‫ﺍﺑﺰﺍﺭﻱ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺑﺎ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ‬
‫ﻋﻠﻲ ﮐﻤﻨﺪﻱ‪ ،‬ﻣﺤﻤﺪ ﻋﺒﺪﺍﻟﻠﻬﻲ ﺍﺯﮔﻤﻲ ﻭ ﻋﻠﻲ ﻣﻮﻗﺮ ﺭﺣﻴﻢﺁﺑﺎﺩﻱ‬
‫ﺩﺍﻧﺸﮑﺪﻩ ﻣﻬﻨﺪﺳﻲ ﮐﺎﻣﭙﻴﻮﺗﺮ‬
‫ﺩﺍﻧﺸﮕﺎﻩ ﺻﻨﻌﺘﻲ ﺷﺮﻳﻒ‬
‫‪[email protected]‬‬
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‫‪[email protected]‬‬
‫ﭼﮑﻴﺪﻩ‪ :‬ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ )‪ (SANs‬ﻳﮑﻲ ﺍﺯ ﺑﺴﻂﻫﺎﻱ ﻗﻮﻱ ﻭ ﻗﺎﺑﻞ ﺍﻧﻌﻄﺎﻑ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ‪ ١‬ﺍﺳﺖ‪ .‬ﺍﺧﻴﺮﹰﺍ ﻳﮏ ﺑﺴﻂ ﺷﺊﮔﺮﺍ ﺑﺎ‬
‫ﻧﺎﻡ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ )‪ (OSAN: Object Stochastic Activity Networks‬ﺑﺮﺍﻱ ﺍﻳﻦ ﻣﺪﻟﻬﺎ ﻣﻌﺮﻓﻲ ﺷﺪﻩ‬
‫ﺍﺳﺖ‪ .‬ﻫﺪﻑ ﺍﺯ ﺍﻳﻦ ﺑﺴﻂ ﻓﺮﺍﻫﻢﺳﺎﺯﻱ ﺗﺴﻬﻴﻼﺕ ﺳﻄﺢ ﺑﺎﻻ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺳﻴﺴﺘﻢﻫﺎﻱ ﭘﻴﭽﻴﺪﻩ ﻭ ﺑﺰﺭﮒ ﺍﺳﺖ‪ .‬ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺗﺤﻠﻴﻞ‬
‫ﺑﺎ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺷﻴﺌﻲ ﻧﻴﺎﺯ ﺑﻪ ﻳﮏ ﺍﺑﺰﺍﺭ ﻣﺪﻟﺴﺎﺯﻱ ﺍﺳﺖ ﮐﻪ ﺳﺎﺧﺖ ﻣﺪﻝ ﻭ ﺗﺤﻠﻴﻞ ﺟﻨﺒﻪﻫﺎﻱ ﻣﻨﻄﻘﻲ ﻭ ﻋﻤﻠﻴﺎﺗﻲ ﺁﻧﺮﺍ ﺍﻣﮑﺎﻧﭙﺬﻳﺮ ﻧﻤﺎﻳﺪ‪.‬‬
‫ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺍﺑﺰﺍﺭﻱ ﺭﺍ ﻣﻌﺮﻓﻲ ﺧﻮﺍﻫﻴﻢ ﮐﺮﺩ ﮐﻪ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺑﺎ ‪ OSAN‬ﻃﺮﺍﺣﻲ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺍﻣﮑﺎﻥ ﻭﻳﺮﺍﻳﺶ ﻭ‬
‫ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ ﻭ ﺣﻞ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﻣﺪﻝ ﺑﺎ ﺭﻭﺷﻬﺎﻱ ﺗﺤﻠﻴﻠﻲ ﻭ ﺷﺒﻴﻪﺳﺎﺯﻱ ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﮐﻨﺪ‪ .‬ﺑﻪ ﻣﻨﻈﻮﺭ ﻧﺰﺩﻳﮏ ﮐﺮﺩﻥ ﻣﻔﺎﻫﻴﻤﻲ ﺍﺯ‬
‫ﻗﺒﻴﻞ ﺩﺭﺳﺘﻲﻳﺎﺑﻲ ﻭ ﺍﺭﺯﻳﺎﺑﻲ ﮐﺎﺭﺁﻳﻲ‪ ٢‬ﺑﺎ ﻣﻔﺎﻫﻴﻤﻲ ﺍﺯ ﻗﺒﻴﻞ ﻣﺪﻟﺴﺎﺯﻱ ﺷﻲﺀﮔﺮﺍ )‪ (OOM‬ﻭ ﻣﺪﻟﺴﺎﺯﻱ ﻓﺮﺁﻳﻨﺪﻫﺎﻱ ﺗﺠﺎﺭﻱ )‪ (BPM‬ﺍﻳﻦ‬
‫ﺍﺑﺰﺍﺭ ﺭﺍ ﺑﻪ ﺻﻮﺭﺕ ﻳﮏ ‪ Plug-In‬ﺭﻭﻱ ‪ Together‬ﺳﺎﺧﺘﻪﺍﻳﻢ‪ .‬ﺑﻪ ﺍﻳﻦ ﺗﺮﺗﻴﺐ ﺑﺮﺧﻲ ﺍﺯ ﺟﻨﺒﻪﻫﺎﻱ ﺳﻴﺴﺘﻢ ﺑﻪ ﮐﻤﮏ ‪ ٣UML‬ﻭ ﺟﻨﺒﻪ‬
‫ﻫﺎﻱ ﺩﻳﮕﺮ ﺁﻥ ﺑﻪ ﮐﻤﮏ ‪ OSAN‬ﻗﺎﺑﻞ ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺗﺤﻠﻴﻞ ﺍﺳﺖ‪.‬‬
‫ﮐﻠﻤﺎﺕ ﮐﻠﻴﺪﻱ‪ :‬ﺷﺒﮑﻪ ﭘﺘﺮﻱ‪ ،‬ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ‪ ،‬ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ‪ ،‬ﺍﺑﺰﺍﺭ ﻣﺪﻟﺴﺎﺯﻱ‬
‫‪ .١‬ﻣﻘﺪﻣﻪ‬
‫ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ )‪ (SANs‬ﻳﮑﻲ ﺍﺯ ﺑﺴﻂﻫﺎﻱ ﻗﻮﻱ ﻭ ﻗﺎﺑﻞ ﺍﻧﻌﻄﺎﻑ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ ﺍﺳﺖ‪ .‬ﺍﺧﻴﺮﹰﺍ ﻳﮏ ﺑﺴﻂ ﺷﺊﮔﺮﺍ ﺑﺎ ﻧﺎﻡ‬
‫ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ )‪ (OSAN: Object Stochastic Activity Networks‬ﺑﺮﺍﻱ ﺍﻳﻦ ﻣﺪﻟﻬﺎ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫ﻫﺪﻑ ﺍﺯ ﺍﻳﻦ ﺑﺴﻂ ﻓﺮﺍﻫﻢﺳﺎﺯﻱ ﺗﺴﻬﻴﻼﺕ ﺳﻄﺢ ﺑﺎﻻﻱ ﻣﻨﺎﺳﺐ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺳﻴﺴﺘﻢﻫﺎﻱ ﭘﻴﭽﻴﺪﻩ ﻭ ﺑﺰﺭﮒ ﺍﺳﺖ‪ .‬ﻣﺪﻝ ﺷﺒﮑﻪﻫﺎﻱ‬
‫‪Petri Net 1‬‬
‫‪Performance 2‬‬
‫‪Unified Modeling Language 3‬‬
‫ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ]‪ [۱‬ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ﻣﻨﺎﺳﺒﻲ ﺭﺍ ﺟﻬﺖ ﻣﺪﻝ ﮐﺮﺩﻥ ﺳﻴﺴﺘﻢ ﻭ ﺍﺭﺯﻳﺎﺑﻲ ﻣﻌﻴﺎﺭﻫﺎﻱ ﮐﺎﺭﺁﻳﻲ ﻭ ﺍﺗﮑﺎﺀﭘﺬﻳﺮﻱ‪ ١‬ﺁﻥ ﻓﺮﺍﻫﻢ ﺁﻭﺭﺩﻩ‬
‫ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﻣﺪﻝ ﺑﺎ ﻣﻌﺮﻓﻲ ﻣﻔﺎﻫﻴﻤﻲ ﭼﻮﻥ ﻓﻌﺎﻟﻴﺘﻬﺎﻱ ﻣﺎﮐﺮﻭ‪ ٢‬ﻭ ﻣﮑﺎﻧﻬﺎﻱ ﺭﻧﮕﻲ‪ ٣‬ﻭ ﺑﺎ ﺣﻔﻆ ﺗﻤﺎﻡ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ﻣﺪﻟﻬﺎﻱ ‪ ،SAN‬ﮔﺰﻳﻨﻪ ﻣﻨﺎﺳﺒﻲ‬
‫ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺳﻴﺴﺘﻤﻬﺎﻱ ﻫﻤﺮﻭﻧﺪ ﻭ ﺗﻮﺯﻳﻊ ﺷﺪﻩ ﻣﻲﺑﺎﺷﺪ‪ .‬ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺗﺤﻠﻴﻞ ﺷﺒﮑﻪﻫﺎﻱ ‪ OSAN‬ﻧﻴﺎﺯ ﺑﻪ ﺍﺑﺰﺍﺭﻱ ﺍﺳﺖ ﮐﻪ ﺍﻣﮑﺎﻥ‬
‫ﻭﻳﺮﺍﻳﺶ ﻣﺪﻝ ﻭ ﺣﻞ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﺁﻧﺮﺍ ﻓﺮﺍﻫﻢ ﮐﻨﺪ‪.‬‬
‫ﺯﺑﺎﻥ ﻣﺪﻟﺴﺎﺯﻱ ‪ UML‬ﺩﺭ ﺳﺎﻟﻬﺎﻱ ﺍﺧﻴﺮ ﺑﺴﻴﺎﺭ ﻣﻮﺭﺩ ﺗﻮﺟﻪ ﻭﺍﻗﻊ ﺷﺪﻩ ﻭ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ﺧﻮﺑﻲ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺟﻨﺒﻪﻫﺎﻱ ﻣﺨﺘﻠﻒ ﺳﻴﺴﺘﻢﻫﺎ‬
‫ﻓﺮﺍﻫﻢ ﺁﻭﺭﺩﻩ ﺍﺳﺖ‪ .‬ﺍﺯ ﻃﺮﻓﻲ ‪ UML‬ﻓﺎﻗﺪ ﺭﻭﺵ ﺻﻮﺭﻱ‪ ٤‬ﻣﻨﺎﺳﺐ ﺑﺮﺍﻱ ﻣﻘﺎﺻﺪ ﺗﺤﻠﻴﻞ ﺳﻴﺴﺘﻢﻫﺎ ﺍﺳﺖ‪ .‬ﻣﺎ ﺑﻪ ﻣﻨﻈﻮﺭ ﻧﺰﺩﻳﮏ ﮐﺮﺩﻥ‬
