• Journal of the Korean Operations Research and Management Science Society
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• v.28 no.3
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• pp.123-134
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• 2003

Fire Sequencing Problem (FSP) is to find a sequence of targets, where there exist a number of targets with different time units required to fire. Because of the weapon's specifications and the size of the targets, several weapons may fire on the same targets, and the time units required on firing for each weapon may be different from each other. The objective is to minimize the completion time of firing for given number of targets. Mathematical formulation is given, and the heuristic algorithm based on the paring of targets in advance is suggested. Performance of the heuristic is evaluated by comparison of heuristic appeared in the literature through the computational experiments.

• Industrial Engineering and Management Systems
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• v.15 no.1
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• pp.1-10
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• 2016

This paper presents a fast sequencing algorithm for a mixed model assembly line with multiple workstations which minimize the total utility work and idle time. We compare the proposed algorithms with another heuristic, the Tsai-based heuristic, for a sequencing problem that minimizes the total utility works. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. The Tsai-based heuristic performs best in terms of utility work, but the fast sequencing algorithm performs well for both utility work and idle time. However, the computational complexity of the fast sequencing algorithm is O (KN) while the Tsai-based algorithm is O (KNlogN). Actual computational time of the fast sequencing heuristic is 2-6 times faster than that of the Tsai-based heuristic.

• Genomics & Informatics
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• v.12 no.1
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• pp.2-11
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• 2014

After the initial enthusiasm of the human genome project, it became clear that without additional data pertaining to the epigenome, i.e., how the genome is marked at specific developmental periods, in different tissues, as well as across individuals and species-the promise of the genome sequencing project in understanding biology cannot be fulfilled. This realization prompted several large-scale efforts to map the epigenome, most notably the Encyclopedia of DNA Elements (ENCODE) project. While there is essentially a single genome in an individual, there are hundreds of epigenomes, corresponding to various types of epigenomic marks at different developmental times and in multiple tissue types. Unprecedented advances in next-generation sequencing (NGS) technologies, by virtue of low cost and high speeds that continue to improve at a rate beyond what is anticipated by Moore's law for computer hardware technologies, have revolutionized molecular biology and genetics research, and have in turn prompted innovative ways to reduce the problem of measuring cellular events involving DNA or RNA into a sequencing problem. In this article, we provide a brief overview of the epigenome, the various types of epigenomic data afforded by NGS, and some of the novel discoveries yielded by the epigenomics projects. We also provide ample references for the reader to get in-depth information on these topics.

• 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.

• Korean Management Science Review
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• v.13 no.1
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• pp.13-27
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• 1996

This paper considers the sequencing problem in mixed model assembly lines with hybrid workstation types and sequence-dependent setup times. Computation time is often a critical factor in choosing a method of determining the sequence. We develop a mathematical formulation of the problem to minimize the overall length of a line, and present a tabu search technique which can provide a near optimal solution in real time. The proposed technique is compared with a genetic algorithm and a branch-and-bound method. Experimental results are reported to demonstrate the efficiency of the technique.

• Journal of the military operations research society of Korea
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• v.31 no.2
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• pp.28-44
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• 2005

The previous studies approach the field artillery fire scheduling problem as deterministic and do not explicitly include information on the potential scenario changes. Unfortunately, the effort used to optimize fire sequences and reduce the total time of engagement is often inefficient as the collected military intelligence changes. Instead of modeling the fire sequencing problem as deterministic model, we consider a stochastic artillery fire scheduling model and devise a solution methodology to integrate possible enemy attack scenarios in the evaluation of artillery fire sequences. The goal is to use that information to find robust solutions that withstand disruptions in a better way, Such an approach is important because we can proactively consider the effects of certain unique scheduling decisions. By identifying more robust schedules, cascading delay effects will be minimized. In this paper we describe our stochastic model for the field artillery fire sequencing problem and offer revised robust stochastic model which considers worst scenario first. The robust stochastic model makes the solution more stable than the general two-stage stochastic model and also reduces the computational cost dramatically. We present computational results demonstrating the effectiveness of our proposed method by EVPI, VSS, and Variances.

• IE interfaces
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• v.10 no.2
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• pp.51-56
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• 1997

This paper concerns with the problem of mixed model assembly sequencing using neural net. In recent years, because of two characteristics of it, massive parallelism and learning capability, neural nets have emerged to solve the problems for which more conventional computational approaches have proven ineffective. This paper proposes a method using neural net that can consider line balancing and grouping problems simultaneously. In order to solve the mixed model assembly sequencing of the motor industry, this paper uses the modified ART1 algorithm.

• 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.

• IE interfaces
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• v.15 no.2
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• pp.189-198
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• 2002

Simulated Annealing(SA) approach has been successfully applied to the combinatorial optimization problems with NP-hard complexity. To apply an SA algorithm to specific problems, generic parameters as well as problem-specific parameters must be determined. To overcome the embedded nature of SA, long computational time, some studies suggested the parameter design methods of determining SA related parameters. In this study, we propose a new parameter design approach based on robust design method. To show the effectiveness of the proposed method, the extensive computation experiments are conducted on the mixed-model sequencing problems.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 1998.10a
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• pp.731-740
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• 1998

This paper discusses a brcriterion approachto sequencing with time/cost trade-offs. The first problem is to minimize the total flow time and the maximum tardiness. And second is to the maximum tardiness and resource allocation costs. This approach , which produces an efficient frontier of possible schedules, has the advantage that it does not require the sequencing criteria to be measurable in the same units as the resource allocation cost. The basic single machine model is used to treat a class of problems in which the sequencing objective is to minimize the maximum completion penalty. It is further assumed that resource allocation costs can be represented by linear time/cost function.