• Korean Journal of Social Welfare Studies
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• v.44 no.3
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• pp.323-346
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• 2013

The main purpose of this study is to analyze job sequences according to poverty profiles that the working poor have had. For the stated purpose, this study examines characteristics and patterns of job sequences by diving the subgroups, using the 10 year data of KLIPS and the sequence analysis. Major findings are as follows. The working-transient poor have different characteristics, such as longer working term, less job change, less number of gap and length, and relatively higher monthly income, from the working-recurrent poor and the working-persistent poor. However, there are no different characteristics between the working-recurrent poor and working-persistent poor, except for monthly income. Job sequences are divided into 5 clusters and job sequences types according to the working poor subgroups are quite different. Such analysis results would contribute to planning poverty policies based on job sequences differently seen in subgroups and finding specific policy alternatives to relieve the working poor.

• Korean Management Science Review
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• v.18 no.1
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• pp.147-153
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• 2001

In this paper, we Propose the Job-Pair Comparison (JPC) rule to minimize total tardiness in a single machine. For this purpose we derive conditional expression to choose the desirable job sequence. We also prove the transitivity of JPC rule, to prevent cycle and to decrease the complexity of comparison.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2002.11a
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• pp.363-368
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• 2002

Performance-based course design on the job of receiving substation installations is presented to achieve a work based curriculum development. It includes a job analysis method called ISD(Instructional Systems Design & Development) and DACUM(Developing a Curriculum) which is designed to accept various requirements of industrial fields. It is provided with course profile, key contents, terminal learning objectives, intermediate learning objectives, instruction strategy, instruction sequence, case study, action learning and lesson plan.

• Korean Management Science Review
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• v.13 no.2
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• pp.25-48
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• 1996

Mixed Model Assembly Lines (MMALs) are increasingly used to produce differentiated products on a single assembly line without work-in-process storage, Usually, a typical MMAL consists of a number of (1) stations doing exactly the same operation on every job, (2) stations involving operations with different choices, and (3) stations offering operations that are not performed on every job, or that are performed on every job but with many options. For stations of the first type there is no sequencing problem at all. However, for the second type a set-up cost is incurred each time the operation switches from one choice to another. At the third type of stations, different models, requring different amounts and choices of assembly work, creates an uneven flow of work along the line and variations in the work load at these stations. When a subsequence of jobs requires more work load than the station can handle, it is necessary to help the operations at the station or to complete the work elsewhere. Therefore, a schedule which minimize the sum of set-up cost and utility work cost is desired. So this study has developed Fixed Random Ordering Rule (FROR), Fixed Ascending Ordering Rule (FAOR), Fixed Descending Ordering Rule, and Extended NHR (ENHR). ENHR is to choose optimal color ordering of each batch with NHR, and to decide job sequence of the batch with it, too. As the result of experiments, ENHR was the best heuristic algorithm. NHR is a new heuristic rule in which only the minimum addition of violations from both partial sequence and unassigned sequence at every branch could be considered. And this is a heuristic sequencing rule for the third type of stations at MMAL. This study developed one more heuristic algorithm to test the performance of NHR, which is named as Practical Heuristic Rule (PHR).

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.3
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• pp.169-180
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• 2015

Recently, scheduling problems with position-dependent processing times have received considerable attention in the literature, where the processing times of jobs are dependent on the processing sequences. However, they did not consider cases in which each processed job has different learning or aging ratios. This means that the actual processing time for a job can be determined not only by the processing sequence, but also by the learning/aging ratio, which can reflect the degree of processing difficulties in subsequent jobs. Motivated by these remarks, in this paper, we consider a two-agent single-machine scheduling problem with linear job-dependent position-based learning effects, where two agents compete to use a common single machine and each job has a different learning ratio. Specifically, we take into account two different objective functions for two agents: one agent minimizes the total weighted completion time, and the other restricts the makespan to less than an upper bound. After formally defining the problem by developing a mixed integer non-linear programming formulation, we devise a branch-and-bound (B&B) algorithm to give optimal solutions by developing four dominance properties based on a pairwise interchange comparison and four properties regarding the feasibility of a considered sequence. We suggest a lower bound to speed up the search procedure in the B&B algorithm by fathoming any non-prominent nodes. As this problem is at least NP-hard, we suggest efficient genetic algorithms using different methods to generate the initial population and two crossover operations. Computational results show that the proposed algorithms are efficient to obtain near-optimal solutions.