‫‪ UML‬ﻭ ‪ OSAN‬ﺑﻪ ﮔﻮﻧﻪﺍﻱ ﮐﻪ ﻫﺮ ﻳﮏ ﻧﻘﺎﻁ ﺿﻌﻒ ﺩﻳﮕﺮﻱ ﺭﺍ ﭘﻮﺷﺶ ﺩﻫﻨﺪ‪ ،‬ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺭﺍ ﺑﻪ ﺻﻮﺭﺕ ﻳﮏ ‪ Plug-In‬ﺭﻭﻱ‬
‫‪[۲] Together‬ﺳﺎﺧﺘﻪﺍﻳﻢ‪ .‬ﺑﻪ ﺍﻳﻦ ﺗﺮﺗﻴﺐ ﻣﺪﻟﺴﺎﺯ ﻣﻲﺗﻮﺍﻧﺪ ﻣﺪﻟﺴﺎﺯﻱ ﺟﻨﺒﻪﻫﺎﻳﻲ ﺍﺯ ﺳﻴﺴﺘﻢ ﻣﺎﻧﻨﺪ ﻓﺮﺁﻳﻨﺪﻫﺎﻱ ﺳﺎﺯﻣﺎﻧﻲ ﺭﺍ ﺑﻪ ﮐﻤﮏ‬
‫ﻧﻤﻮﺩﺍﺭﻫﺎﻱ ‪ UML‬ﺍﻧﺠﺎﻡ ﺩﻫﺪ ﻭ ﺟﻨﺒﻪﻫﺎﻳﻲ ﺍﺯ ﺳﻴﺴﺘﻢ ﺭﺍ ﮐﻪ ﺑﻪ ﺍﺭﺯﻳﺎﺑﻲ‪ ٥‬ﻭ ﻳﺎ ﺩﺭﺳﺘﻲﻳﺎﺑﻲ‪ ٦‬ﺳﻴﺴﺘﻢ ﻣﺮﺑﻮﻁ ﻣﻲﺷﻮﺩ‪ ،‬ﺑﺎ ‪ OSAN‬ﻣﺪﻝ‬
‫ﻧﻤﺎﻳﺪ ﻭ ﺑﻪ ﺍﻳﻦ ﺗﺮﺗﻴﺐ ﺍﺯ ﻣﺰﺍﻳﺎﻱ ﻫﺮ ﺩﻭ ﺑﻪ ﺻﻮﺭﺕ ﻣﮑﻤﻞ ﺍﺳﺘﻔﺎﺩﻩ ﻧﻤﺎﻳﺪ‪.‬‬
‫ﺳﺎﺧﺘﺎﺭ ﻣﻘﺎﻟﻪ ﺑﻪ ﺍﻳﻦ ﺻﻮﺭﺕ ﺍﺳﺖ ﮐﻪ ﺩﺭ ﺑﺨﺶ )‪ (۲‬ﺑﻪ ﻣﻌﺮﻓﻲ ﻣﺨﺘﺼﺮ ﻣﺪﻝ ‪ OSAN‬ﻣﻲﭘﺮﺩﺍﺯﻳﻢ‪ .‬ﺩﺭ ﺑﺨﺶ )‪ (۳‬ﻣﺮﻭﺭﻱ ﺑﻪ ﮐﺎﺭﻫﺎﻱ‬
‫ﻣﺸﺎﺑﻪ ﺍﻧﺠﺎﻡ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺩﺭ ﺑﺨﺶ )‪ (۴‬ﺳﺎﺧﺘﺎﺭ ﺍﺑﺰﺍﺭ ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﻣﺆﻟﻔﻪﻫﺎﻱ ﺁﻥ ﺍﺭﺍﺋﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺑﺨﺶ )‪ (۵‬ﺑﻪ ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭ ﻣﻲﭘﺮﺩﺍﺯﺩ‪ .‬ﺩﺭ‬
‫ﺑﺨﺶ )‪ (۶‬ﮐﺎﺭﻫﺎﻱ ﺑﻌﺪﻱ ﮐﻪ ﺑﺮﺍﻱ ﺗﮑﻤﻴﻞ ﺍﻳﻦ ﮐﺎﺭ ﺑﺎﻳﺪ ﺍﻧﺠﺎﻡ ﺷﻮﺩ‪ ،‬ﻣﻄﺮﺡ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫‪ .٢‬ﻣﻌﺮﻓﻲ ﻣﺪﻝ ‪OSAN‬‬
‫ﻣﺪﻝ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ )‪ [۳] (PNs‬ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺳﻴﺴﺘﻤﻬﺎﻱ ﻫﻤﺮﻭﻧﺪ ﻭ ﺗﻮﺯﻳﻊ ﺷﺪﻩ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺍﻳﻨﮑﻪ ﺍﻳﻦ ﻣﺪﻝ ﺗﻨﻬﺎ‬
‫ﺍﺯ ﺩﻭ ﺟﺰﺀ ﻣﮑﺎﻥ ﻭ ﺍﻧﺘﻘﺎﻝ ﺗﺸﮑﻴﻞ ﺷﺪﻩ ﺍﺳﺖ‪ ،‬ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺗﺤﻠﻴﻞ ﺑﺎ ﺁﻥ ﺁﺳﺎﻥ ﺍﺳﺖ‪ .‬ﺍﻣﺎ ﺍﻳﻦ ﺳﺎﺩﮔﻲ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺳﻴﺴﺘﻢﻫﺎﻱ ﭘﻴﭽﻴﺪﻩ‪،‬‬
‫ﺩﺭﺩﺳﺮ ﺁﻓﺮﻳﻦ ﺍﺳﺖ‪.‬‬
‫ﻣﺪﻝ ‪ PN‬ﺑﻪ ﺻﻮﺭﺗﻬﺎﻱ ﻣﺨﺘﻠﻒ ﺗﻌﻤﻴﻢ ﭘﻴﺪﺍ ﮐﺮﺩﻩ ﺍﺳﺖ ﻭ ﻣﺪﻟﻬﺎﻱ ﻣﺨﺘﻠﻔﻲ ﺑﺮ ﻣﺒﻨﺎﻱ ﺁﻥ ﺑﻪ ﻭﺟﻮﺩ ﺁﻣﺪﻩﺍﻧﺪ‪ .‬ﻣﺪﻝ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ ﺷﻴﺌﻲ‬
‫)‪ [۴] (OPN‬ﻳﮑﻲ ﺍﺯ ﺗﻌﻤﻴﻢﻫﺎﻱ ‪ PN‬ﺑﻪ ﺷﻤﺎﺭ ﻣﻲﺁﻳﺪ ﮐﻪ ﺍﺯ ﺗﻠﻔﻴﻖ ﻣﻔﺎﻫﻴﻢ ﺷﻲﺀ ﮔﺮﺍﻳﻲ ﺑﺎ ﺁﻥ ﺑﻮﺟﻮﺩ ﺁﻣﺪﻩ ﺍﺳﺖ‪ .‬ﺣﺴﻦ ﻋﻤﺪﻩ ﺍﻳﻦ ﻣﺪﻝ‬
‫ﺍﻳﻦ ﺍﺳﺖ ﮐﻪ ﻣﺪﻟﺴﺎﺯﻱ ﺳﻴﺴﺘﻢﻫﺎﻱ ﭘﻴﭽﻴﺪﻩﺗﺮ ﺭﺍ ﺗﺴﻬﻴﻞ ﻣﻲﮐﻨﺪ ﻭ ﻣﺰﺍﻳﺎﻱ ﻗﺎﺑﻞ ﺗﻮﺟﻬﻲ ﺭﺍ ﺑﻪ ‪ PN‬ﺍﻓﺰﻭﺩﻩ ﺍﺳﺖ‪.‬‬
‫ﺍﺯ ﻃﺮﻑ ﺩﻳﮕﺮ ﺑﺎ ﺗﻠﻔﻴﻖ ﺑﺮﺧﻲ ﺍﺯ ﻣﻔﺎﻫﻴﻢ ﻣﺪﻝ ﺻﻒ ﺑﺎ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ‪ ،‬ﻣﺪﻝ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ )‪ (SAN‬ﺑﻪ ﻭﺟﻮﺩ ﺁﻣﺪﻩ ﺍﺳﺖ‪.‬‬
‫]‪ [۶،۵‬ﻣﺪﻝ ‪ SAN‬ﺍﺯ ﺑﺴﻂﻫﺎﻱ ﻗﻮﻱ ﻭ ﻗﺎﺑﻞ ﺍﻧﻌﻄﺎﻑ ‪ PN‬ﺍﺳﺖ ﻭ ﺑﻪ ﮐﻤﮏ ﺁﻥ ﻣﻲﺗﻮﺍﻥ ﺟﻨﺒﻪﻫﺎﻳﻲ ﻧﻈﻴﺮ ﻋﺪﻡ ﻗﻄﻌﻴﺖ ﺭﺍ ﻧﻴﺰ ﻣﺪﻝ ﻧﻤﻮﺩ‪.‬‬
‫ﻋﻼﻭﻩ ﺑﺮ ﺍﻳﻦ‪ ،‬ﺑﻪ ﮐﻤﮏ ﻣﺪﻝ ‪ SAN‬ﻣﻲﺗﻮﺍﻥ ﺟﻨﺒﻪﻫﺎﻱ ﻋﻤﻠﻴﺎﺗﻲ ﺳﻴﺴﺘﻢ ﻧﻈﻴﺮ ﮐﺎﺭﺁﻳﻲ ﻭ ﺍﺗﮑﺎﺀﭙﺬﻳﺮﻱ ﺭﺍ ﺍﺭﺯﻳﺎﺑﻲ ﻧﻤﻮﺩ‪.‬‬
‫ﻣﺪﻝ ‪ [۱] OSAN‬ﺍﺯ ﺗﻠﻔﻴﻖ ﻣﻔﺎﻫﻴﻢ ﺷﺊﮔﺮﺍﻳﻲ ﺑﺎ ﻣﺪﻝ ‪ SAN‬ﺑﻮﺟﻮﺩ ﺁﻣﺪﻩ ﺍﺳﺖ ﻭ ﺗﻌﻤﻴﻢ ﺷﺊﮔﺮﺍﻱ ﺁﻥ ﺑﻪ ﺷﻤﺎﺭ ﻣﻲﺁﻳﺪ‪ OSAN .‬ﺍﺯ‬
‫ﺑﺮﺧﻲ ﺟﻬﺎﺕ ﺷﺒﻴﻪ ‪ SAN‬ﻭ ﺍﺯ ﺟﻬﺎﺗﻲ ﺩﻳﮕﺮ ﻣﺸﺎﺑﻪ ‪ OPN‬ﺍﺳﺖ‪ .‬ﻣﺆﻟﻔﻪﻫﺎﻱ ﺍﺳﺎﺳﻲ ﺍﻳﻦ ﻣﺪﻝ ﻋﺒﺎﺭﺗﻨﺪ ﺍﺯ‪:‬‬
‫‪Dependability 1‬‬
‫‪Macro Activities 2‬‬
‫‪Colored Places3‬‬
‫‪Formal 4‬‬
‫‪Evaluation 5‬‬
‫‪Verification 6‬‬
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‫ﻣﮑﺎﻥ‪ :١‬ﻣﺸﺎﺑﻪ ﻣﻔﻬﻮﻡ ﻣﮑﺎﻥ ﺩﺭ ‪ SAN‬ﻭ ‪ PN‬ﺍﺳﺖ ﻭ ﺑﻪ ﺷﮑﻞ ﺩﺍﻳﺮﻩ ﻧﻤﺎﻳﺶ ﺩﺍﺩﻩ ﻣﻲﺷﻮﺩ‪.‬‬
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‫ﻣﮑﺎﻥ ﺭﻧﮕﻲ‪ :٢‬ﺍﻳﻦ ﻣﻔﻬﻮﻡ ﺩﺭ ‪ SAN‬ﻭﺟﻮﺩ ﻧﺪﺍﺭﺩ ﻭﻟﻲ ﻣﺸﺎﺑﻪ ﺁﻥ ﺩﺭ ‪ OPN‬ﻭﺟﻮﺩ ﺩﺍﺭﺩ‪ .‬ﻣﮑﺎﻧﻬﺎﻱ ﺭﻧﮕﻲ ﻣﮑﺎﻧﻬﺎﻳﻲ ﻫﺴﺘﻨﺪ ﮐﻪ‬
‫ﻣﻲﺗﻮﺍﻧﻨﺪ ﻧﺸﺎﻧﻪ‪٣‬ﻫﺎﻳﻲ ﺑﺎ ﺗﺎﻳﭗﻫﺎﻱ ﻣﺨﺘﻠﻒ ﺭﺍ ﺩﺭ ﺑﺮ ﺑﮕﻴﺮﻧﺪ‪ .‬ﻣﮑﺎﻥ ﺭﻧﮕﻲ ﺩﺭ ﻧﻤﺎﻳﺶ ﮔﺮﺍﻓﻴﮑﻲ ﺑﻪ ﺻﻮﺭﺕ ﺑﻴﻀﻲ ﻧﺸﺎﻥ ﺩﺍﺩﻩ ﻣﻲﺷﻮﺩ‪.‬‬
‫ﻧﺸﺎﻧﻪﻫﺎ ﺩﺭ ﻣﮑﺎﻧﻬﺎﻱ ﺭﻧﮕﻲ ﺍﺷﻴﺎﺀ ﻓﻌﺎﻝ ﻫﺴﺘﻨﺪ ﮐﻪ ﺷﺎﻣﻞ ﺗﻌﺪﺍﺩﻱ ﻣﺸﺨﺼﻪ ﻭ ﻣﺘﺪ ﻣﻲﺑﺎﺷﻨﺪ‪ .‬ﺍﻳﻦ ﻧﺸﺎﻧﻪﻫﺎ ﺗﻮﺳﻂ ﻓﻌﺎﻟﻴﺘﻬﺎ ﺑﺮ ﺍﺳﺎﺱ‬
‫ﺍﺳﺘﺮﺍﺗﮋﻱ ﺯﻣﺎﻧﺒﻨﺪﻱ ﻣﮑﺎﻥ ﺭﻧﮕﻲ ﺑﺮﺩﺍﺷﺘﻪ ﻣﻲﺷﻮﻧﺪ‪ .‬ﺍﺳﺘﺮﺍﺗﮋﻱ ﺯﻣﺎﻧﺒﻨﺪﻱ ﺍﺯ ﻣﺠﻤﻮﻋﻪ‬
‫‪{NONE, FCFS, LCFS,‬‬
‫}‪ PRIORITY, USER_DEF‬ﺍﻧﺘﺨﺎﺏ ﻣﻲﺷﻮﺩ‪.‬‬
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‫ﻓﻌﺎﻟﻴﺖ ﺯﻣﺎﻧﻲ‪ :٤‬ﻓﻌﺎﻟﻴﺘﻬﺎﻳﻲ ﻫﺴﺘﻨﺪ ﮐﻪ ﺍﺟﺮﺍﻱ ﺁﻧﻬﺎ ﻣﺴﺘﻠﺰﻡ ﺻﺮﻑ ﺯﻣﺎﻥ ﻣﺸﺨﺼﻲ ﺍﺳﺖ‪ .‬ﻫﺪﻑ ﺍﺻﻠﻲ ﺍﺯ ﺍﻳﻦ ﻧﻮﻉ ﻓﻌﺎﻟﻴﺘﻬﺎ ﻣﺪﻝ ﮐﺮﺩﻥ‬
‫ﻫﻤﺮﻭﻧﺪﻱ ﺩﺭ ﺳﻴﺴﺘﻢ ﻣﻲﺑﺎﺷﺪ‪ ،‬ﺑﻪ ﺍﻳﻦ ﺗﺮﺗﻴﺐ ﮐﻪ ﭼﻨﺪ ﻓﻌﺎﻟﻴﺖ ﺍﺯ ﺍﻳﻦ ﻧﻮﻉ ﻣﻲﺗﻮﺍﻧﻨﺪ ﺑﻪ ﻃﻮﺭ ﻫﻤﺰﻣﺎﻥ ﺩﺭ ﺳﻴﺴﺘﻢ ﺩﺭ ﺣﺎﻝ ﺍﺟﺮﺍ ﺑﺎﺷﻨﺪ‪.‬‬
‫ﺍﺟﺮﺍﻱ ﺍﻳﻦ ﻓﻌﺎﻟﻴﺘﻬﺎ ﺗﻨﻬﺎ ﺩﺭ ﺻﻮﺭﺗﻲ ﻣﻲﺗﻮﺍﻧﺪ ﺁﻏﺎﺯ ﺷﻮﺩ ﮐﻪ ﮔﺰﺍﺭﻩ ﻣﻨﻄﻘﻲ ﻓﻌﺎﻟﺴﺎﺯﻱ‪ ٥‬ﺁﻥ ﺑﺮﻗﺮﺍﺭ ﺑﺎﺷﺪ‪ .‬ﭼﻨﺎﻧﭽﻪ ﻗﺒﻞ ﺍﺯ ﺍﺗﻤﺎﻡ ﻓﻌﺎﻟﻴﺘﻲ‬
