• Korean Management Science Review
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• v.32 no.1
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• pp.35-47
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• 2015

The purpose of this paper is to find an optimal weaving connection method of sweater threads while weaving intarsia sweater by the genetic algorithm. The objective function was devised to minimize labor cost and lessen the amount of thread usage. In order to create the parental population group in the genetic algorithm, we developed five thread connection methods. Besides, elite chromosome screening methods for the offspring group was selected both to the whole chromosome thread elite and to a color-coded elite thread chromosome. Commonly used diamond pattern in Intarsia sweater manufacturing was applied to the experiments. The experimental results showed that thread system saved the labor and material costs than woven method under the existing software. When weaving Intarsia sweater in the field, we can apply the developed genetic algorithm to improve productivity of weaving connection method.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.4
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• pp.133-141
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• 2001

Rapid development in computer and network technology these days has created in environment in which decisions for manufacturing companies can be made in a much broader perspective. Especially, better decisions on production and distribution planning(PDP) problems can be made laking advantage of real time information from all the parties concerned. However, since the PDP problem-a core part of the supply chain management- is known to be the so-called NP-hard problem, so heuristic methods are dominantly used to find out solutions in a reasonable time. As one of those heuristic techniques, many previous studios considered genetic a1gorithms. A standard genetic a1gorithm applies rules of reproduction, gene crossover, and mutation to the pseudo-organisms so the organisms can pass along beneficial and survival-enhancing trails to a new generation. When it comes to representing a chromosome on the problem, it is hard to guarantee an evolution of solutions through classic a1gorithm operations alone, for there exists a strong epitasis among genes. To resolve this problem, we propose a hybrid genetic a1gorithm based on Silver-Meal heuristic. Using IMS-TB(Intelligent Manufacturing System Test-bed) problem sets. the good performance of the proposed a1gorithm is demonstrated.

• Proceedings of the KIEE Conference
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• 2006.10c
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• pp.120-122
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• 2006

The paper concerns the hybrid optimization of fuzzy inference systems that is based on Hierarchical Fair Competition-based Parallel Genetic Algorithms (HFCGA) and information data granulation. The granulation is realized with the aid of the Hard C-means clustering and HFCGA is a kind of multi-populations of Parallel Genetic Algorithms (PGA), and it is used for structure optimization and parameter identification of fuzzy model. It concerns the fuzzy model-related parameters such as the number of input variables to be used, a collection of specific subset of input variables, the number of membership functions, the order of polynomial, and the apexes of the membership function. In the hybrid optimization process, two general optimization mechanisms are explored. Thestructural optimization is realized via HFCGA and HCM method whereas in case of the parametric optimization we proceed with a standard least square method as well as HFCGA method as well. A comparative analysis demonstrates that the proposed algorithm is superior to the conventional methods.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2003.06a
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• pp.1006-1011
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• 2003

In this study, a novel systematic design procedure by Genetic Algorithm of a two stage relief valve is proposed. First of all, a mathematical model describing the dynamics of a balanced piston type relief valve has been derived. Governing equations such as dynamic equations for the main spool and the pilot spool and flow equations for each orifice are established. The mathematical model is verified by comparing the results of simulation with that of experiments. Furthermore, influences of the parameters on the dynamic characteristics of a relief valve have been investigated by simulation of the proposed model. Major design parameters on the valve response are determined, which affect the system response significantly. And then, using the determined parameters, the optimization of the two stage relief valve by Genetic Algorithm, which is a random search algorithm can find the global optimum without converging local optimum, is performed. The optimal design process of a two stage relief valve is presented to determine the major design parameters. Fitness function reflects the changing pressure according to parameters. It is shown that the genetic algorithms satisfactorily optimized the major design parameters of the two stage relief valve.

• Journal of Integrative Natural Science
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• v.8 no.2
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• pp.117-127
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• 2015

Lentil (Lens culinaris Medik.) is one of the important legumes and cheaper source of protein in Bangladesh that displays great biological diversity. Isozyme, one of the most important protein markers to detect genetic polymorphism in lentil, whereas we considered thirteen-isozyme in six varieties viz., BARI masur-1, BARI masur-2, BARI masur-3, BARI masur-4, BARI masur-5 and BARI masur-6. The highest polymorphism was found in tyrosinase isozyme system. UPGMA analysis revealed that the highest similarity between BARI masur-5 and BARI masur-6 whereas, the highest genetic distance between BARI masur-1 and BARI masur-5 reflecting higher intervarietal variation. Principal component analysis (PCA) also revealed the similar results that of unweighted pair group method with arithmetic mean (UPGMA). The first, second and third PCs contributed 81.58%, 11.19% and 4.94% variation respectively, with cumulative variation of the first three PCs was 75.45%. Consequently, Isozyme could clearly assed the genetic diversity at intervarietal levels and these two varieties can be considered as valuable gene resources for future breeding and conservation programs.

