Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
Current Result Document :
ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
Solving the test resource allocation using variable group genetic algorithm |
ÀúÀÚ(Author) |
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Chang-min Mun
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¿ø¹®¼ö·Ïó(Citation) |
VOL 20 NO. 08 PP. 1415 ~ 1421 (2016. 08) |
Çѱ۳»¿ë (Korean Abstract) |
¹«±âü°èÀÇ ±â´É ¹× ¼º´É °ËÁõÀ» À§ÇÑ ½ÃÇèµéÀÌ Áö¼ÓÀûÀ¸·Î Áõ°¡ÇÔ¿¡ µû¶ó °¡¿ë ÀÚ¿øµéÀÇ È¿À²ÀûÀÎ È°¿ëÀ» À§ÇÑ ¹æ¾È¿¡ °ü·ÃµÈ ¿¬±¸°¡ ´ëµÎµÇ°í ÀÖÀ¸¸ç, ÀÚ¿øÇÒ´ç º¹Àâµµ°¡ Áõ°¡ÇÔ¿¡ µû¶ó ½ÃÇè°èȹ ½Ã¿¡ ÀÇ»ç°áÁ¤ Áö¿øÀÌ ¿ä±¸µÇ°í ÀÖ´Ù. ½ÃÇèÀÚ¿øÇÒ´çÀº ÀüÅëÀûÀÎ FJSP(Flexible Job Shop Problem)¿Í ±âº»ÀûÀ¸·Î µ¿ÀÏÇÑ ¹®Á¦À̸ç, ÀÌ´Â NP-hard¹®Á¦·Î¼ ±âÁ¸ÀÇ °æÇè±â¹Ý ½ÃÇèÀÚ¿ø ÇÒ´ç ¹æ¹ýÀ¸·Î´Â ½Ã°£ È¿À²ÀûÀÎ ÀÚ¿øÇÒ´ç¿¡ ÀÖ¾î¼ ÇÑ°è°¡ Á¸ÀçÇÑ´Ù. FJSP¿¡ À¯ÀüÀÚ¾Ë°í¸®ÁòÀ» Àû¿ëÇÑ ÃÖÀûÇØ Å½»ö ¿¬±¸°¡ ÁøÇàµÇ¾î ¿ÔÁö¸¸, ÇϳªÀÇ ±â°èÁ¶ÀÛ¿¡ ´ëÇØ µÎ °³ ÀÌ»ó ±â°èÀÇ µ¿½Ã ÀÛµ¿ÀÌ ÇÊ¿äÇÑ ½ÃÇèÀÚ¿øÇÒ´ç µµ¸ÞÀο¡¼ÀÇ Àû¿ëÀº Á¦ÇÑÀûÀÌ´Ù. ÀÌ¿¡ º» ³í¹®¿¡¼´Â °¡º¯ ±×·ì À¯ÀüÀÚ¾Ë°í¸®ÁòÀ» Á¦¾ÈÇÑ´Ù. Á¦¾ÈÇÏ´Â ¾Ë°í¸®ÁòÀº ¼öÀÛ¾÷ ±â¹ÝÀÇ ±âÁ¸ ½ÃÇèÀÚ¿øÇÒ´çÀ» ÀÚµ¿ÈÇÏ°í ÃÖÀûÈÇÔÀ¸·Î½á ½ÃÇè È¿À²À» Çâ»ó½Ãų °ÍÀ¸·Î ±â´ëµÇ¸ç, MATLABÀ» ÀÌ¿ëÇÑ ½Ã¹Ä·¹À̼ÇÀ» ÅëÇØ ±× Àû¿ë¼ºÀ» È®ÀÎÇÏ¿´´Ù.
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¿µ¹®³»¿ë (English Abstract) |
There are considerable concern on the methods for the efficient utilization of the test-resources as increasing of the number of the tests for functionality and performance verification of weapon systems. Furthermore, with an increase in
the complexity of the resource assignment the decision support is required. Test resource allocation is basically the same problems as conventional NP-hard FJSP(Flexible Job Shop Problem), therefore empirical test resource allocation
method that has been used in many decades is limited in the time performance. Although research has been conducted applying the genetic algorithm to the FJSP, it is limited in the test resource allocation domain in which more than one machine is necessary for a single operation. In this paper, a variable group genetic algorithm is proposed. The algorithm is expected to improve the test plan efficiency by automating and optimizing the existing manual based allocation. The simulation result shows that the algorithm could be applicable to the test plan.
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Å°¿öµå(Keyword) |
À¯ÀüÀÚ ¾Ë°í¸®Áò
ÀÇ»ç°áÁ¤Áö¿ø
½ÃÇè°èȹ
½ÃÇèÀÚ¿øÇÒ´ç
Genetic Algorithm
Decision Making Support
Test Plan
Test Resource Allocation
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