‫ﺍﺯ ﺍﻳﻦ ﻧﻮﻉ‪ ،‬ﮔﺰﺍﺭﻩ ﻣﻨﻄﻘﻲ ﻓﻌﺎﻟﺴﺎﺯﻱ ﻧﺎﺩﺭﺳﺖ ﺷﻮﺩ‪ ،‬ﺍﺟﺮﺍﻱ ﺁﻥ ﻣﺘﻮﻗﻒ ﻣﻲﺷﻮﺩ‪.‬‬
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‫ﻓﻌﺎﻟﻴﺖ ﺁﻧﻲ‪ :٦‬ﺍﻳﻦ ﻧﻮﻉ ﻓﻌﺎﻟﻴﺘﻬﺎ ﺑﻪ ﺻﻮﺭﺕ ﺁﻧﻲ ﺍﺟﺮﺍ ﻣﻲﺷﻮﻧﺪ‪ ،‬ﺑﻪ ﻋﺒﺎﺭﺗﻲ ﺍﺟﺮﺍﻱ ﺁﻧﻬﺎ ﺯﻣﺎﻧﻲ ﻧﻤﻲﮔﻴﺮﺩ‪ .‬ﺑﺮﺧﻲ ﺍﺯ ﺟﻨﺒﻪﻫﺎﻱ ﻏﻴﺮ‬
‫ﻗﻄﻌﻲ ﺳﻴﺴﺘﻢ ﺭﺍ ﻣﻲﺗﻮﺍﻥ ﺑﻪ ﮐﻤﮏ ﺍﻳﻦ ﻓﻌﺎﻟﻴﺘﻬﺎ ﻣﺪﻝ ﮐﺮﺩ‪.‬‬
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‫ﻓﻌﺎﻟﻴﺘﻬﺎﻱ ﻣﺎﮐﺮﻭ‪ :٧‬ﺍﻳﻦ ﻣﻔﻬﻮﻡ ﻳﮑﻲ ﺍﺯ ﻧﻘﺎﻁ ﻗﻮﺕ ﺍﺳﺎﺳﻲ ﺩﺭ ﻣﺪﻝ ‪ OSAN‬ﺑﻪ ﺷﻤﺎﺭ ﻣﻲﺁﻳﺪ‪ .‬ﻫﺮ ﻓﻌﺎﻟﻴﺖ ﻣﺎﮐﺮﻭ ﻫﻤﺎﻧﻨﺪ ﻳﮏ ﺯﻳﺮ‬
‫ﺷﺒﮑﻪ ﺑﺮﺍﻱ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ ﺍﺳﺖ‪ .‬ﻫﻤﭽﻨﻴﻦ ﻣﻲﺗﻮﺍﻥ ﻣﺎﮐﺮﻭﻫﺎ ﺭﺍ ﺟﺪﺍﮔﺎﻧﻪ ﺫﺧﻴﺮﻩ ﮐﺮﺩ ﻭ ﺑﻪ ﺻﻮﺭﺕ ﮐﺘﺎﺑﺨﺎﻧﻪﻫﺎﻱ‬
‫ﮐﻤﮑﻲ ﺍﺯ ﺁﻧﻬﺎ ﺍﺳﺘﻔﺎﺩﻩ ﮐﺮﺩ‪.‬‬
‫ﻣﺜﺎﻝ‪ :‬ﺷﮑﻞ ‪ ۱‬ﻧﻤﻮﻧﻪ ﺍﻱ ﺍﺯ ﻣﺪﻝ ‪ OSAN‬ﺭﺍ ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ‪ .‬ﺍﻳﻦ ﻣﺪﻝ ﺍﺯ ﺩﻭ ﻓﻌﺎﻟﻴﺖ ﺯﻣﺎﻧﻲ ‪ TA1‬ﻭ ‪ ،TA2‬ﻳﮏ ﻓﻌﺎﻟﻴﺖ ﺁﻧﻲ ‪ ،IA1‬ﻳﮏ‬
‫ﻣﮑﺎﻥ ﺭﻧﮕﻲ ‪ cp1‬ﻭ ﺩﻭ ﻣﮑﺎﻥ ‪ p1‬ﻭ ‪ p2‬ﺗﺸﮑﻴﻞ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫ﺷﮑﻞ ‪ :۱‬ﻧﻤﻮﻧﻪ ﺍﻱ ﺍﺯ ﻣﺪﻝ ‪OSAN‬‬
‫‪ .۳‬ﻣﺮﻭﺭﻱ ﺑﺮ ﮐﺎﺭﻫﺎﻱ ﻣﺸﺎﺑﻪ‬
‫‪Place 1‬‬
‫‪Colored Place2‬‬
‫‪Token 3‬‬
‫‪Timed Activities 4‬‬
‫‪Enabling Predicate 5‬‬
‫‪Instantaneous Activities 6‬‬
‫‪Macro Activities 7‬‬
‫ﺍﺑﺰﺍﺭﻫﺎﻱ ﻣﺪﻟﺴﺎﺯﻱ ﻣﺘﻌﺪﺩﻱ ﺑﺮﺍﻱ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ ﻭ ﺑﺴﻂﻫﺎﻱ ﺁﻥ ﻣﻌﺮﻓﻲ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺩﺭ ﺍﻳﻦ ﻣﻴﺎﻥ ﻣﻲﺗﻮﺍﻥ ‪ [۷] DesignCPN‬ﻭ‬
‫‪ [۸] CPNTool‬ﺭﺍ ﻧﺎﻡ ﺑﺮﺩ‪ .‬ﺍﻳﻦ ﺩﻭ ﺍﺑﺰﺍﺭ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺑﺎ ﺷﺒﮑﻪﻫﺎﻱ ﭘﺘﺮﻱ ﺭﻧﮕﻲ ﺑﻮﺟﻮﺩ ﺁﻣﺪﻩ ﺍﻧﺪ‪ [۹] METASAN .‬ﺍﺑﺰﺍﺭﻱ‬
‫ﺍﺳﺖ ﮐﻪ ﺑﺮﺍﻱ ﺍﺭﺯﻳﺎﺑﻲ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺑﻮﺟﻮﺩ ﺁﻣﺪﻩ ﺍﺳﺖ‪ [۱۰] UltraSAN .‬ﻭ ‪ [۱۱] Mobius‬ﺍﺯ ﺍﺑﺰﺍﺭﻫﺎﻱ ﻣﺪﻟﺴﺎﺯﻱ‬
‫ﺑﺎ ‪ SAN‬ﻣﻲ ﺑﺎﺷﻨﺪ‪ .‬ﻫﺮ ﺳﻪ ﺍﺑﺰﺍﺭ ‪ UltraSAN ،METASAN‬ﻭ ‪ Mobius‬ﺑﺮ ﺍﺳﺎﺱ ﺗﻌﺮﻳﻒ ﻗﺪﻳﻤﻲ ‪ SAN‬ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺍﻧﺪ‪.‬‬
‫)ﺗﻌﺮﻳﻒ ﺳﺎﻝ ‪ [۱۲] SharifSAN .(۱۹۸۴‬ﺍﺑﺰﺍﺭ ﺩﻳﮕﺮﻱ ﺍﺳﺖ ﮐﻪ ﺑﺮ ﺍﺳﺎﺱ ﺗﻌﺮﻳﻒ ﺟﺪﻳﺪ ‪ SAN‬ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﺑﺰﺍﺭﻫﺎﻱ ﻓﻮﻕ‬
‫ﻗﺎﺑﻠﻴﺖ ﺳﺎﺧﺘﻦ ﻣﺪﻝ‪ ،‬ﺷﺒﻴﻪﺳﺎﺯﻱ ﻭ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﻣﺪﻝ ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﮐﻨﻨﺪ‪ .‬ﺗﻌﺮﻳﻒ ﻣﺪﻝ ‪ OSAN‬ﺟﺪﻳﺪ ﺑﻮﺩﻩ ﻭ ﺍﺑﺰﺍﺭﻱ ﮐﻪ ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ‬
‫ﻣﻌﺮﻓﻲ ﻣﻲ ﺷﻮﺩ‪ ،‬ﻧﺨﺴﺘﻴﻦ ﺍﺑﺰﺍﺭ ﺑﺮﺍﻱ ﺍﻳﻦ ﻣﺪﻟﻬﺎ ﺍﺳﺖ‪.‬‬
‫‪ .۴‬ﻣﻌﺮﻓﻲ ﺳﺎﺧﺘﺎﺭ ﺍﺑﺰﺍﺭ ﻭ ﻣﺆﻟﻔﻪﻫﺎﻱ ﺁﻥ‬
‫ﺑﺮﺍﻱ ﺍﻳﻨﮑﻪ ﺑﺘﻮﺍﻧﻴﻢ ﺍﺯ ﻣﺪﻟﻬﺎﻱ ‪ OSAN‬ﺩﺭ ﻋﻤﻞ ﺍﺳﺘﻔﺎﺩﻩ ﮐﻨﻴﻢ‪ ،‬ﻧﻴﺎﺯﻣﻨﺪ ﺍﺑﺰﺍﺭﻱ ﻫﺴﺘﻴﻢ ﮐﻪ ﺗﺴﻬﻴﻼﺗﻲ ﺭﺍ ﺟﻬﺖ ﺳﺎﺧﺖ ﻣﺪﻝ‪ ،‬ﺣﻞ ﻭ‬
‫ﺷﺒﻴﻪﺳﺎﺯﻱ ﻣﺪﻝ ﻭ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﺁﻥ ﺩﺭ ﺍﺧﺘﻴﺎﺭ ﻣﺎ ﻗﺮﺍﺭ ﺩﻫﺪ‪ .‬ﺩﺭ ﺍﻳﻦ ﺑﺨﺶ ﻭﻳﮋﮔﻴﻬﺎ ﻭ ﻣﺆﻟﻔﻪﻫﺎﻱ ﺍﺻﻠﻲ ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺭﺍ ﻣﻮﺭﺩ ﺑﺮﺭﺳﻲ ﻗﺮﺍﺭ‬
‫ﻣﻲﺩﻫﻴﻢ‪.‬‬
‫ﺷﮑﻞ ‪ ۲‬ﻣﺆﻟﻔﻪﻫﺎﻱ ﺍﺻﻠﻲ ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺭﺍ ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ‪ .‬ﻃﺒﻴﻌﺘﹰﺎ ﭼﻨﻴﻦ ﺍﺑﺰﺍﺭﻱ ﻧﻴﺎﺯ ﺑﻪ ﻳﮏ ﻭﺍﺳﻂ ﮐﺎﺭﺑﺮ ﮔﺮﺍﻓﻴﮑﻲ ﺩﺍﺭﺩ ﺗﺎ ﮐﺎﺭﺑﺮ ﺍﺯ ﻃﺮﻳﻖ ﺁﻥ‬
‫ﺑﺘﻮﺍﻧﺪ ﻓﻌﺎﻟﻴﺘﻬﺎﻱ ﻣﻮﺭﺩ ﻧﻈﺮ ﺧﻮﺩ ﻣﺎﻧﻨﺪ ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺣﻞ ﻣﺪﻝ ﺭﺍ ﺍﻧﺠﺎﻡ ﺩﻫﺪ‪ .‬ﻣﺆﻟﻔﻪ ﻭﻳﺮﺍﻳﺸﮕﺮ ﮔﺮﺍﻓﻴﮑﻲ ‪ OSAN‬ﺍﻣﮑﺎﻥ ﺗﺮﺳﻴﻢ ﻣﺪﻝ ﻭ‬
‫ﺗﻌﺮﻳﻒ ﮐﺮﺩﻥ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻱ ﻣﺮﺑﻮﻁ ﺑﻪ ﻫﺮ ﻳﮏ ﺍﺯ ﺍﺟﺰﺍﺀ ﻣﺪﻝ ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﺁﻭﺭﺩ‪ .‬ﻣﺆﻟﻔﻪﻫﺎﻱ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﻣﺪﻝ‪ ،‬ﺷﺒﻴﻪﺳﺎﺯ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ‪،‬‬
‫ﺗﻮﻟﻴﺪ ﮐﻨﻨﺪﻩ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﻭ ﻣﺪﻳﺮ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺩﺭ ﺣﻞ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﻣﺪﻝ ﻧﻘﺶ ﺩﺍﺭﻧﺪ‪ .‬ﻣﺆﻟﻔﻪ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ ﻭﻇﻴﻔﻪ‬
