• Journal of the Korean Operations Research and Management Science Society
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• v.41 no.1
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• pp.87-98
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• 2016

A single machine scheduling problem for jobs with stochastic processing time is considered in this study. Shortest processing time (SPT) sequencing according to the expected processing times of jobs is optimal for schedules with minimal expected mean flow time when all the jobs arrive to be scheduled and their expected processing times are known. However, SPT sequencing according to the expected processing time may not be optimal for the minimization of the mean flow time when the actual processing times of jobs are known. This study evaluates the complexity of SPT sequencing through a comparison of the mean flow times of schedules based on the expected processing times and actual processing times of randomly generated jobs. Evaluation results show that SPT sequencing according to the expected flow time exhibits chaotic variation to the optimal mean flow time. The relative deviation from the optimal mean flow time increases as the number of jobs, processing time, or coefficient of variation increases.

• Journal of Korean Society for Quality Management
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• v.17 no.1
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• pp.69-81
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• 1989

This study is concerned with a job sequencing method using the concept of sampling technique in the case of Job Shop. This is the follow study of Kang and Ro (1988) which examined the possibility of application of sampling technique to determine the Job Sequence in the case of Flow Shop. Not only it is very difficult, but also it takes too much time to develop the appropriate job schedules that satisfy the complex work conditions. The most job sequencing algorithms have been developed to determine the best or good solution under the special conditions or assumptions. The application areas of these algorithms are also very narrow, so it is very hard to find the appropriate algorithm which satisfy the complex work conditions. In this case it is very desirable to develop a simple job sequencing method which can select the optimal job sequence or near optimal job sequence with a little effort. This study is to examine the effect of sampling job sequencing which can select the good job of 0.01%~5% upper good group. The result shows that there is the sets of 0.05%~23% job sequence group which has the same amount of performance measure with the optimal job sequence in the case of experiment of N/M/G/$F_{max}$. This indicates that the sampling job sequencing method is a useful job sequencing method to find the optimal or good job sequence with consuming a small amount of time. The results of ANOVA show that the only one factor, number of machines is the significant factor for determining the job sequence at ${\alpha}=0.01$. It takes about 10 minutes to compare the number of 10,000 samples of job sequence by personal computer and it is proved that the selection rate of the same job sequence with optimal job sequence is 23.0%, 3.9% and 0.065% in the case of 2 machines, 3 machines and 4 machines, respectively. The area of application can readily be extended to the other work condition.

• Journal of Korean Institute of Industrial Engineers
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• v.12 no.1
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• pp.81-94
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• 1986

A shortest path dynamic programming formulation is proposed and attemped to solve an uncapacitated expansion sequencing problem. It is also compared with the Extended Binary State Space approach with total capacity. Difficulties and merits associated with the formulation are discussed. The shortest path dynamic programming lacks the separability condition and an optimal solution is not guaranteed. However it has other merits and seems to be the practical solution procedure for the expansion sequencing problem in a sense that it finds near optimal solution with less state evaluations.

• Journal of Korean Institute of Industrial Engineers
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• v.30 no.4
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• pp.301-305
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• 2004

In a warehouse, plates listed in the different orders are placed mixed. Nevertheless, when plates are going out, a group of plates listed in the same order should be sequentially loaded on a transporter. Since the access to the plates is only from the top of the stack, it happens often that we have to move some plates on a temporary place in order to access a plate below them. This paper deals with a problem of sequencing orders for transportation to minimize the number of plates temporarily moved.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.12 no.19
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• pp.25-30
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• 1989