• Journal of Korean Institute of Industrial Engineers
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• v.21 no.3
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• pp.409-428
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• 1995

Job shop scheduling with m different machines and n different jobs is a NP-hard problem of combinatorial optimization. The purpose of the paper is to develop the heuristic method using tabu search for job shop scheduling to minimize makespan or mean flowtime. To apply tabu search to job shop scheduling problem, in this paper we propose the several move methods that employ insert moves in order to generate the neighbor solutions, and present the efficient rescheduling procedure that yields active schedule for a changed operation sequence by a move of operations. We also discuss the tabu search techniques of diversifying the search of solution space as well as the simple tabu search. By experiments, we find the appropriate tabu list size and tabu attributes, and analyze the proposed tabu search techniques with respect to the quality of solutions and the efforts of computation. The experimental results show that the proposed tabu search techniques using long-term memory function have the ability to search a good solution, and are more efficient in the mean flowtime minimization problem than in the makespan minimization.

• Journal of the Korea Safety Management & Science
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• v.14 no.1
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• pp.167-177
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• 2012

In this paper, we raised the performance of heuristic algorithm to assign job to workers in parallel line inspection process without sequence. In previous research, we developed the heuristic algorithm. But the heuristic algorithm can't find optimal solution perfectly. In order to solve this problem, we proposed new method to make initial solution called FN(First Next) method and combined the new FN method and old FE method using previous heuristic algorithm. Experiments of assigning job are performed to evaluate performance of this FE+FN heuristic algorithm. The result shows that the FE+FN heuristic algorithm can find the optimal solution to assign job to workers evenly in many type of cases. Especially, in case there are optimal solutions, this heuristic algorithm can find the optimal solution perfectly.

• Journal of the Korean Operations Research and Management Science Society
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• v.18 no.1
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• pp.119-126
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• 1993

We study a manufacturing process that is quite common in semiconductor wafer fabrication of semiconductor chip production. A machine is used to process a job consisting of J wafers. Each job requires a setup, and the i$_{th}$ setup for a job is sucessful with probability P$_{i}$. The setup is prone to failure, which results in the loss of expensive wafers. Therefore, a tiral run is first conducted on a small batch. If the set up is successful, the test is passed and the balance of the job can be processed. If the setup is unsuccessful, the exposed wafers are lost to scrap and the mask is realigned. The process then repeats on the balance of the job. We call this as send-ahead policy and consider general policies in which the number of wafers that are sent shead depend on the cost of the raw wafer, the sequence of success probabilities, and the balance of the job. We model this process and determine the expected number of good wafers per job,the expected time to process a job, and the long run average throughput. An algorithm to minimize the cost per good wafer subject to a demand constraint is provided.d.d.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.183-199
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• 2007

In this paper we research the problem in which the objective is to minimize the sum of squared deviations of job expected completion times from the due date, and the job processing times are stochastic. In the problem the machine is subject to stochastic breakdowns and all jobs are preempt-repeat. In order to show that the replacing ESSD by SSDE is reasonable, we discuss difference between ESSD function and SSDE function. We first give an express of the expected completion times for both cases without resampling and with resampling. Then we show that the optimal sequence of the problem V-shaped with respect to expected occupying time. A dynamic programming algorithm based on the V-shape property of the optimal sequence is suggested. The time complexity of the algorithm is pseudopolynomial.

• Journal of applied mathematics & informatics
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• v.25 no.1_2
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• pp.305-314
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• 2007

This paper considers single machine scheduling problems where the processing time of a job increases as a function of its position in the sequence. In this model, the later a given job is scheduled in the sequence, the longer its processing time. It is shown that the optimal schedule may be very different from that of the classical version of the problem. We introduce polynomial solutions for the makespan minimization problem, the sum of completion times minimization problem and the sum of earliness penalties minimization problem. For two resource constrained problems, based on the analysis of the problems, the optimal resource allocation methods are presented, respectively.