‫ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﺩﺭ ﺟﻬﺖ ﺁﻣﻮﺯﺵ ﻣﺪﻝ ‪ OSAN‬ﻭ ﻳﺎ ﺑﺮ ﻃﺮﻑ ﮐﺮﺩﻥ ﺍﻳﺮﺍﺩﻫﺎﻱ ﻣﺪﻝ ﺭﺍ ﺑﺮ ﻋﻬﺪﻩ ﺩﺍﺭﺩ‪ .‬ﺩﺭ ﺍﻳﻨﺠﺎ ﺑﻪ ﺷﺮﺡ ﻣﻔﺼﻞ ﺗﺮ ﻫﺮ‬
‫ﻳﮏ ﺍﺯ ﺍﻳﻦ ﻣﺆﻟﻔﻪﻫﺎ ﺧﻮﺍﻫﻴﻢ ﭘﺮﺩﺍﺧﺖ‪.‬‬
‫ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺑﻪ ﺻﻮﺭﺕ ﻳﮏ ﭘﻴﻤﺎﻧﻪ‪ ١‬ﺑﻪ ﻧﺮﻡﺍﻓﺰﺍﺭ ﻣﻌﺮﻭﻑ ‪ [۲] Together‬ﮐﻪ ﻳﮑﻲ ﺍﺯ ﻣﻌﺮﻭﻑﺗﺮﻳﻦ ‪ case tool‬ﻫﺎﻱ ﺷﺊﮔﺮﺍﻱ ﻣﻮﺟﻮﺩ‬
‫ﻻ ‪ Together‬ﺍﻣﮑﺎﻧﺎﺕ ﺯﻳﺎﺩﻱ ﺭﺍ‬
‫ﺍﺳﺖ‪ ،‬ﺍﺿﺎﻓﻪ ﻣﻲﺷﻮﺩ ﻭ ﺍﺯ ﺑﺮﺧﻲ ﺍﺯ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ﺁﻥ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﮐﻨﺪ‪ .‬ﺩﻻﻳﻞ ﺍﻳﻦ ﮐﺎﺭ ﻋﺒﺎﺭﺗﻨﺪ ﺍﺯ ﺍﻳﻨﮑﻪ ﺍﻭ ﹰ‬
‫ﺩﺭ ﺭﺍﺑﻄﻪ ﺑﺎ ﻣﺪﻟﺴﺎﺯﻱ ﺩﺭ ﺍﺧﺘﻴﺎﺭ ﮐﺎﺭﺑﺮ ﻗﺮﺍﺭ ﻣﻲﺩﻫﺪ ﻭ ﺩﺭ ﻭﺍﻗﻊ ﺷﺎﻣﻞ ﻣﺠﻤﻮﻋﻪ ﮐﺎﻣﻠﻲ ﺍﺯ ﺍﻣﮑﺎﻧﺎﺗﻲ ﺍﺳﺖ ﮐﻪ ﻳﮏ ﻣﺪﻟﺴﺎﺯ ﻣﻤﮑﻦ ﺍﺳﺖ‬
‫ﻧﻴﺎﺯ ﺩﺍﺷﺘﻪ ﺑﺎﺷﺪ‪ ،‬ﻭ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ﺁﻥ ﺍﺯ ﺩﻭﺑﺎﺭﻩﻧﻮﻳﺴﻲ ﺍﻳﻦ ﻗﺎﺑﻠﻴـﺘﻬﺎ ﺧﻮﺩﺩﺍﺭﻱ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﻋﻼﻭﻩ ﺑﺮ ﺍﻳﻦ‪ ،‬ﮐﺎﺭﺑﺮ ﻧﺮﻡﺍﻓﺰﺍﺭ ﺍﻣﮑﺎﻧﺎﺕ‬
‫ﻳﮏ ‪ case tool‬ﺑﺰﺭﮒ ﺭﺍ ﺩﺭ ﻫﻨﮕﺎﻡ ﻣﺪﻟﺴﺎﺯﻱ ﺑﺎ ﺷﺒﮑﻪﻫﺎﻱ ‪ OSAN‬ﺩﺭ ﺍﺧﺘﻴﺎﺭ ﺧﻮﺍﻫﺪ ﺩﺍﺷﺖ‪ .‬ﺍﺯ ﻃﺮﻓﻲ ﺑﻪ ﺩﻟﻴﻞ ﻓﺮﺍﮔﻴﺮ ﺑﻮﺩﻥ ﻧﺮﻡ‬
‫ﺍﻓﺰﺍﺭ ‪ Together‬ﺑﺴﻴﺎﺭﻱ ﺍﺯ ﺍﻓﺮﺍﺩ ﺑﺎ ﺁﻥ ﺁﺷﻨﺎ ﻫﺴﺘﻨﺪ ﻭ ﺑﻨﺎﺑﺮﺍﻳﻦ ﻭﻗﺖ ﺯﻳﺎﺩﻱ ﺑﺮﺍﻱ ﻓﺮﺍﮔﺮﻓﺘﻦ ﺭﻭﺵ ﮐﺎﺭ ﺑﺎ ﺍﺑﺰﺍﺭ ﻣﺪﻟﺴﺎﺯﻱ ‪ OSAN‬ﻧﻴﺎﺯ‬
‫ﻧﺨﻮﺍﻫﻨﺪ ﺩﺍﺷﺖ‪ .‬ﻳﮑﻲ ﺍﺯ ﺍﻫﺪﺍﻓﻲ ﮐﻪ ﺩﺭ ﻃﺮﺍﺣﻲ ﻭ ﺳﺎﺧﺖ ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﻫﻤﻮﺍﺭﻩ ﻣﺪ ﻧﻈﺮ ﺑﻮﺩﻩ ﺍﺳﺖ‪ ،‬ﺍﻳﻦ ﺍﺳﺖ ﮐﻪ ﺗﺎ ﺣﺪ ﻣﻤﮑﻦ ﻣﺒﺎﺣﺚ‬
‫ﻣﺪﻟﺴﺎﺯﻱ ﺭﺍ ﮐﻪ ﺗﺎ ﮐﻨﻮﻥ ﺍﻏﻠﺐ ﺍﺯ ﺑﻌﺪ ﺗﺤﻘﻴﻘﺎﺗﻲ ﻭ ﺗﺌﻮﺭﻱ ﻣﻮﺭﺩ ﺗﻮﺟﻪ ﺑﻮﺩﻩ ﺑﺎ ﻣﻔﺎﻫﻴﻢ ﻭ ﺍﺑﺰﺍﺭﻱ ﮐﻪ ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ ﺻﻨﻌﺘﻲ ﻭ ﮐﺎﺭﺑﺮﺩﻱ‬
‫ﺩﺍﺷﺘﻪ ﺍﺳﺖ‪ ،‬ﺑﻪ ﻫﻢ ﻧﺰﺩﻳﮏ ﮐﻨﻴﻢ‪ .‬ﺑﻪ ﻋﺒﺎﺭﺗﻲ ﻣﻲﺧﻮﺍﻫﻴﻢ ﻣﺪﻟﻬﺎﻱ ﺭﻳﺎﺿﻲ ﻭ ﺗﺌﻮﺭﻱ ﻣﺎﻧﻨﺪ ﻣﺪﻝ ‪ OSAN‬ﺭﺍ ﺑﻪ ﺻﻮﺭﺕ ﮐﺎﺭﺑﺮﺩﻱﺗﺮ ﻭ ﺩﺭ‬
‫ﻳﮏ ﻧﺮﻡﺍﻓﺰﺍﺭ ﻭ ﺑﺴﺘﻪ ﮐﺎﺭﺑﺮﺩﻱ ﻋﺮﺿﻪ ﮐﻨﻴﻢ ﺗﺎ ﺑﺪﻳﻦ ﻧﺤﻮ ﺭﺍﻩ ﻳﺎﻓﺘﻦ ﺍﻳﻦ ﻣﺒﺎﺣﺚ ﺑﻪ ﺣﻮﺯﻩ ﮐﺎﺭﺑﺮﺩ ﺗﺴﻬﻴﻞ ﺷﻮﺩ ﻭ ﻫﻤﻴﻦ ﺍﻣﺮ ﻳﮑﻲ ﺩﻳﮕﺮ ﺍﺯ‬
‫ﺩﻻﺋﻠﻲ ﺑﻮﺩﻩ ﺍﺳﺖ ﮐﻪ ‪ Together‬ﺭﺍ ﺑﺮﺍﻱ ﺍﻳﻦ ﻣﻨﻈﻮﺭ ﺍﻧﺘﺨﺎﺏ ﮐﺮﺩﻩﺍﻳﻢ‪.‬‬
‫‪Plug-In 1‬‬
‫ﺷﮑﻞ‪ :۲‬ﺳﺎﺧﺘﺎﺭ ﺍﺑﺰﺍﺭ ﻣﺪﻟﺴﺎﺯﻱ‬
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‫ﻭﺍﺳﻂ ﮔﺮﺍﻓﻴﮑﻲ ﮐﺎﺭﺑﺮ‪ :‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺷﺎﻣﻞ ﻗﺴﻤﺘﻬﺎﻳﻲ ﺍﺯ ﻭﺍﺳﻂ ﮐﺎﺭﺑﺮ ﺍﺳﺖ ﮐﻪ ﻣﺪﻟﺴﺎﺯ ﺑﺮﺍﻱ ﮐﺎﺭﮐﺮﺩﻥ ﺑﺎ ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﻧﻴﺎﺯ ﺩﺍﺭﺩ‪ .‬ﺑﻪ‬
‫ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﻣﻨﻮﻫﺎﻱ ﻣﺨﺘﻠﻔِﻲ ﺑﺮﺍﻱ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ‪ ،‬ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺣﻞ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﺗﺤﻠﻴﻠﻲ ﻭ ﺷﺒﻴﻪﺳﺎﺯﻱ ﺗﻬﻴﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﻫﻤﭽﻨﻴﻦ‬
‫ﺍﻣﮑﺎﻧﺎﺕ ﺍﺳﺎﺳﻲ ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺍﺯ ﻗﺒﻴﻞ ﺍﻣﮑﺎﻥ ﭼﺎﭖ ﻧﻤﻮﺩﺍﺭﻫﺎ‪ ،‬ﺍﻣﮑﺎﻧﺎﺕ ﺫﺧﻴﺮﻩ ﮐﺮﺩﻥ ﻭ ﺧﻮﺍﻧﺪﻥ ﻣﺪﻝ ﻭ ﻧﻈﺎﻳﺮ ﺁﻥ ﺍﺯ ﻃﺮﻳﻖ ﻫﻤﻴﻦ ﻣﺆﻟﻔﻪ‬
‫ﺩﺭ ﺍﺧﺘﻴﺎﺭ ﮐﺎﺭﺑﺮ ﻗﺮﺍﺭ ﻣﻲﮔﻴﺮﺩ‪ .‬ﻃﺮﺍﺣﻲ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﻋﻤﺪﺗﹰﺎ ﺑﺮ ﺍﺳﺎﺱ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ‪ Together‬ﺍﻧﺠﺎﻡ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
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‫ﻭﻳﺮﺍﻳﺸﮕﺮ ﮔﺮﺍﻓﻴﮑﻲ ‪ :OSAN‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ﻣﺪﻟﺴﺎﺯﻱ ﻭ ﺗﺮﺳﻴﻢ ﺷﺒﮑﻪﻫﺎﻱ ‪ OSAN‬ﻭ ﻫﻤﭽﻨﻴﻦ ﺗﻌﻴﻴﻦ ﭘﺎﺭﺍﻣﺘﺮﻫﺎ ﻭ‬
‫ﻣﺸﺨﺼﺎﺕ ﻫﺮ ﻳﮏ ﺍﺯ ﺍﺟﺰﺍﺀ ﺷﺒﮑﻪ ‪ OSAN‬ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﺁﻭﺭﺩ‪ .‬ﺍﺯ ﻃﺮﻳﻖ ﺍﻳﻦ ﻭﻳﺮﺍﻳﺸﮕﺮ ﮐﺎﺭﺑﺮ ﻣﻲﺗﻮﺍﻧﺪ ﺑﻪ ﺭﺍﺣﺘﻲ ﻫﺮ ﻳﮏ ﺍﺯ‬
‫ﺍﺟﺰﺍﺀ ﻣﺪﻝ ‪ OSAN‬ﻣﺎﻧﻨﺪ ﻣﮑﺎﻥ‪ ،‬ﻣﮑﺎﻥ ﺭﻧﮕﻲ‪ ،‬ﻓﻌﺎﻟﻴﺖ ﺁﻧﻲ‪ ،‬ﻓﻌﺎﻟﻴﺖ ﺯﻣﺎﻧﻲ‪ ،‬ﻓﻌﺎﻟﻴﺖ ﻣﺎﮐﺮﻭ ﺭﺍ ﺍﻧﺘﺨﺎﺏ ﮐﺮﺩﻩ ﻭ ﺭﻭﻱ ﺻﻔﺤﻪ ﻧﻤﻮﺩﺍﺭ‬
‫ﻗﺮﺍﺭ ﺩﻫﺪ‪ .‬ﻋﻼﻭﻩ ﺑﺮ ﺍﻳﻦ ﻣﻲﺗﻮﺍﻧﺪ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻱ ﻣﺮﺑﻮﻁ ﺑﻪ ﻫﺮ ﻳﮏ ﺍﺯ ﺍﻳﻦ ﺍﺟﺰﺍﺀ‪ ،‬ﻫﻤﺎﻧﻨﺪ ﮔﺰﺍﺭﻩﻫﺎﻱ ﻣﻨﻄﻘﻲ‪ ،‬ﺗﻮﺍﺑﻊ‪ ،‬ﺗﻮﺯﻳﻊ ﺍﺣﺘﻤﺎﻟﻲ ﻭ‬
‫ﺳﺎﻳﺮ ﻣﻮﺍﺭﺩ ﻣﻮﺭﺩ ﻧﻴﺎﺯ ﺭﺍ ﺍﺯ ﻃﺮﻳﻖ ﻳﮏ ﻭﺍﺳﻂ ﻫﻤﺎﻫﻨﮓ ﻭ ﻳﮑﭙﺎﺭﭼﻪ ﻭﺍﺭﺩ ﻧﻤﺎﻳﺪ‪ .‬ﻫﻤﭽﻨﻴﻦ ﻣﻲﺗﻮﺍﻧﺪ ﺍﺟﺰﺍﺀ ﻣﺨﺘﻠﻒ ﺭﺍ ﺍﺯ ﻃﺮﻳﻖ‬
‫ﺧﻄﻮﻁ ﺍﺭﺗﺒﺎﻁ ﺩﻫﻨﺪﻩ ﺑﻪ ﻫﻢ ﻣﺘﺼﻞ ﮐﻨﺪ‪ .‬ﻣﺆﻟﻔﻪﻫﺎﻳﻲ ﺑﺮﺍﻱ ﻭﺍﺭﺩ ﮐﺮﺩﻥ ﮔﺰﺍﺭﻩﻫﺎ ﻭ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻳﻲ ﮐﻪ ﺑﻪ ﻋﻨﻮﺍﻥ ﻧﺘﻴﺠﻪ ﺷﺒﻴﻪﺳﺎﺯﻱ ﻳﺎ ﺣﻞ‬
‫ﺗﺤﻠﻴﻠﻲ ﺑﺎﻳﺪ ﻣﺤﺎﺳﺒﻪ ﺷﻮﻧﺪ‪ ،‬ﺩﺭ ﻧﻈﺮ ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﻣﮑﺎﻧﺎﺕ ﮐﻤﮑﻲ ﺍﺯ ﻗﺒﻴﻞ ﻗﺎﺑﻠﻴﺖ ﺑﺰﺭﮒ ﻭ ﮐﻮﭼﮏ ﮐﺮﺩﻥ ﻧﻤﻮﺩﺍﺭ‪ ،‬ﻳﺎﺩﺩﺍﺷﺖ‬
‫ﮔﺬﺍﺷﺘﻦ ﺑﺮﺍﻱ ﻫﺮ ﻳﮏ ﺍﺯ ﺍﺟﺰﺍﺀ ﻳﺎ ﮐﻞ ﻧﻤﻮﺩﺍﺭ ﻭ ﮐﭙﻲ ﻭ ﺑﺮﻳﺪﻥ ﻭ ﭼﺴﺒﺎﻧﺪﻥ‪ ١‬ﻧﻴﺰ ﺩﺭ ﺍﻳﻦ ﻭﻳﺮﺍﻳﺸﮕﺮ ﺩﺭ ﺍﺧﺘﻴﺎﺭ ﻣﺪﻟﺴﺎﺯ ﺍﺳﺖ ﮐﻪ‬
‫ﺑﺮﺧﻲ ﺍﺯ ﺍﻳﻦ ﻗﺎﺑﻠﻴﺘﻬﺎ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ ‪ Together‬ﻓﺮﺍﻫﻢ ﺷﺪﻩﺍﻧﺪ‪ .‬ﺷﮑﻞ ‪ ۳‬ﻧﻤﻮﻧﻪﺍﻱ ﺍﺯ ﺻﻔﺤﻪ ﻭﻳﺮﺍﻳﺸﮕﺮ ﻣﺪﻝ ﺭﺍ ﻧﺸﺎﻥ‬
‫ﻣﻲﺩﻫﺪ‪.‬‬
‫‪Cut and Paste 1‬‬
‫ﺷﮑﻞ‪ :۳‬ﻧﻤﺎﻳﻲ ﺍﺯ ﻭﺍﺳﻂ ﮐﺎﺭﺑﺮﻱ ﺍﺑﺰﺍﺭ‬
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‫ﻣﺆﻟﻔﻪ ﻣﻮﻟﺪ ﻣﺪﻝ‪ :‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﻭﻇﻴﻔﻪ ﺧﻮﺍﻧﺪﻥ ﺍﻃﻼﻋﺎﺕ ﻣﺪﻝ ﺍﺯ ﻃﺮﻳﻖ ‪ API‬ﻧﺮﻡ ﺍﻓﺰﺍﺭ ‪ Together‬ﻭ ﺳﺎﺧﺘﻦ ﻳﮏ ﻣﺪﻝ‬
‫‪ OSAN‬ﺭﺍ ﺑﻪ ﻋﻬﺪﻩ ﺩﺍﺭﺩ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﻪ ﮐﻤﮏ ﻣﺆﻟﻔﻪ ﻫﻤﻮﺍﺭﺳﺎﺯﻱ ﻣﺪﻝ‪ ،‬ﺗﻤﺎﻣﻲ ﻓﻌﺎﻟﻴﺘﻬﺎﻱ ﻣﺎﮐﺮﻭﻳﻲ ﺭﺍ ﮐﻪ ﺩﺭ ﻣﺪﻝ ﺑﻪ ﮐﺎﺭ ﺭﻓﺘﻪﺍﻧﺪ‪،‬‬
‫ﮔﺴﺘﺮﺵ ﻣﻲﺩﻫﺪ ﺗﺎ ﻣﺪﻝ ﺑﻪ ﻳﮏ ﻣﺪﻝ ﻫﻤﻮﺍﺭ ﺗﺒﺪﻳﻞ ﺷﻮﺩ‪ .‬ﻣﺪﻝ ﻫﻤﻮﺍﺭ ﻓﻌﺎﻟﻴﺖ ﻣﺎﮐﺮﻭ ﻧﺪﺍﺭﺩ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺩﺭ ﺣﻴﻦ ﺳﺎﺧﺘﻦ ﻣﺪﻝ‬
‫‪ OSAN‬ﻣﻮﺍﺭﺩﻱ ﺭﺍ ﺍﺯ ﻟﺤﺎﻅ ﺗﮑﻤﻴﻞ ﻭ ﺩﺭﺳﺖ ﺑﻮﺩﻥ ﻣﺪﻝ ﮐﻨﺘﺮﻝ ﻣﻲﮐﻨﺪ ﻭ ﺩﺭ ﺻﻮﺭﺗﻴﮑﻪ ﻧﻘﺼﻲ ﺩﺭ ﻣﺪﻝ ﻭﺟﻮﺩ ﺩﺍﺷﺘﻪ ﺑﺎﺷﺪ‪ ،‬ﺑﺎ‬
‫ﭘﻴﻐﺎﻡ ﻣﻨﺎﺳﺐ ﺍﺯ ﮐﺎﺭﺑﺮ ﻣﻲﺧﻮﺍﻫﺪ ﺗﺎ ﻧﺴﺒﺖ ﺑﻪ ﺭﻓﻊ ﺁﻥ ﺍﻗﺪﺍﻡ ﻧﻤﺎﻳﺪ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﺮﺍﻱ ﺧﻮﺍﻧﺪﻥ ﺍﺟﺰﺍﺀ ﺷﺒﮑﻪ ‪ OSAN‬ﺍﺭﺗﺒﺎﻁ ﺗﻨﮕﺎﺗﻨﮕﻲ‬
‫ﺑﺎ ﻧﺮﻡ ﺍﻓﺰﺍﺭ ‪ Together‬ﺩﺍﺭﺩ ﻭ ﺍﺯ ‪ API‬ﻓﺮﺍﻫﻢ ﺷﺪﻩ ﺗﻮﺳﻂ ﺁﻥ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﮐﻨﺪ‪ .‬ﻧﺴﺨﻪ ‪ Together‬ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ‪ ،‬ﻧﺴﺨﻪ ‪۶‬‬
‫ﻣﻲﺑﺎﺷﺪ‪.‬‬
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‫ﻣﺆﻟﻔﻪ ﺗﺒﺪﻳﻞ ﻣﺪﻝ ﺑﻪ ﻣﺪﻝ ﻫﻤﻮﺍﺭ‪ :‬ﭼﻨﺎﻧﭽﻪ ﻣﺪﻝ ﺷﺎﻣﻞ ﻳﮏ ﻳﺎ ﭼﻨﺪ ﻓﻌﺎﻟﻴﺖ ﻣﺎﮐﺮﻭ ﺑﺎﺷﺪ‪ ،‬ﻣﺎﮐﺮﻭﻫﺎﻱ ﺁﻥ ﺗﻮﺳﻂ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﺎﺯ‬
‫ﻼ ﻫﻤﻮﺍﺭ ﺗﺒﺪﻳﻞ ﻣﻲﺷﻮﺩ‪ .‬ﮔﺴﺘﺮﺵ ﻓﻌﺎﻟﻴﺘﻬﺎﻱ ﻣﺎﮐﺮﻭ ﺑﻪ ﺻﻮﺭﺕ ﺑﺎﺯﮔﺸﺘﻲ ﺍﻧﺠﺎﻡ ﻣﻲﺷﻮﺩ‪.‬‬
‫ﻣﻲﺷﻮﻧﺪ ﻭ ﻣﺪﻝ ﺑﻪ ﻳﮏ ﻣﺪﻝ ﮐﺎﻣ ﹰ‬
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‫ﻣﺆﻟﻔﻪ ﻣﺤﺎﺳﺒﻪ ﮔﺰﺍﺭﻩﻫﺎﻱ ﻣﻨﻄﻘﻲ ﻭ ﺍﺟﺮﺍﻱ ﺗﻮﺍﺑﻊ‪ :‬ﻫﺮ ﻓﻌﺎﻟﻴﺖ ﺁﻧﻲ ﻭ ﺯﻣﺎﻧﻲ ﻳﮏ ﮔﺰﺍﺭﻩ ﻣﻨﻄﻘﻲ ﺩﺍﺭﺩ ﻭ ﺩﺭ ﺻﻮﺭﺗﻲ ﻓﻌﺎﻝ‬
‫ﻣﻲﺷﻮﺩ ﮐﻪ ﺁﻥ ﮔﺰﺍﺭﻩ ﺩﺭﺳﺖ ﺑﺎﺷﺪ‪ .‬ﻋﻼﻭﻩ ﺑﺮ ﺍﻳﻦ ﻫﺮ ﻓﻌﺎﻟﻴﺖ ﺯﻣﺎﻧﻲ ﻳﺎ ﺁﻧﻲ ﻳﮏ ﺗﺎﺑﻊ ﺩﺍﺭﺩ ﮐﻪ ﺩﺭ ﻫﻨﮕﺎﻡ ﺍﺟﺮﺍﻱ ﺁﻥ ﻓﻌﺎﻟﻴﺖ‪ ،‬ﺗﺎﺑﻊ‬
‫ﻣﺮﺑﻮﻃﻪ ﺍﺟﺮﺍ ﻣﻲﺷﻮﺩ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﻭﻇﻴﻔﻪ ﻣﺤﺎﺳﺒﻪ ﺍﻳﻦ ﮔﺰﺍﺭﻩﻫﺎﻱ ﻣﻨﻄﻘﻲ ﻭ ﺍﺟﺮﺍﻱ ﺍﻳﻦ ﺗﻮﺍﺑﻊ ﺭﺍ ﺑﺮ ﻋﻬﺪﻩ ﺩﺍﺭﺩ‪ .‬ﻣﺆﻟﻔﻪ ﻣﻮﻟﺪ ﻣﺪﻝ‪ ،‬ﺩﺭ‬
‫ﺣﻴﻦ ﺗﻮﻟﻴﺪ ﻣﺪﻝ‪ ،‬ﻳﮏ ﮐﻼﺱ ﺟﺎﻭﺍ ﺷﺎﻣﻞ ﻣﺘﺪﻫﺎﻳﻲ ﺑﺮﺍﻱ ﮔﺰﺍﺭﻩﻫﺎﻱ ﻣﻨﻄﻘﻲ ﻭ ﺗﻮﺍﺑﻊ ﻓﻌﺎﻟﻴﺘﻬﺎ ﺍﻳﺠﺎﺩ ﻣﻲﮐﻨﺪ‪ .‬ﻣﺆﻟﻔﻪ ﻣﺤﺎﺳﺒﻪ ﮔﺰﺍﺭﻩﻫﺎ‬
‫ﻭ ﺍﺟﺮﺍﻱ ﺗﻮﺍﺑﻊ ﭘﺲ ﺍﺯ ﮐﺎﻣﭙﺎﻳﻞ ﮐﺮﺩﻥ ﺍﻳﻦ ﮐﻼﺱ ﺩﺭ ﺯﻣﺎﻥ ﺍﺟﺮﺍ‪ ،‬ﻣﺘﺪﻫﺎﻱ ﻻﺯﻡ ﺭﺍ ﺍﺯ ﺁﻥ ﻓﺮﺍﺧﻮﺍﻧﻲ ﻣﻲﮐﻨﺪ‪ .‬ﺑﺮﺍﻱ ﺍﻳﻦ ﻣﻨﻈﻮﺭ ﺍﺯ‬
‫ﺍﺑﺰﺍﺭﻱ ﮐﻪ ﻫﻤﺮﺍﻩ ﺑﺴﺘﻪ ‪ JDK‬ﺟﺎﻭﺍ ﻭﺟﻮﺩ ﺩﺍﺭﺩ‪ ،‬ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﺷﻮﺩ‪ .‬ﻧﺴﺨﻪ ﺟﺎﻭﺍﻱ ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ ‪ ۱,۳,۱‬ﻣﻲﺑﺎﺷﺪ‪.‬‬
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‫ﻣﺆﻟﻔﻪ ﺷﺒﻴﻪﺳﺎﺯ‪ :‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺩﺭ ﺷﺒﻴﻪﺳﺎﺯﻱ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﻣﺪﻝ ﺑﻪ ﮐﺎﺭ ﻣﻲﺭﻭﺩ‪ .‬ﺷﺒﻴﻪﺳﺎﺯ ﻣﺪﻝ ﺍﺯ ﺭﻭﺵ ﮔﺴﺴﺘﻪ‪-‬ﭘﻴﺸﺎﻣﺪ‪ ١‬ﺍﺳﺘﻔﺎﺩﻩ‬
‫ﻣﻲﮐﻨﺪ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﻣﻲﺗﻮﺍﻧﺪ ﺩﺭ ﺷﺒﻴﻪﺳﺎﺯﻱ ﺗﻤﺎﻣﻲ ﻣﺪﻟﻬﺎﻱ ‪ OSAN‬ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﻮﺩ ﻭ ﻣﺤﺪﻭﺩﻳﺘﻲ ﻧﺪﺍﺭﺩ‪ .‬ﮐﺎﺭﺑﺮ ﻣﻮﺍﺭﺩﻱ ﺭﺍ ﮐﻪ‬
‫ﻣﻲﺧﻮﺍﻫﺪ ﺑﻪ ﻋﻨﻮﺍﻥ ﻧﺘﻴﺠﻪ ﺷﺒﻴﻪﺳﺎﺯﻱ ﺗﻮﺳﻂ ﺍﺑﺰﺍﺭ ﻣﺤﺎﺳﺒﻪ ﺷﻮﺩ‪ ،‬ﺍﺯ ﻃﺮﻳﻖ ﻭﺍﺳﻂ ﮔﺮﺍﻓﻴﮑﻲ ﻣﺸﺨﺺ ﻣﻲﮐﻨﺪ ﻭ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺩﺭ ﭘﺎﻳﺎﻥ‬
‫ﺷﺒﻴﻪﺳﺎﺯﻱ ﻣﻘﺪﺍﺭ ﻫﺮ ﻳﮏ ﺍﺯ ﺁﻧﻬﺎ ﺭﺍ ﺍﻋﻼﻡ ﻣﻲﮐﻨﺪ‪.‬‬
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‫ﻣﺆﻟﻔﻪ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﻣﺪﻝ‪ :‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﺮﺍﻱ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﻣﺪﻝ ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﻣﻲﺷﻮﺩ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺍﺯ ‪ ۴‬ﻣﺆﻟﻔﻪ‬
