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ÃÖ±Ù ¹°·ù ±â¾÷¿¡¼­´Â AS/RS(Automated Storage and Retrieval Systems)ÀÇ ÇÑ °¡Áö ÇüÅÂÀÎ SBS/RS(Shuttle-based Storage and Retrieval Systems)À» µµÀÔÇÏ¿© â°í ÀÚµ¿È­¸¦ ÁøÇàÇÏ°í ÀÖ´Ù. ƯÈ÷ tier-captive SBS/RS´Â ÇÑ ¼ÅƲÀÌ ÇϳªÀÇ ÃþÀ» Àü´ãÇÏ´Â ´ÙÃþ ÀÚµ¿ ÀúÀåâ°íÀÌ´Ù. ÀúÀå¼Ò¿¡¼­ ÇÑ Àåºñ¿¡ °úµµÇÑ ºÎÇÏ°¡ »ý±â¸é Àüü ÀúÀå ¼º´É¿¡ ¿µÇâÀ» ¹ÌÄ¡±â ¶§¹®¿¡, °¢ Àåºñ º° ¾÷¹« ºÎÇϸ¦ Á¶Á¤ÇÒ ÇÊ¿ä°¡ ÀÖ´Ù. ±×·¯¹Ç·Î Á¦Ç°ÀÇ ÀÔ°í ¹× Ãâ°í À§Ä¡¸¦ ÀûÀýÈ÷ °áÁ¤ÇÔÀ¸·Î½á, ¼³ºñÀÇ Á¤Ã¼¸¦ ÃÖ¼ÒÈ­ÇÏ´Â °ÍÀº ¸Å¿ì Áß¿äÇÑ ¹®Á¦ÀÌ´Ù. º» ¿¬±¸¿¡¼­´Â tier-captive ¹æ½ÄÀÇ SBS/RS¿¡¼­ Á¦Ç°ÀÇ ÀÔ°í À§Ä¡ °áÁ¤À» °­È­ÇнÀ ±â¹ÝÀÇ °èÃþÀû ¸ðÇü ±¸Á¶·Î Á¢±ÙÇÑ´Ù. µÎ °¡Áö Agent´Â ÀÔ°í½Ã°£À» ÃÖ¼ÒÈ­Çϱâ À§ÇÑ ¸ñÇ¥¸¦ °¡Áö°í, ÇнÀÀÇ º¸»óÀ¸·Î Çൿ¿¡ ´ëÇÑ ¼Ò¿ä½Ã°£À» È°¿ëÇÏ¿´´Ù. ±×¸®°í »óÀ§°èÃþÀÇ °áÁ¤¿¡¼­ ¼³ºñÀÇ ºÎÇÏ°¡ ±ÕµîÇÏ°Ô ¹èºÐÇÏÁö ¸øÇÏ´Â ¹®Á¦¸¦ º¸»ó ÇÔ¼öÀÇ Á¤±ÔÈ­¸¦ ÅëÇÏ¿© ÇØ°áÇÏ¿´´Ù. À̸¦ ÅëÇØ °¢ Á¦Ç°ÀÇ ÀÔ°í½Ã°£À» ÃÖ¼ÒÈ­ÇÏ´Â Á¤Ã¥ÀÌ Àüü ÀÔ°í ó¸®È¿À²À» Áõ°¡½ÃÅ´À» È®ÀÎÇÏ¿´À¸¸ç, ´Ü¼ø ½Ã°£¿¹ÃøÀÌ ¾Æ´Ñ ¼³ºñÀÇ ºÎÇϸ¦ ±ÕµîÇÏ°Ô ¹èºÐÇÔÀ¸·Î½á ¿ì¼öÇÑ ¼º´ÉÀ» µµÃâÇÏ¿´´Ù.

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In supply chain, most partners except the top level suppliers have inbound and outbound logistics. For example, toll manufacturing companies get unprocessed materials from a requesting company and send the processed materials back to the company after toll processing. Accordingly, those companies have inbound and outbound transportation costs in their total logistics costs. For many cases, the company may make the schedule of distributions by considering only the due delivery dates. However, the inbound and outbound transportation costs could significantly affect the total logistics costs. Thus, this paper considers the inbound and outbound transportation costs to find the optimal distribution plans. In addition, we have considered the inventory holding costs as well with transportation costs. From the experimental results, we have provided the optimal strategies for the distributions of replenishment as well as deliveries.

