• 산업경영시스템학회지
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• 제39권3호
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• pp.83-89
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• 2016

The group formation problem of the machine and part is a critical issue in the planning stage of cellular manufacturing systems. The machine-part grouping with alternative process plans means to form machine-part groupings in which a part may be processed not only by a specific process but by many alternative processes. For this problem, this study presents an algorithm based on self organizing neural networks, so called SOM (Self Organizing feature Map). The SOM, a special type of neural networks is an intelligent tool for grouping machines and parts in group formation problem of the machine and part. SOM can learn from complex, multi-dimensional data and transform them into visually decipherable clusters. In the proposed algorithm, output layer in SOM network had been set as one-dimensional structure and the number of output node has been set sufficiently large in order to spread out the input vectors in the order of similarity. In the first stage of the proposed algorithm, SOM has been applied twice to form an initial machine-process group. In the second stage, grouping efficacy is considered to transform the initial machine-process group into a final machine-process group and a final machine-part group. The proposed algorithm was tested on well-known machine-part grouping problems with alternative process plans. The results of this computational study demonstrate the superiority of the proposed algorithm. The proposed algorithm can be easily applied to the group formation problem compared to other meta-heuristic based algorithms. In addition, it can be used to solve large-scale group formation problems.

• 국제학술발표논문집
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• The 6th International Conference on Construction Engineering and Project Management
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• pp.174-178
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• 2015

This paper presents a new method for forecasting construction project cost and time at completion or at any intermediate time horizon of the project duration. The method is designed to overcome identified limitations of current applications of earned value method in forecasting project cost and time. The proposed method usesfuzzy set theory to model uncertainties associated with project performance and it integrates the earned value technique and the contractors' judgement. The fuzzy set theory is applied as an alternative approach to deterministic and probabilistic methods. Using fuzzy set theory allows contractors to: (1) perform risk analysis for different scenarios of project performance indices, and (2) perform different scenarios expressing vagueness and imprecision of forecasted project cost and time using a set of measures and indices. Unlike the current applications of Earned Value Method(EVM), The proposed method has a numberof interesting features: (1) integrating contractors' judgement in forecasting project performance; (2) enabling contractors to evaluate the risk associated with cost overrun in much simpler method comparing with that of simulation, and (3) accounting for uncertainties involved in the forecasting project cost.

• 한국대기환경학회지
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• 제6권1호
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• pp.51-56
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• 1990

This Study shows the effect of the district heating on the $SO_2$ concentration reduction. In order to analyze the effect of the district heating, three alternatives were set up as follows; Alternative I represented present central heating system, and Alternative II and Alternative III represented district heating system of which the scale were different from. The concludions of this study are as follows; In case of the Alternative II and III, annual average $SO_2$ concentration are reduced by 9.0% and 14.6% respectively, and winter season $SO_2$ concentrations are reduced by 12.2% and 15.8% respectively, at the highest points. The average reduction rates of $SO_2$ concentration in the district heating area are about the same as the reduction rates at the highest points. Also, it was found that using the district heating system, the ground level $SO_2$ concentrations could be reduced within the area of 5 to 10 km radius.

• 품질경영학회지
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• 제47권4호
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• pp.701-711
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• 2019

Purpose: The problem of optimizing redundancy allocation in multi-level systems is considered when each item in a multi-level system has alternative items with the same function. The number of redundancy of multi-level system is allocated to maximize the reliability of the system under path set and cost limitation constraints. Methods: Based on cost limitation and path set constraints, a mathematical model is established to maximize system reliability. Particle swarm optimization is employed for redundant allocation and verified by numerical experiments. Results: Comparing the particle swarm optimization method and the memetic algorithm for the 3 and 4 level systems, the particle swarm optimization method showed better performance for solution quality and search time. Particularly, the particle swarm optimization showed much less than the memetic algorithm for variation of results. Conclusion: The proposed particle swarm optimization considerably shortens the time to search for a feasible solution in MRAP with path set constraints. PS optimization is expected to reduce search time and propose the better solution for various problems related to MRAP.