‫ﮐﻮﭼﮑﺘﺮ ﺗﺸﮑﻴﻞ ﺷﺪﻩ ﺍﺳﺖ ﮐﻪ ﻫﺮ ﻳﮏ ﺑﺨﺸﻲ ﺍﺯ ﻭﻇﺎﻳﻒ ﻣﺮﺗﺒﻂ ﺑﺎ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﻣﺪﻝ ﺭﺍ ﺑﺮ ﻋﻬﺪﻩ ﺩﺍﺭﻧﺪ‪ .‬ﺍﻳﻦ ‪ ۴‬ﻣﺆﻟﻔﻪ ﻋﺒﺎﺭﺗﻨﺪ ﺍﺯ‬
‫ﻣﺆﻟﻔﻪ ﻣﻮﻟﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﻭ ﺯﻧﺠﻴﺮﻩ ﻣﺎﺭﮐﻮﻑ‪ ،‬ﻣﺆﻟﻔﻪ ﻣﺪﻳﺮ ﻓﻀﺎﻱ ﺣﺎﻟﺖ‪ ،‬ﻣﺆﻟﻔﻪ ﻋﻤﻠﻴﺎﺕ ﻣﺎﺗﺮﻳﺴﻲ ﻭ ﻣﺆﻟﻔﻪ ﻋﻤﻠﻴﺎﺕ ﻣﺤﺎﺳﺒﺎﺕ‬
‫ﻋﺪﺩﻱ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪﻫﺎ ﺩﺭ ﺑﺨﺶﻫﺎﻱ ﺑﻌﺪﻱ ﺷﺮﺡ ﺩﺍﺩﻩ ﻣﻲﺷﻮﻧﺪ‪.‬‬
‫‪ o‬ﻣﺆﻟﻔﻪ ﻣﻮﻟﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ‪ :‬ﻭﻇﻴﻔﻪ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮﺍﻱ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﻣﺪﻝ ﻣﻲﺑﺎﺷﺪ‪ .‬ﺣﻞ ﺗﺤﻠﻴﻠﻲ‬
‫ﺗﻨﻬﺎ ﺩﺭ ﻣﻮﺭﺩ ﻣﺪﻟﻬﺎﻳﻲ ﺑﻪ ﮐﺎﺭ ﻣﻲﺭﻭﺩ ﮐﻪ ﺷﺮﺍﻳﻂ ﺧﺎﺻﻲ ﺭﺍ ﺩﺍﺭﺍ ﺑﺎﺷﻨﺪ ﻭ ﺑﺘﻮﺍﻥ ﺑﺮﺍﻱ ﺁﻧﻬﺎ ﺯﻧﺠﻴﺮﻩ ﻣﺎﺭﮐﻮﻑ ﻣﻌﺎﺩﻝ ﺗﻮﻟﻴﺪ ﮐﺮﺩ‪ .‬ﺍﻳﻦ‬
‫ﻣﺆﻟﻔﻪ ﺩﺭ ﺣﻴﻦ ﺗﻮﻟﻴﺪ ﺣﺎﻟﺘﻬﺎﻱ ﺳﻴﺴﺘﻢ‪ ،‬ﻧﺮﺥ ﺍﻧﺘﻘﺎﻝ ﺍﺯ ﻳﮏ ﺣﺎﻟﺖ ﺑﻪ ﺣﺎﻟﺖ ﺩﻳﮕﺮ ﺭﺍ ﻧﻴﺰ ﻣﺤﺎﺳﺒﻪ ﻣﻲﮐﻨﺪ‪ ،‬ﺑﻨﺎﺑﺮﺍﻳﻦ ﻧﺘﻴﺠﻪ ﺁﻥ ﻳﮏ‬
‫ﺯﻧﺠﻴﺮﻩ ﻣﺎﺭﮐﻮﻑ ﺍﺳﺖ ﮐﻪ ﻣﻲﺗﻮﺍﻥ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻓﺮﻣﻮﻟﻬﺎﻱ ﺭﻳﺎﺿﻲ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻱ ﻣﻮﺭﺩ ﻧﻈﺮ ﺩﺭ ﺍﺭﺗﺒﺎﻁ ﺑﺎ ﮐﺎﺭﺁﻳﻲ ﺳﻴﺴﺘﻢ ﺭﺍ ﺍﺯ ﺭﻭﻱ‬
‫ﺁﻥ ﻣﺤﺎﺳﺒﻪ ﻧﻤﻮﺩ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺍﺯ ﻣﺆﻟﻔﻪ ﻣﺪﻳﺮ ﻓﻀﺎﻱ ﺣﺎﻟﺖ‪ ،‬ﺟﻬﺖ ﻧﮕﻬﺪﺍﺭﻱ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﮐﻨﺪ‪.‬‬
‫‪ o‬ﻣﺆﻟﻔﻪ ﻣﺪﻳﺮ ﻓﻀﺎﻱ ﺣﺎﻟﺖ‪ :‬ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺍﻳﻨﮑﻪ ﺩﺭ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﺳﻴﺴﺘﻢ‪ ،‬ﺗﻮﻟﻴﺪ ﻭ ﻧﮕﻬﺪﺍﺭﻱ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺍﻣﺮﻱ ﺑﺴﻴﺎﺭ‬
‫ﺣﺴﺎﺱ ﺍﺳﺖ‪ ،‬ﻟﺬﺍ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﻪ ﻣﻨﻈﻮﺭ ﻧﮕﻬﺪﺍﺭﻱ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺗﻮﺳﻂ ﻣﺆﻟﻔﻪ ﻣﻮﻟﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﻃﺮﺍﺣﻲ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺑﺪﻳﻬﻲ‬
‫ﺍﺳﺖ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺳﺎﺧﺘﻤﺎﻥ ﺩﺍﺩﻩﻫﺎﻱ ﮐﺎﺭﺁﻣﺪ ﺟﻬﺖ ﻧﮕﻬﺪﺍﺭﻱ ﺣﺎﻟﺘﻬﺎ‪ ،‬ﮐﻪ ﺍﻣﮑﺎﻥ ﺟﺴﺘﺠﻮﻱ ﺳﺮﻳﻊ ﺣﺎﻟﺘﻬﺎ ﺭﺍ ﻓﺮﺍﻫﻢ ﮐﻨﺪ ﻭ ﻫﻤﭽﻨﻴﻦ‬
‫ﺗﮑﺮﺍﺭﻱ ﻧﺒﻮﺩﻥ ﺣﺎﻟﺘﻬﺎ ﺭﺍ ﺗﻀﻤﻴﻦ ﮐﻨﺪ ﺑﺴﻴﺎﺭ ﺿﺮﻭﺭﻱ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺍﺯ ﺳﺎﺧﺘﻤﺎﻥ ﺩﺍﺩﻩﻫﺎﻱ ﻣﺘﻨﻮﻋﻲ ﺍﺯ ﻗﺒﻴﻞ ﻟﻴﺴﺖ ﻭ ﺟﺪﻭﻝ ‪hash‬‬
‫ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﮐﻨﺪ‪.‬‬
‫ﻃﺮﺍﺣﻲ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﻪ ﺻﻮﺭﺕ ﻳﮏ ﻣﺆﻟﻔﻪ ﻣﺠﺰﺍ‪ ،‬ﺍﻣﮑﺎﻥ ﺑﺴﻂ ﺁﻧﺮﺍ ﻓﺮﺍﻫﻢ ﻣﻲﮐﻨﺪ ﻭ ﻣﻲﺗﻮﺍﻥ ﺩﺭ ﺁﻳﻨﺪﻩ ﻗﺎﺑﻠﻴﺘﻬﺎﻳﻲ ﻣﺎﻧﻨﺪ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ‬
‫ﺭﻭﺷﻬﺎﻱ ﻣﺒﺘﻨﻲ ﺑﺮ ﺩﻳﺴﮏ ]‪ [۱۳‬ﺟﻬﺖ ﻧﮕﻬﺪﺍﺭﻱ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺭﺍ ﺑﻪ ﺁﻥ ﺍﻓﺰﻭﺩ‪.‬‬
‫ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﺮﺍﻱ ﻧﮕﻬﺪﺍﺭﻱ ﻧﺮﺥ ﺍﻧﺘﻘﺎﻝ ﺑﻴﻦ ﺣﺎﻟﺘﻬﺎ ﺍﺯ ﻣﺆﻟﻔﻪ ﻋﻤﻠﻴﺎﺕ ﻣﺎﺗﺮﻳﺴﻲ ﺍﺳﺘﻔﺎﺩﻩ ﻣﻲﮐﻨﺪ ﮐﻪ ﺩﺭ ﻗﺴﻤﺘﻬﺎﻱ ﺑﻌﺪﻱ ﺑﻪ ﺷﺮﺡ ﺁﻥ‬
‫ﺧﻮﺍﻫﻴﻢ ﭘﺮﺩﺍﺧﺖ‪.‬‬
‫‪ o‬ﻣﺆﻟﻔﻪ ﻋﻤﻠﻴﺎﺕ ﻣﺎﺗﺮﻳﺴﻲ‪ :‬ﺯﻧﺠﻴﺮﻩ ﻣﺎﺭﮐﻮﻓﻲ ﮐﻪ ﺗﻮﺳﻂ ﻣﺆﻟﻔﻪ ﻣﻮﻟﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺗﻮﻟﻴﺪ ﻣﻲﺷﻮﺩ‪ ،‬ﺑﺎ ﻳﮏ ﻣﺎﺗﺮﻳﺲ ﻣﺘﻨﺎﻇﺮ ﺍﺳﺖ‬
‫ﮐﻪ ﻣﺒﻨﺎﻱ ﻋﻤﻠﻴﺎﺕ ﻣﺤﺎﺳﺒﺎﺕ ﻋﺪﺩﻱ ﺑﺮﺍﻱ ﻣﺤﺎﺳﺒﻪ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻱ ﮐﺎﺭﺁﻳﻲ ﺳﻴﺴﺘﻢ ﻗﺮﺍﺭ ﻣﻲﮔﻴﺮﺩ‪ .‬ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺍﻳﻨﮑﻪ ﺍﻏﻠﺐ ﻣﺎﺗﺮﻳﺲ ﻧﺮﺥ‬
‫ﺍﻧﺘﻘﺎﻝ ﻣﺎﺗﺮﻳﺴﻲ ﺗﻨﮏ ﻭ ﮐﻢ ﺗﺮﺍﮐﻢ ﺍﺳﺖ‪ ،‬ﺑﻪ ﻋﺒﺎﺭﺗﻲ ﺗﻌﺪﺍﺩ ﺯﻳﺎﺩﻱ ﻋﻨﺼﺮ ﺻﻔﺮ ﺩﺍﺭﺩ‪ ،‬ﻟﺬﺍ ﺑﺮﺍﻱ ﻧﮕﻬﺪﺍﺭﻱ ﺁﻥ ﺍﺯ ﺭﻭﺵ ﻟﻴﺴﺘﻲ ﺍﺳﺘﻔﺎﺩﻩ‬
‫ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺑﺮﺍﻱ ﺍﻓﺰﺍﻳﺶ ﮐﺎﺭﺁﻳﻲ ﺍﻳﻦ ﻣﺎﺗﺮﻳﺲ ﺩﻭ ﻟﻴﺴﺖ ﺭﺍ ﺩﺭ ﺑﺮ ﻣﻲﮔﻴﺮﺩ ﮐﻪ ﻳﮑﻲ ﺑﺮ ﺣﺴﺐ ﺣﺎﻟﺖ ﻣﺒﺪﺃ ﻭ ﺩﻳﮕﺮﻱ ﺑﺮ ﺣﺴﺐ‬
‫ﺣﺎﻟﺖ ﻣﻘﺼﺪ ﻣﺮﺗﺐ ﺷﺪﻩﺍﻧﺪ ﻭ ﺑﺎ ﺩﺍﺷﺘﻦ ﺩﻭ ﻟﻴﺴﺖ ﻣﺠﺰﺍ ﻋﻤﻠﻴﺎﺕ ﻣﺤﺎﺳﺒﺎﺗﻲ ﺑﺎ ﺳﺮﻋﺖ ﺑﺎﻻﺗﺮﻱ ﻣﻴﺴﺮ ﻣﻲﺷﻮﺩ‪.‬‬
‫‪ o‬ﻣﺆﻟﻔﻪ ﻋﻤﻠﻴﺎﺕ ﻣﺤﺎﺳﺒﺎﺕ ﻋﺪﺩﻱ‪ :‬ﺩﺭ ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﺣﺎﻟﺖ ﭘﺎﻳﺪﺍﺭ ﻣﺪﻝ‪ ،‬ﭘﺲ ﺍﺯ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﻭ ﺯﻧﺠﻴﺮﻩ ﻣﺎﺭﮐﻮﻑ ﻣﺘﻨﺎﻇﺮ ﻭ‬