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½ÃÁ¶ ´Â 30¿© Á¾ÀÇ °¡Áý¿¡ ¼ö·ÏµÇ¾î ÀÖ°í 10¿© Á¾ÀÌ ³Ñ´Â ¹®Áý·ù ¹®Çå¿¡ ½ÃÁ¶ ¿ø¹® ÇÑ¿ª½Ã, ÀÛ°¡, âÀÛ ¹è°æ µÕ¿¡ °üÇÑ ±â·ÏÀÌ µé¾îÀÖ´Ù. ¿À·§µ¿¾È Á¶½ÄÀÇ ÀÛÇ°À¸·Î ¾Ë·ÁÁ® ¿Â ÀÌ ½ÃÁ¶ÀÇ ÀÛ°¡°¡ ±èÀÀÁ¤À̶ó´Â ÁÖÀåÀÌ Á¦±â¾úÀ¸³ª ÀϺΠÇмú³íÀú¿Í ´ëºÎºÐÀÇ ±³¾çµµ¼­ ¹× ÇнÀÂü°í¼­·ù¿¡¼­´Â Á¶½ÄÀÇ ÀÛÇ°À¸·Î ´Ù·ç°í ÀÖ´Ù. ƯÈ÷ ÀÛ°¡ ½Ãºñ°¡ ³íÀǵǰí ÀÖ´Ù´Â ÀÌÀ¯·Î ±³°ú¼­¿¡¼­ »èÁ¦µÇ¾ú´Ù. °¡Áý·ùÀÇ ´ë´Ù¼ö¿¡ ÀÌ ½ÃÁ¶ÀÇ ÀÛ°¡°¡ Á¶½ÄÀ¸·Î Ç¥±âµÇ¾î ÀÖ°í ÀϺο¡ ¾çÀÀÁ¤À¸·Î ³ª¿ÀÁö¸¸ À̸¦ °´°üÀû ÀÚ·á·Î »ï±â´Â ¾î·Æ´Ù. ¹®Áý·ù ¹®Çå¿¡´Â ±æÀç(ÑÎî¢) ±èÀÎÈÄ(ÑÑìçý§), À̸ù±Ô(ì°ÙÓÐ¥), ±èÀÀÁ¤(ÑÑëëð£), ±è·É(ÑÑÖ¼) µîÀÌ ÀÛ°¡·Î ³ª¿À´Âµ¥, Àú¸¶´Ù ÇØ´ç Àι°ÀÇ »ý¾Ö¿Í âÀÛ ¹è°æ µîÀ» Á¦½ÃÇÏ¸ç ¼­·Î ´Ù¸¥ ÁÖÀåÀ» ³»¼¼¿ì°í À־ È¥¶õ½º·´´Ù. °¡Áý·ùÀÇ °æ¿ì´Â ¿øÀÛÀÚ ±èÀÀÁ¤ÀÌ ¾çÀÀÁ¤À¸·Î À߸ø ±â·ÏµÇ°í. ÇàÀû°ú ¸í¼ºÀÌ (»ïµ¿¿¡ º£¿Ê ÀÔ°í)ÀÇ ³»¿ë°ú ¹è°æ¿¡ °¡±î¿î Á¶½ÄÀ¸·Î ¹Ù²î¾î Àü½ÂµÈ °ÍÀ̶ó ÇÒ ¼ö ÀÖ´Ù. ¹®Áý·ù ¹®ÇåÀÇ °æ¿ì´Â ´ë»ó Àι°ÀÇ »ý¾Ö¿Í ÇàÀû, ÀÏÈ­¿Í ¸í¼º, ½Ã¹® âÀÛ, ½Ã´ë ¹è°æ°ú ¿ª»çÀû »ç°Ç µîÀÌ (»ïµ¿¿¡ º£¿Ê ÀÔ°í)ÀÇ ³»¿ë ¹× ¹è°æ°ú ½±°Ô °áºÎµÉ ¼ö ÀÖ¾ú±â ¶§¹®¿¡ ±× ÇàÀûÀ̳ª ÀÏÈ­°¡ Àü½ÂµÇ¸é¼­ ÀÌ ½ÃÁ¶ÀÇ ÀÛ°¡·Î ¾Ë·ÁÁö°Ô µÇ¾úÀ» °¡´É¼ºÀÌ Å©´Ù. ƯÈ÷ ÀÌ ½ÃÁ¶ÀÇ ÀÛ°¡·Î ³»¼¼¿öÁø Àι°ÀÇ °¡¹®ÀÇ ÈļÕ, Ç⸮ ÈĹè, ÇÐÆijª Á¤ÆÄÀÇ ÈÄÁø µîÀÌ ¼±Á¶³ª ¼±¹èÀÇ ÀýÀǸ¦ µå·¯³»±â À§ÇØ Áý¾ÈÀ̳ª Ç⸮, ÇÐÆÄ µî¿¡ Àü½ÂµÇ´ø (»ïµ¿¿¡ º£¿Ê ÀÔ°í) âÀÛ¼³À» ƯÁ¤ ±â·ÏÀ¸·Î Á¤¸®ÇÏ¿´´Ù°í ÇÒ ¼ö ÀÖ´Ù. °á±¹ °ü·ÃµÈ ¹®ÇåÀڷḦ ±¤¹üÀ§ÇÏ°Ô °ËÅäÇÑ °á°ú ÀÌ ½ÃÁ¶´Â ±èÀÀÁ¤ÀÌ ¸íÁ¾ÀÇ ½ÂÇϽÿ¡ Áö¾ú´Ù´Â »ç½ÇÀ» ±¸Ã¼Àû ÀڷḦ ÅëÇØ È®ÀÎÇÒ ¼ö ÀÖ¾ú´Ù. ¾Æ¿ï·¯ (»ïµ¿¿¡ º£¿Ê ÀÔ°í)ÀÇ ¿ø¹® Ç¥±â âÀÛ ¹è°æ ¾îÈÖ Ç®ÀÌ µî¿¡ ´ëÇØ Á¾ÇÕÀûÀ¸·Î »ìÆ캸¾Ò´Ù. ½ÃÁ¶ (»ïµ¿¿¡ º£¿Ê ÀÔ°í)¿Í °ü·ÃµÈ ´Ù¾çÇÑ ¹®ÇåÀÚ·áÀÇ Àü½ÂÀº ÇØ´ç ½ÃÁ¶ »Ó ¾Æ´Ï¶ó ½ÃÁ¶¹®ÇÐÀÇ ¼ö¿ë°ú Àü½Â¾ç»óÀ» º¸¿©ÁÖ´Â ÀÚ·á·Î¼­ÀÇ Áß¿äÇÑ Àǹ̸¦ °®´Â´Ù°í ÇÒ ¼ö ÀÖ´Ù

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A cross docking operation involves multiple inbound trucks that deliver items from suppliers to a distribution center and multiple outbound trucks that ship items from the distribution center to customers. Based on customer demands, an inbound truck may have its items transferred to multiple outbound trucks. Similarly, an outbound truck can receive its consignments from multiple inbound trucks. The objective of this study is to find the best truck spotting sequence for both inbound and outbound trucks in order to minimize total operation time of the cross docking system under the condition that multiple visits to the dock by a truck to unload or load its consignments is allowed. The allocations of the items from inbound trucks to outbound trucks are determined simultaneously with the spotting sequences of both the inbound and outbound trucks.

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ÀÇ·ù ÀÎÅÍ³Ý ¼îÇθôµéÀº ÆǸŠ»óÇ°ÀÇ Æ÷Àå°ú ¹è¼ÛÀ» À§ÇÑ »óÇ° â°í¸¦ ¿î¿µÇÏ°í ÀÖ´Ù. »óÇ°µéÀº µ¿ÀