicidal ideation and sociodemographic characteristics. We investigated the sociodemographic and clinical characteristics of the problematic drinking group ($AUIDT-K{\geq}12$) and used binary logistic regression to determine the impact of job stress on problematic drinking. Results : Age, gender, marital status, education year, sleep duration, obesity, smoking, suicidal ideation, and depression and anxiety showed significant differences between the problematic drinking group and normal group. Among the various kinds of job stresses, job insecurity and lack of reward were associated with problematic drinking. Conclusion : The result of this study suggested that certain kinds of job stress might be associated with problematic drinking in Korean employees.

• 한국경영과학회지
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• 제25권2호
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• pp.47-57
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• 2000

In this paper we consider an n-job non-preemptive and identical parallel machine scheduling problem of minimizing the sum of earliness and tardiness with different release times and due dates. In the real world this problem is more realistic than the problems that release times equal to zero or due dates are common. The problem is proved to be NP-complete. Thus a heuristic is developed to solve this problem To illustrate its suitability a proposed heuristic is compared with a genetic algorithm for a large number of randomly generated test problems. Computational results show the effectiveness and efficiency of proposed heuristic. In summary the proposed heuristic provides good solutions than genetic algorithm when the problem size is large.

• 대한산업공학회지
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• 제25권1호
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• pp.47-55
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• 1999

This paper develops an efficient heuristic algorithm for scheduling workforce level that can accommodate all the requested maintenance jobs. Each job has its own release and due dates as well as man-day requirement, and must be scheduled in a non-interrupted time interval, namely, without preemption. Duration of each job is not fixed, but to be determined within given specific range. The objective is to minimize workforce level to complete all the requested maintenance jobs. We show that the problem can be seen as a variant of the two-dimensional bin-packing problem with some additional constraints. A non-linear mixed integer programming model for the problem is developed, and an efficient heuristic algorithm based on bin-packing algorithms is proposed. In order to evaluate goodness of the solution obtained from the proposed algorithm, a scheme for getting a good lower bound for the optimum solution is presented and analyzed. The computational experiment shows that the proposed algorithm performs quite satisfactorily.

• 경영과학
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• 제17권1호
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• pp.41-54
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• 2000

in the real world situations that some jobs need be processed only on certain limited machines frequently occur due to the capacity restrictions of machines such as tools fixtures or material handling equipment. In this paper we consider n-job non-preemptive and m parallel machines scheduling problem having two machines group. The objective function is to minimize the sum of earliness and tardiness with different release times and due dates. The problem is formulated as a mixed integer programming problem. The problem is proved to be Np-complete. Thus a heuristic is developed to solve this problem. To illustrate its suitability and efficiency a proposed heuristic is compared with a genetic algorithm and tabu search for a large number of randomly generated test problems in ship engine assembly shop. Through the experimental results it is showed that the proposed algorithm yields good solutions efficiently.

• 대한산업공학회지
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• 제33권3호
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• pp.329-338
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• 2007

This paper presents a dispatching algorithm for nonidentical parallel machines problem considering rework, sequence dependent setup times and release times. For each pair of a machine and a job type, rework probability of each job on a machine can be known through historical data acquisition. The heuristic scheduling scheme named by EDDR (Earliest Due Date with Rework probability) algorithm is proposed in this paper making use of the rework probability. The proposed dispatching algorithm is measured by two objective function value: 1) total tardiness and 2) the number of reworked jobs, respectively. The extensive computational results show that the proposed algorithm gives very efficient schedules superior to the existing dispatching algorithms.

• 디지털산업정보학회논문지
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• 제20권2호
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• pp.125-139
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• 2024

High turnover and short tenure in the gaming industry has been a problem since the 2000s. Although high turnover can increase the flexibility of working hours, it has a negative impact on labor stability, and the negative effects of turnover outweigh the positive effects for companies, so efforts are needed to reduce turnover intention, an antecedent of turnover. Therefore, this study investigated the factors affecting turnover intention of employees in the game industry, focusing on organizational commitment and job satisfaction, which are factors that affect turnover intention through previous studies. The results of the regression analysis and mediation effect verification by conducting a questionnaire survey on 221 employees in the game industry showed that emotional engagement among the components of organizational commitment influences turnover intention through job satisfaction. Therefore, it is important to provide goals such as game release and rewards to keep employees emotionally engaged in order to reduce turnover intention.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제11권7호
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• pp.205-216
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• 2022

딥러닝 분산 학습에 사용되는 많은 도구 중 하나는 컨테이너 오케스트레이션 도구인 쿠버네티스에서 실행되는 큐브플로우이다. 그리고 큐브플로우에서 기본적으로 제공하는 오퍼레이터를 사용하여 텐서플로우 학습 작업을 관리할 수 있다. 하지만 파라미터 서버 아키텍처 기반의 딥러닝 분산 학습 작업을 고려할 때 기존의 오퍼레이터가 사용하는 스케줄링 정책은 분산학습 작업의 태스크 친화도를 고려하지 않으며 자원을 동적으로 할당하거나 해제하는 기능을 제공하지 않는다. 이는 작업의 완료 시간이 오래 걸리거나 낮은 자원 활용률로 이어질 수 있다. 따라서 본 논문에서는 작업의 완료 시간을 단축시키고 자원 활용률을 높이기 위해 딥러닝 분산 학습 작업을 효율적으로 스케줄링하는 새로운 오퍼레이터를 제안한다. 기존 오퍼레이터를 수정하여 새로운 오퍼레이터를 구현하고 성능 평가를 위한 실험을 수행한 결과, 제안한 스케줄링 정책은 평균 작업 완료 시간 감소율을 최대 84%, 평균 CPU 활용 증가율을 최대 92%까지 향상시킬 수 있음을 보여준다.