This study is concerned with a job sequencing method using the concept of sampling technique. This sampling technique has never been applied to develop the scheduling algorithms. The most job sequencing algorithms have been developed to determine the best or good solution under the special conditions. Thus, it is not only very difficult, but also taken too much time to develop the appropriate job schedules that satisfy the complex work conditions. The application areas of these algorithms are also very narrow. Under these circumstances it is very desirable to develop a simple job sequencing method which can produce the good solution with the short tine period under any complex work conditions. It is called a sampling job sequencing method in this study. This study is to examine the selection of the good job sequence of 1%-5% upper group by the sampling method. The result shows that there is the set of 0.5%-5% job sequence group which has to same amount of performance measure with the optimal job sequence in the case of experiment of 2/n/F/F max. This indicates that the sampling job sequencing method is a useful job sequencing method to find the optimal or good job sequence with a little effort and time consuming. The results of ANOVA show that the two factors, number of jobs and the range of processing time are the significant factors for determining the job sequence at $\alpha$=0.01. This study is extended to 3 machines to machines job shop problems further.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2007.04a
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• pp.309-314
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• 2007

Optimal layer sequencing of a multi-layered acoustical foam is solved to maximize its sound transmission loss. A foam consisting of air and poroelastic layers can be optimized when a limited amount of a poroelastic material is allowed. By formulating the sound transmission loss maximization problem as a one dimensional topology optimization problem, optimal layer sequencing and thickness were systematically found for several frequencies. For optimization, the transmission losses of air and poroelastic layers were calculated by the transfer matrix derived from Biot's theory. By interpolating five intrinsic parameters among several poroelastic material parameters, dear air-poroelastic layer distributions were obtained; no filtering or post-processing was necessary. The optimized foam layouts by the proposed method were shown to differ depending on the frequency of interest.

• Proceedings of the Safety Management and Science Conference
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• 2008.11a
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• pp.35-39
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• 2008

This paper considers the sequencing of products in mixed model assembly lines. The sequence which minimizes overall utility work in car assembly lines reduce the cycle time, the number of utility workers, and the risk of conveyor stopping. The sequencing problem is solved using Tabu Search. Tabu Search is a heuristic method which can provide a near optimal solution in real time. Various examples are presented and experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.3
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• pp.25-43
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• 2010

We consider a multi-objective balancing and sequencing problem in mixed model assembly lines, which is important for an efficient use of the assembly lines. In this paper, we present a neighborhood symbiotic evolutionary algorithm to simultaneously solve the two problems of balancing and model sequencing under multiple objectives. We aim to find a set of well-distributed solutions close to the true Pareto optimal solutions for decision makers. The proposed algorithm has a two-leveled structure. At Level 1, two populations are operated : One consists of individuals each of which represents a partial solution to the balancing problem and the other consists of individuals for the sequencing problem. Level 2, which is an upper level, works one population whose individuals represent the combined entire solutions to the two problems. The process of Level 1 imitates a neighborhood symbiotic evolution and that of Level 2 simulates an endosymbiotic evolution together with an elitist strategy to promote the capability of solution search. The performance of the proposed algorithm is compared with those of the existing algorithms in convergence, diversity and computation time of nondominated solutions. The experimental results show that the proposed algorithm is superior to the compared algorithms in all the three performance measures.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1117-1122
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• 1993

The environment for surface mounting machines plays an important role in a throughput. An approach to organize the optimal integrated environment for surface mounting machines is presented to increase a throughput. An optimization problem is divided into a feeder setting problem and a task sequencing problem. Two algorithms for each problem are proposed. The feeder setting problems is optimized by an algorithm based on heuristic methods. The task sequencing problem is modeled as a TSP(Traveling salesman problem). An algorithm based on a heuristic tour-to-tour improvement method for TSP is proposed to optimize the task sequencing problem. A simulation is carried out to test developed algorithms.

• Journal of the Korean Operations Research and Management Science Society
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• v.13 no.2
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• pp.18-24
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• 1988

We consider sensitivity analysis sequencing problems, in which sequence of a finite set of expansion projects is sought to meet a deterministic demand projection in minimum discounted cost. In particular, by characterizing the underlying network structure, we find analytically the sensitivity range for a project cost such that the optimal sequencing policy remains unchanged for any value in the range. A numerical example is presented.