• Management Science and Financial Engineering
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• 제20권2호
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• pp.1-5
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• 2014

The robust inventory control problem was proposed and solved by Bertsimas and Thiele (2006). Their results are very interesting in that the problem can be solved easily and also the solution possesses nice properties of those found in the traditional stochastic inventory control problem. However, their formulation is shown to be incorrect, which invalidates all of the results given there. In this paper, we propose an alternative formulation of the problem which uses a different but practically applicable uncertainty set. Under the newly proposed model, all of the useful properties given in Bertsimas and Thiele (2006) will be shown to be valid.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권4호
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• pp.1757-1772
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• 2020

As the smartphone market becomes saturated, an innovative device equipped with new features is expected to appear soon in mobile communications. In particular, various possibilities were raised regarding the alternative technologies that can develop post-smartphones, which are differentiated from the current smartphones, as Korea commercialized the 5G infrastructure for the first time in the world. Under these circumstances, the Korean government announced the "5G+ Strategy for Realizing Innovative Growth" in April 2019, vowing to build an innovative industrial ecosystem quickly while creating various convergence services based on the 5G infrastructure. As described above, the policy importance of the alternative technologies that will develop post-smartphones is increasing, but the theoretical study on the technology evolution of post-smartphones has not been systematically conducted until now. This study reviewed the alternative technologies that can develop post-smartphones through documentary research, and data mining analysis was performed on the research result using actual data. The policy priority was also set quantitatively for the alternative technologies of post-smartphones in order to determine the alternative post-smartphone technology that the government should focus on given the constraint of limited resources. As a results, autonomous vehicle(43.68%) was found to be most important, followed by artificial intelligence(17.4%) and Internet of Things(13.1%), among alternative technologies that could develop into the post-smartphone.

• Asia Marketing Journal
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• 제18권3호
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• pp.47-63
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• 2016

Rational choice theory holds that the alternative with largest expected utility in the choice set should always be chosen. However, it is often observed that an alternative with the largest expected utility is not always chosen while the choice task itself being avoided. Such a choice phenomenon cannot be explained by the traditional expected utility maximization principle. The current study posits shows that such a phenomenon can be attributed to the gap between the expected perceived gain (or loss) and the expected perceived value. This study mathematically analyses the relationship between the expectation of an alternative's gains or losses over the reference point and its expected value, when the perceived gains or losses follow continuous probability distributions. The proposed expected value (EV) function can explain the effects of loss aversion and uncertainty on the evaluation of an alternative based on the prospect theory value function. The proposed function reveals why the expected gain of an alternative should exceed some positive threshold in order for the alternative to be chosen. The model also explains why none of the two equally or similarly attractive options is chosen when they are presented together, but either of them is chosen when presented alone. The EV function and EG-EV curve can extract and visualize the core tenets of the prospect theory more clearly than the value function itself.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.1565-1568
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• 1997

In this paper, a fuzzy based machine-cell formation algorithm for cellular manufacturing is presented. The fuzzy lovic is employed to express the degree of appropriateness when alternative machnies are specified to process a part shape. For machine grouping, the similarity coefficient based approach is used. The algorithm produces efficient machine cells and part families which maximize the similarity values.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.203-210
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• 1996

A linear neural unit with a modified anti-Hebbian learning rule has been shown to be able to optimally fit curves, surfaces, and hypersurfaces by adaptively extracting the minor component of the input data set. In this paper, we study how to use the robust version of this neural fitting method for linear regression analysis. Furthermore, we compare this method with other methods when data set is contaminated by outliers.

• Journal of the Korean Statistical Society
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• 제14권1호
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• pp.7-17
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• 1985

Data set generated by teh bivariate dichotomous choice made by individuals often occurs in practice. This paper presents general model of how such data set is generated as well as methods of estimation. The M.L.E. is examined and found to be computationally burdensome. A simpler estimator, the bivariate dichotomous two-stage estimator, is suggested as an alternative. The two-stage estimator is found to be as efficient as the M.L.E.