• 한국산업보건학회지
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• 제24권4호
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• pp.536-546
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• 2014

Objectives: The major objective of this paper is compare the exposure work hours and experieence of ill health symptoms among under and over employment and matched group. Workers with over employment have more exposed to hazards than that with under employment because that workers with over employment work more than those with ender employment. Methods: This study as heuristics one used the third Korean Working Conditions Survey done by Occupational Safety and Health Research Institute in 2011. The sample size is 50,023 economic active persons. Over and under employment were measured by matching method of preferred and actual work hours. The exposed work hours to hazards were measured according to 13 hazardous factors and the experience of ill health symptoms were scaled by the number of experienced 14 ill health symptoms. To compare the exposure and the symptoms experience were compared by mean difference test with F test. Results: The proportion of over employment in male employees is 32.1% and that in female employees is 29.3% and under employment rate is 11.2% in mae and 13.9% in female employees. There is significant difference of the rate of over and under emplyment among age groups, industrial sectors, occupational groups and the state of employment. The difference of the exposed work hours to hazards among under, over and matched group were statistically significant in all hazards by gender. The exposed work hours to hazards in over employment were more than those in under employment. The number of experienced symptoms in over employment is statistically significantly more than that in under employment. Conclusions: Workers with over employment may be vulnerable group in the criteria of hazard exposure and health status. The results have some implications and limitations because that this study is heuristic one. The mismatch of preferred and actual working hours may be unfavorable work condition that has impact on safety and health of workers. The impact mechanism may be investigated as future study, Because that this study used cross sectional survey data, some causal relationships cannot be evaluated.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1993년도 추계학술대회발표논문집; 서강대학교, 서울; 25 Sep. 1993
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• pp.12-19
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• 1993

In general, the line balancing problem is defined as of finding an assignment of the given jobs to the workstations under the precedence constraints given to the set of jobs. Usually, the objective is either minimizing the cycle time under the given number of workstations or minimizing the number of workstations under the given cycle time. In this paper, we present a new type of an assembly line balancing problem which occurs in an electronics company manufacturing home appliances. The main difference of the problem compared to the general line balancing problem lies in the structure of the precedence given to the set of jobs. In the problem, the set of jobs is partitioned into two disjoint subjects. One is called the set of fixed jobs and the other, the set of floating jobs. The fixed jobs should be processed in the linear order and some pair of the jobs should not be assigned to the same workstations. Whereas, to each floating job, a set of ranges is given. The range is given in terms of two fixed jobs and it means that the floating job can be processed after the first job is processed and before the second job is processed. There can be more than one range associated to a floating job. We present a procedure to find an approximate solution to the problem. The procedure consists of two major parts. One is to find the assignment of the floating jobs under the given (feasible) assignment of the fixed jobs. The problem can be viewed as a constrained bin packing problem. The other is to find the assignment of the whole jobs under the given linear precedence on the set of the floating jobs. First problem is NP-hard and we devise a heuristic procedure to the problem based on the transportation problem and matching problem. The second problem can be solved in polynomial time by the shortest path method. The algorithm works in iterative manner. One step is composed of two phases. In the first phase, we solve the constrained bin packing problem. In the second phase, the shortest path problem is solved using the phase 1 result. The result of the phase 2 is used as an input to the phase 1 problem at the next step. We test the proposed algorithm on the set of real data found in the washing machine assembly line.