‫ﻣﺎﺗﺮﻳﺲ ﻧﺮﺥ ﺍﻧﺘﻘﺎﻝ‪ ،‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﺷﻬﺎﻱ ﻣﺤﺎﺳﺒﺎﺕ ﻋﺪﺩﻱ ﭘﺎﺭﺍﻣﺘﺮﻫﺎﻱ ﮐﺎﺭﺁﻳﻲ ﻣﻮﺭﺩ ﻧﻈﺮ ﺳﻴﺴﺘﻢ ﺭﺍ ﻣﺤﺎﺳﺒﻪ ﻣﻲﮐﻨﺪ‪.‬‬
‫‪Discrete Event Simulation 1‬‬
‫ﺣﻞ ﺯﻧﺠﻴﺮﻩ ﻣﺎﺭﮐﻮﻑ ﻣﻨﺠﺮ ﺑﻪ ﺣﻞ ﺩﺳﺘﮕﺎﻩ ﻣﻌﺎﺩﻻﺕ ﺧﻄﻲ ﻣﻲﺷﻮﺩ ﮐﻪ ﺍﻳﻦ ﮐﺎﺭ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﺷﻬﺎﻱ ﺗﮑﺮﺍﺭﻱ‪ ١‬ﻣﺤﺎﺳﺒﺎﺕ ﻋﺪﺩﻱ‬
‫ﺍﻧﺠﺎﻡ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
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‫ﻣﺆﻟﻔﻪ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ‪ :‬ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﻪ ﻣﻨﻈﻮﺭ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ ﺟﻬﺖ ﺁﻣﻮﺧﺘﻦ ﻣﺪﻝ ‪ OSAN‬ﻭ ﻳﺎ ﺭﻓﻊ ﺍﻳﺮﺍﺩﻫﺎﻱ ﻣﺪﻝ‬
‫ﺑﻪ ﮐﺎﺭ ﻣﻲﺭﻭﺩ‪ .‬ﺭﻭﺵ ﮐﺎﺭ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﺑﺴﻴﺎﺭ ﺷﺒﻴﻪ ﻣﺆﻟﻔﻪ ﺷﺒﻴﻪﺳﺎﺯ ﻣﺪﻝ ﺍﺳﺖ ﺑﺎ ﺍﻳﻦ ﺗﻔﺎﻭﺕ ﮐﻪ ﭘﺲ ﺍﺯ ﻫﺮ ﺗﻐﻴﻴﺮﻱ ﺩﺭ ﻣﺪﻝ ﻣﻲﺗﻮﺍﻥ‬
‫ﻧﺘﻴﺠﻪ ﺁﻧﺮﺍ ﻣﺸﺎﻫﺪﻩ ﮐﺮﺩ ﻭ ﺍﺟﺮﺍﻱ ﮔﺎﻡ ﺑﻌﺪﻱ ﺩﺭ ﺍﺧﺘﻴﺎﺭ ﮐﺎﺭﺑﺮ ﻣﻲﺑﺎﺷﺪ‪ .‬ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ ﺩﺭ ﻓﻬﻢ ﻧﺤﻮﻩ ﮐﺎﺭﮐﺮﺩ ﻣﺪﻝ ﻭ ﺭﻓﻊ‬
‫ﻧﻮﺍﻗﺺ ﺁﻥ ﺑﺴﻴﺎﺭ ﻣﺆﺛﺮ ﺍﺳﺖ‪ .‬ﻣﺴﺄﻟﻪ ﺩﻳﮕﺮﻱ ﮐﻪ ﺩﺭ ﺍﻳﻨﺠﺎ ﺣﺎﺋﺰ ﺍﻫﻤﻴﺖ ﺍﺳﺖ‪ ،‬ﺍﻳﻦ ﺍﺳﺖ ﮐﻪ ﺑﺮ ﺧﻼﻑ ﻣﺪﻟﻬﺎﻳﻲ ﻫﻤﭽﻮﻥ ‪ SAN‬ﻭ‬
‫‪ PN‬ﮐﻪ ﺗﻨﻬﺎ ﺷﺎﻣﻞ ﻧﺸﺎﻧﻪﻫﺎﻱ ﺳﺎﺩﻩ ﺑﻮﺩﻧﺪ‪ ،‬ﺩﺭ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ ‪ OSAN‬ﺍﻣﮑﺎﻥ ﻧﺸﺎﻥ ﺩﺍﺩﻥ ﺍﺷﻴﺎﺋﻲ ﮐﻪ ﺩﺭ ﻣﮑﺎﻧﻬﺎﻱ ﺭﻧﮕﻲ‬
‫ﻭﺟﻮﺩ ﺩﺍﺭﻧﺪ ﻓﺮﺍﻫﻢ ﻧﻴﺴﺖ‪ .‬ﺑﻪ ﻋﺒﺎﺭﺕ ﺩﻳﮕﺮ ﻧﻤﻲﺗﻮﺍﻥ ﺍﺷﻴﺎﺀ ﺁﻧﺮﺍ ﺑﻪ ﺻﻮﺭﺕ ﮔﺮﺍﻓﻴﮑﻲ ﻧﻤﺎﻳﺶ ﺩﺍﺩ‪ .‬ﺍﺯ ﻃﺮﻑ ﺩﻳﮕﺮ ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﺍﻳﻨﮑﻪ‬
‫ﻣﺪﻝ ‪ OSAN‬ﺍﻣﮑﺎﻥ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﻓﻌﺎﻟﻴﺘﻬﺎﻱ ﻣﺎﮐﺮﻭ ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﺁﻭﺭﺩ‪ ،‬ﻟﺬﺍ ﺩﺭ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻧﻤﻲﺗﻮﺍﻥ ﺗﻤﺎﻣﻲ ﻣﮑﺎﻧﻬﺎﻱ ﻣﺪﻝ ﺭﺍ‬
‫ﻳﮑﺠﺎ ﺩﻳﺪ‪ .‬ﺑﻪ ﻫﻤﻴﻦ ﺩﻟﻴﻞ ﺩﺭ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ ﻣﺪﻝ ﺍﺯ ﺭﻭﺷﻲ ﺷﺒﻴﻪ ﺍﺷﮑﺎﻟﺰﺩﺍ‪ ٢‬ﻫﺎﻱ ﺯﺑﺎﻧﻬﺎﻱ ﺑﺮﻧﺎﻣﻪﺳﺎﺯﻱ ﺍﺳﺘﻔﺎﺩﻩ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺑﺪﻳﻦ‬
‫ﺻﻮﺭﺕ ﮐﻪ ﮐﺎﺭﺑﺮ ﻣﻲﺗﻮﺍﻧﺪ ﻋﺒﺎﺭﺗﻬﺎﻱ ﻣﻮﺭﺩ ﻧﻈﺮ ﺧﻮﺩ ﺭﺍ ﻣﺸﺎﺑﻪ ﻣﻔﻬﻮﻡ ‪ watch‬ﺩﺭ ﺍﺑﺰﺍﺭﻫﺎﻱ ﻣﺮﺑﻮﻁ ﺑﻪ ﺯﺑﺎﻧﻬﺎﻱ ﺑﺮﻧﺎﻣﻪ ﻧﻮﻳﺴﻲ‬
‫ﺗﻌﺮﻳﻒ ﮐﻨﺪ ﻭ ﺳﭙﺲ ﺍﻳﻦ ﻣﺆﻟﻔﻪ ﭘﺲ ﺍﺯ ﺍﺟﺮﺍﻱ ﻫﺮ ﻣﺮﺣﻠﻪ‪ ،‬ﻣﻘﺪﺍﺭ ﺁﻥ ﻋﺒﺎﺭﺍﺕ ﺭﺍ ﻣﺤﺎﺳﺒﻪ ﮐﺮﺩﻩ ﻭ ﺑﻪ ﮐﺎﺭﺑﺮ ﻧﺸﺎﻥ ﺧﻮﺍﻫﺪ ﺩﺍﺩ‪.‬‬
‫‪ .۵‬ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭ‬
‫ﺩﺭ ﺍﻳﻦ ﺑﺨﺶ ﺑﻪ ﮐﻤﮏ ﻳﮏ ﻣﺪﻝ ﺑﻪ ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭ ﻭ ﮐﺎﺭﺁﻳﻲ ﺁﻥ ﺧﻮﺍﻫﻴﻢ ﭘﺮﺩﺍﺧﺖ‪.‬‬
‫ﻣﺪﻝ ‪ :Kanban‬ﺑﺮﺍﻱ ﺍﺭﺯﻳﺎﺑﻲ ﺯﻣﺎﻥ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺍﺯ ﺍﻳﻦ ﻣﺪﻝ ﺍﺳﺘﻔﺎﺩﻩ ﮐﺮﺩﻩﺍﻳﻢ‪ .‬ﻳﮑﻲ ﺍﺯ ﻣﺰﺍﻳﺎﻱ ﺍﻳﻦ ﻣﺪﻝ ﺍﻳﻦ ﺍﺳﺖ ﮐﻪ ﺑﺎ ﺍﻓﺰﺍﻳﺶ‬
‫ﺗﻌﺪﺍﺩ ﻧﺸﺎﻧﻪﻫﺎ ﺩﺭ ﺣﺎﻟﺖ ﺍﻭﻟﻴﻪ‪ ،‬ﺗﻌﺪﺍﺩ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﻪ ﺳﺮﻋﺖ ﺭﺷﺪ ﭘﻴﺪﺍ ﻣﻲﮐﻨﺪ‪ .‬ﻋﻼﻭﻩ ﺑﺮ ﺍﻳﻦ ﻣﺪﻝ ‪ Kanban‬ﻣﺪﻝ‬
‫ﻣﻨﺎﺳﺒﻲ ﺑﺮﺍﻱ ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭﻫﺎﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺍﺳﺖ ﻭ ﺑﻪ ﻋﻨﻮﺍﻥ ﻣﺜﺎﻝ ﺑﺮﺍﻱ ﺍﺭﺯﻳﺎﺑﻲ ‪ [۱۰ ] UltraSAN‬ﻭ ‪ [۱۱] Mobius‬ﻧﻴﺰ ﺑﻪ ﮐﺎﺭ‬
‫ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫‪٣‬‬
‫ﻣﺪﻝ ‪ Kanban‬ﺗﻮﺳﻂ ‪ Ciardo‬ﻭ ‪ [۱۴] Tilgner‬ﺑﺮﺍﻱ ‪ GSPN‬ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﻫﻤﭽﻨﻴﻦ ﺍﻳﻦ ﻣﺪﻝ ﺑﺮﺍﻱ ﺍﺭﺯﻳﺎﺑﻲ‬
‫‪ Mobius‬ﻭ ﻣﻘﺎﻳﺴﻪ ﺁﻥ ﺑﺎ ‪ UltraSAN‬ﺩﺭ ]‪ [۱۵‬ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﻣﺪﻝ ﺧﻂ ﺗﻮﻟﻴﺪ ﺳﺎﺩﻩ ﻳﮏ ﮐﺎﺭﺧﺎﻧﻪ ﺭﺍ ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ ﻭ‬
‫ﺍﺯ ﭼﻬﺎﺭ ﻣﺮﺣﻠﻪ ﺗﺸﮑﻴﻞ ﻣﻲﺷﻮﺩ‪ .‬ﺩﺭ ﻫﺮ ﻣﺮﺣﻠﻪ ﻣﻤﮑﻦ ﺍﺳﺖ ﮐﺎﺭ ﺷﺊ ﻣﻮﺭﺩ ﻧﻈﺮ ﺗﮑﻤﻴﻞ ﺷﻮﺩ ﻭ ﺑﻪ ﻣﺮﺣﻠﻪ ﺑﻌﺪﻱ ﻓﺮﺳﺘﺎﺩﻩ ﺷﻮﺩ ﻭ ﻳﺎ ﺍﻳﻨﮑﻪ‬
‫ﻣﺠﺪﺩﹰﺍ ﻫﻤﺎﻥ ﻣﺮﺣﻠﻪ ﺭﺍ ﻃﻲ ﮐﻨﺪ‪ .‬ﺍﺷﻴﺎﺋﻲ ﮐﻪ ﻣﺮﺣﻠﻪ ‪ ۱‬ﺭﺍ ﭘﺸﺖ ﺳﺮ ﻣﻲﮔﺬﺍﺭﻧﺪ ﺑﻪ ﻃﻮﺭ ﻣﻮﺍﺯﻱ ﻭﺍﺭﺩ ﻣﺮﺣﻠﻪ ‪ ۲‬ﻭ ‪ ۳‬ﻣﻲﺷﻮﻧﺪ‪ .‬ﭘﺲ ﺍﺯ ﺍﻳﻨﮑﻪ‬
‫ﮐﺎﺭ ﺷﺊ ﻣﻮﺭﺩ ﻧﻈﺮ ﺩﺭ ﻫﺮ ﺩﻭ ﻣﺮﺣﻠﻪ ‪ ۲‬ﻭ ‪ ۳‬ﺗﮑﻤﻴﻞ ﺷﺪ‪ ،‬ﺑﻪ ﻣﺮﺣﻠﻪ ‪ ۴‬ﺭﺍﻩ ﭘﻴﺪﺍ ﻣﻲﮐﻨﺪ‪ .‬ﻣﺪﻝ ‪ SAN‬ﻣﺘﻨﺎﻇﺮ ﺑﺎ ﺍﻳﻦ ﻣﺪﻝ ﺩﺭ ﺷﮑﻞ ‪ ۴‬ﻭ ﻣﺪﻝ‬
‫‪ OSAN‬ﻣﺘﻨﺎﻇﺮ ﺑﺎ ﺁﻥ ﺩﺭ ﺷﮑﻞ ‪ ۵‬ﻧﺸﺎﻥ ﺩﺍﺩﻩ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﺩﺭ ﻣﺪﻝ ‪ OSAN‬ﻫﺮ ﻣﺮﺣﻠﻪ ﺑﻪ ﺻﻮﺭﺕ ﻳﮏ ﻓﻌﺎﻟﻴﺖ ﻣﺎﮐﺮﻭ ﻣﺪﻝ ﺷﺪﻩ ﺍﺳﺖ‪.‬‬
‫ﺍﻳﻦ ﻓﻌﺎﻟﻴﺖ ﻣﺎﮐﺮﻭ ‪ ۴‬ﺑﺎﺭ ﺩﺭ ﻣﺪﻝ ﺑﻪ ﮐﺎﺭ ﮔﺮﻓﺘﻪ ﺷﺪﻩ ﺍﺳﺖ‪ .‬ﻣﺪﻝ ﺷﮑﻞ ‪ ۴‬ﺩﺭ ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭﻫﺎﻱ ‪ UltraSAN‬ﻭ ‪ Mobius‬ﺑﺮﺍﻱ ‪SAN‬‬
‫ﻭ ﻣﺪﻝ ﺷﮑﻞ ‪ ۵‬ﺩﺭ ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭ ﻣﺎ ﺑﺮﺍﻱ ‪ OSAN‬ﻣﻮﺭﺩ ﺍﺳﺘﻔﺎﺩﻩ ﻗﺮﺍﺭ ﮔﺮﻓﺘﻪ ﺍﺳﺖ‪ .‬ﻇﺮﻓﻴﺖ ﻫﺮ ﻣﺮﺣﻠﻪ ﺍﺯ ‪ ۴‬ﻣﺮﺣﻠﻪ ﻓﻮﻕ ﺑﻮﺳﻴﻠﻪ ﻣﮑﺎﻧﻬﺎﻱ‬
‫ﻼ ﺍﮔﺮ ﺩﺭ ﻣﮑﺎﻥ ‪ kanban4‬ﺩﺭ ﺣﺎﻟﺖ ﺍﻭﻟﻴﻪ ﺩﻭ ﻧﺸﺎﻧﻪ ﺩﺍﺷﺘﻪ ﺑﺎﺷﻴﻢ‪ ،‬ﺑﻪ ﺍﻳﻦ ﻣﻌﻨﺎﺳﺖ ﮐﻪ ﻣﺮﺣﻠﻪ ‪ ۴‬ﻇﺮﻓﻴﺖ‬
‫‪ Kanban‬ﮐﻨﺘﺮﻝ ﻣﻲﺷﻮﺩ‪ .‬ﻣﺜ ﹰ‬
‫‪Iterative 1‬‬
‫‪Debugger 2‬‬
‫‪Generalized Stochastic Petri Net3‬‬
‫ﺗﻮﻟﻴﺪ ﻭ ﺗﮑﻤﻴﻞ ﺩﻭ ﺷﺊ ﺭﺍ ﺩﺍﺭﺩ‪ .‬ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺭﺍ ﺩﺭ ﺣﺎﻟﺘﻬﺎﻱ ﻣﺨﺘﻠﻔﻲ ﮐﻪ ﺩﺭ ﻫﺮ ﻳﮏ ﺍﺯ ‪ ۴‬ﻣﮑﺎﻥ ‪ ۱ ، kanban‬ﺗﺎ ‪ ۴‬ﻧﺸﺎﻧﻪ ﺩﺍﺷﺘﻪ‬
‫ﺑﺎﺷﻴﻢ‪ ،‬ﻣﻮﺭﺩ ﺑﺮﺭﺳﻲ ﻗﺮﺍﺭ ﺩﺍﺩﻩﺍﻳﻢ‪ .‬ﺑﻨﺎﺑﺮﺍﻳﻦ ﺩﺭ ﺍﻳﻦ ﺁﺯﻣﺎﻳﺶ ﺩﺭ ﮐﻞ ﺷﺒﮑﻪ ﺑﻴﻦ ‪ ۴‬ﺗﺎ ‪ ۱۶‬ﻧﺸﺎﻧﻪ ﺧﻮﺍﻫﻴﻢ ﺩﺍﺷﺖ‪ .‬ﺗﻌﺪﺍﺩ ﻧﺸﺎﻧﻪﻫﺎ ﺩﺭ ﻫﺮ ﻳﮏ ﺍﺯ‬
‫ﺍﻳﻦ ‪ ۴‬ﻣﺮﺣﻠﻪ ﺩﺭ ﻃﻮﻝ ﺍﺟﺮﺍﻱ ﻣﺪﻝ ﺛﺎﺑﺖ ﺧﻮﺍﻫﺪ ﺑﻮﺩ‪) .‬ﻇﺮﻓﻴﺖ ﻫﺮ ﻣﺮﺣﻠﻪ ﺛﺎﺑﺖ ﺍﺳﺖ ﻭ ﺩﺭ ﺣﻴﻦ ﺍﺟﺮﺍ ﺗﻐﻴﻴﺮ ﻧﻤﻲﮐﻨﺪ‪(.‬‬
‫ﺷﮑﻞ ‪ :۴‬ﻣﺪﻝ ‪ Kanba‬ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺗﻌﺮﻳﻒ ﺍﻭﻟﻴﻪ ‪SAN‬‬
‫ﺷﮑﻞ‪ :۵‬ﺷﺒﮑﻪ ‪ OSAN‬ﻣﺪﻝ ‪Kanban‬‬
‫‪1‬‬
‫ﺗﻌﺮﻳﻒ ﺳﺎﻝ ‪۱۹۸۴‬‬
‫‪١‬‬
‫ﺟﺪﻭﻝ ‪ ۱‬ﺗﻌﺪﺍﺩ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﻭ ﺯﻣﺎﻥ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮ ﺍﺳﺎﺱ ﺗﻌﺪﺍﺩ ﻧﺸﺎﻧﻪﻫﺎﻱ ﻣﻮﺟﻮﺩ ﺩﺭ ﻫﺮ ﺣﺎﻟﺖ ‪ Kanban‬ﺭﺍ ﻧﺸﺎﻥ‬
‫ﻣﻲﺩﻫﺪ‪ .‬ﺷﮑﻞ ‪ ۶‬ﻧﻤﻮﺩﺍﺭ ﺯﻣﺎﻥ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮ ﺣﺴﺐ ﺗﻌﺪﺍﺩ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺭﺍ ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ‪.‬‬
‫ﺟﺪﻭﻝ ‪ :۱‬ﻧﺘﺎﻳﺞ ﺍﺭﺯﻳﺎﺑﻲ ﺍﺑﺰﺍﺭ‬
‫ﺗﻌﺪﺍﺩ ﻧﺸﺎﻧﻪﻫﺎ ﺩﺭ ﻣﮑﺎﻧﻬﺎﻱ ‪ Kanban‬ﺩﺭ ﺣﺎﻟﺖ ﺍﻭﻟﻴﻪ ﺗﻌﺪﺍﺩ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ‬
‫ﺯﻣﺎﻥ ﻻﺯﻡ ﺑﺮﺍﻱ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮ ﺣﺴﺐ ﺛﺎﻧﻴﻪ‬
‫‪۱‬‬
‫‪۱۶۰‬‬
‫‪۰,۱۵‬‬
‫‪۲‬‬
‫‪۴۶۰۰‬‬
‫‪۸‬‬
‫‪۳‬‬
‫‪۵۸۴۰۰‬‬
‫‪۸۷۶‬‬
‫‪1000‬‬
‫‪100‬‬
‫‪10‬‬
‫‪1‬‬
‫‪4‬‬
‫‪3‬‬
‫‪2‬‬
‫‪1‬‬
‫‪0‬‬
‫ﺷﮑﻞ ‪ :۶‬ﻧﻤﻮﺩﺍﺭ ﺯﻣﺎﻥ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮ ﺣﺴﺐ ﺗﻌﺪﺍﺩ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺩﺭ ﺍﺑﺰﺍﺭ ‪OSAN‬‬
‫ﺷﮑﻞ ‪ ۷‬ﻧﻤﻮﺩﺍﺭ ﺯﻣﺎﻥ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮ ﺣﺴﺐ ﺗﻌﺪﺍﺩ ﺣﺎﻟﺘﻬﺎﻱ ﺗﻮﻟﻴﺪ ﺷﺪﻩ ﺭﺍ ﮐﻪ ﺑﺮ ﺍﺳﺎﺱ ﻧﻤﻮﻧﻪﻫﺎﻱ ﻣﺨﺘﻠﻒ ﺑﻪ ﺩﺳﺖ ﺁﻣﺪﻩ ﺍﺳﺖ‬
‫ﻧﺸﺎﻥ ﻣﻲﺩﻫﺪ‪.‬‬
‫ﺷﮑﻞ‪ :۷‬ﻧﻤﻮﺩﺍﺭ ﺯﻣﺎﻥ ﺗﻮﻟﻴﺪ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﺑﺮ ﺣﺴﺐ ﺗﻌﺪﺍﺩ ﺣﺎﻻﺕ‬
‫‪ .۶‬ﻧﺘﻴﺠﻪﮔﻴﺮﻱ‬
‫ﺩﺭ ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺍﺑﺰﺍﺭ ﺟﺪﻳﺪﻱ ﺭﺍ ﺑﺮﺍﻱ ﻣﺪﻟﺴﺎﺯﻱ ﺑﺎ ﺷﺒﮑﻪﻫﺎﻱ ﻓﻌﺎﻟﻴﺖ ﺗﺼﺎﺩﻓﻲ ﺷﻴﺌﻲ ﻣﻌﺮﻓﻲ ﮐﺮﺩﻳﻢ‪ .‬ﻫﻤﺎﻧﮕﻮﻧﻪ ﮐﻪ ﺩﺭ ﺑﺨﺶ ‪ ۳‬ﺫﮐﺮ ﺷﺪ‪،‬‬
‫ﺍﺑﺰﺍﺭ ﻣﺸﺎﺑﻬﻲ ﺑﺮﺍﻱ ﻣﺪﻝ ‪ OSAN‬ﻣﻌﺮﻓﻲ ﻧﺸﺪﻩ ﺍﺳﺖ‪ .‬ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﻗﺎﺑﻠﻴﺘﻬﺎﻳﻲ ﻧﻈﻴﺮ ﺳﺎﺧﺘﻦ ﻣﺪﻝ‪ ،‬ﺷﺒﻴﻪﺳﺎﺯﻱ‪ ،‬ﺣﻞ ﺗﺤﻠﻴﻠﻲ ﻭ ﻣﺘﺤﺮﮎﺳﺎﺯﻱ‬
‫ﻣﺪﻝ ﺭﺍ ﻓﺮﺍﻫﻢ ﻣﻲﺁﻭﺭﺩ‪ .‬ﻳﮑﻲ ﺍﺯ ﺍﻫﺪﺍﻑ ﻣﺎ ﺍﻳﻦ ﺑﻮﺩﻩ ﺍﺳﺖ ﮐﻪ ﺗﺎ ﺣﺪ ﻣﻤﮑﻦ ﻣﻔﺎﻫﻴﻢ ﺍﺭﺯﻳﺎﺑﻲ ﻭ ﺩﺭﺳﺘﻲﻳﺎﺑﻲ ﺭﺍ ﺑﻪ ﺻﻮﺭﺕ ﻋﻤﻠﻲ ﺍﺭﺍﺋﻪ‬
‫ﺩﻫﻴﻢ‪ .‬ﺑﻪ ﻫﻤﻴﻦ ﻣﻨﻈﻮﺭ ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺑﻪ ﺻﻮﺭﺕ ﭘﻴﻤﺎﻧﻪﺍﻱ ﺭﻭﻱ ‪ Together‬ﺳﺎﺧﺘﻪ ﺷﺪﻩ ﻭ ﻣﺪﻟﺴﺎﺯ ﻣﻲﺗﻮﺍﻧﺪ ﺑﻪ ﺻﻮﺭﺕ ﻫﻤﺰﻣﺎﻥ ﺍﺯ ﻗﺎﺑﻠﻴﺘﻬﺎﻱ‬
‫‪ OSAN‬ﻭ ‪ UML‬ﺍﺳﺘﻔﺎﺩﻩ ﮐﻨﺪ‪.‬‬
‫ﺑﺎ ﺗﻮﺟﻪ ﺑﻪ ﭘﻴﭽﻴﺪﮔﻴﻬﺎﻳﻲ ﮐﻪ ﺩﺭ ﻣﺪﻝ ‪ OSAN‬ﺍﺿﺎﻓﻪ ﺷﺪﻩ ﺍﺳﺖ‪ ،‬ﺍﻳﻦ ﺍﺑﺰﺍﺭ ﺭﺍ ﻣﻲﺗﻮﺍﻥ ﺍﺯ ﺟﻬﺎﺕ ﻣﺨﺘﻠﻒ ﮔﺴﺘﺮﺵ ﺩﺍﺩ‪ .‬ﺍﻓﺰﻭﺩﻥ ﻗﺎﺑﻠﻴﺘﻲ‬
‫ﺑﺮﺍﻱ ﺣﻞ ﺣﺎﻟﺖ ﻧﺎﭘﺎﻳﺪﺍﺭ‪ ،‬ﺑﻬﻴﻨﻪﮐﺮﺩﻥ ﺍﻟﮕﻮﺭﻳﺘﻢﻫﺎﻱ ﺷﺒﻴﻪﺳﺎﺯﻱ ﻭ ﺣﻞ ﺗﺤﻠﻴﻠﻲ‪ ،‬ﺍﻓﺰﻭﺩﻥ ﻗﺎﺑﻠﻴﺘﻬﺎﻳﻲ ﺑﺮﺍﻱ ﻣﺪﻝ ﮐﺮﺩﻥ ﺳﻴﺴﺘﻢﻫﺎﻱ ﺑﺰﺭﮔﺘﺮ ﺑﺎ‬
‫ﻼ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﺷﻬﺎﻱ ﻣﺒﺘﻨﻲ ﺑﺮ ﺩﻳﺴﮏ‬
‫ﺗﻌﺪﺍﺩ ﺣﺎﻻﺕ ﺯﻳﺎﺩ ﺑﺎ ﺍﺳﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﺷﻬﺎﻱ ﮐﺎﻫﺶ ﺗﻌﺪﺍﺩ ﺣﺎﻟﺖ ﻭ ﻣﺪﻳﺮﻳﺖ ﺑﻬﺘﺮ ﻓﻀﺎﻱ ﺣﺎﻟﺖ ﻣﺜ ﹰ‬
‫ﺍﺯ ﺩﻳﮕﺮ ﺟﻨﺒﻪﻫﺎﻳﻲ ﺍﺳﺖ ﮐﻪ ﻣﻲﺗﻮﺍﻥ ﺩﺭ ﻣﻮﺭﺩ ﺁﻥ ﺗﺤﻘﻴﻖ ﮐﺮﺩ‪.‬‬
‫‪ .۷‬ﻣﺮﺍﺟﻊ‬
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