• Journal of the Korean Society for Precision Engineering
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• v.19 no.10
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• pp.66-76
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• 2002

Automated welding and soldering are an important manufacturing issue in order to lower the cost, increase the quality, and avoid labor problems. An off-line programming, OLP, is one of the powerful methods to solve this kind of diversity problem. Unless an OLP system is ready for the path optimization in welding and soldering, the waste of time and cost is unavoidable due to inefficient paths in welding and soldering processes. Therefore, this study attempts to obtain path optimization using a genetic algorithm based on artificial intelligences. The problem of welding path optimization is defined as a conventional TSP (traveling salesman problem), but still paths have to go through welding lines. An improved genetic algorithm was suggested and the problem was formulated as a TSP problem considering the both end points of each welding line read from database files, and then the transit problem of welding line was solved using the improved suggested genetic algorithm.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2006.11a
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• pp.511-514
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• 2006

Material Management is one of the most important task in construction projects. More than 50% of the cost in a construction project is related to material management process. Material management method using RFID(Radio Frequency Identification) is now trying to the construction field. However, there are no enough researches on effective material management in terms of how and where RFID transponder should be installed and there are no other research that which optimization method can be used for effective installation. Therefore, this paper suggest that where and how RFID transponder can be installed on the appropriate position in construction fields using Genetic Algorithm optimization method.

• Journal of the Korean Operations Research and Management Science Society
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• v.30 no.1
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• pp.43-54
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• 2005

A flexible assembly line (FAL) is a production system that assembles various parts in unidirectional flow line with many constraints and manufacturing flexibilities. In this research we deal with a FAL balancing problem with the objective of minimizing the maximum workload allocated to the stations. However, almost all the existing researches do not appropriately consider various constraints due to the problem complexity. Therefore, this study addresses a balancing problem of FAL with many constraints and manufacturing flexibilities, unlike the previous researches. We use a genetic algorithm (GA) to solve this problem. To apply GA to FAL. we suggest a genetic representation suitable for FAL balancing and devise evaluation method for individual's fitness and genetic operators specific to the problem, including efficient repair method for preserving solution feasibility. After we obtain a solution using the proposed GA. we use a heuristic method for reassigning some tasks of each product to one or more stations. This method can improve workload smoothness and raise work efficiency of each station. The proposed algorithm is compared and analyzed in terms of solution quality through computational experiments.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.46 no.5
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• pp.416-419
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• 2001

Hypernodulation soybean mutant, SS2-2, is characterized with greater nodulation and nitrogen fixing ability in the root nodule than its wild type, Shinpaldalkong 2. The present study was performed to identify a genetic locus conferring hypernodulation in soybean mutant SS2-2 and to determine whether the gene controlling the hypernodulation of SS2-2 is allelic to that controlling the supernodulation of nts382 mutant. Hybridization studies between SS2-2 and Taekwangkong revealed that the recessive gene was responsible for the hypernodulation character in soybean mutant SS2-2. Allelism was also tested by crossing supernodulating mutant nts382 and hypernodulating mutant SS2-2 that both hypernodulation and supernodulation genes were likely controlled by an identical locus. Molecular marker mapping of hypernodulation gene in SS2-2 using SSR markers confirmed that the gene conferring hypernodulation was located at the same loci with the gene conferring supernodulation. It is interesting to note that the same gene controlled the super- and hyper-nodulation characters, although SS2-2 and nts 382 exhibited differences in the amount of nodulation in the root system. Further genetic studies should be needed to clarify the genetic regulation of super- and hyper-nodulation in soybean.

• Journal of the Korean Society for Precision Engineering
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• v.24 no.4 s.193
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• pp.84-92
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• 2007

Dynamic process planning requires not only more flexible capabilities of a CAPP system but also higher utility of the generated process plans. In order to meet the requirements, this paper develops an algorithm that can select machines for the machining operations by calculating the machine loads. The developed algorithm is based on the multi-objective genetic algorithm that gives rise to a set of optimal solutions (in general, known as the Pareto-optimal solutions). The objective is to satisfy both the minimization number of part movements and the maximization of machine utilization. The algorithm is characterized by a new and efficient method for nondominated sorting through K-means algorithm, which can speed up the running time, as well as a method of two stages for genetic operations, which can maintain a diverse set of solutions. The performance of the algorithm is evaluated by comparing with another multiple objective genetic algorithm, called NSGA-II and branch and bound algorithm.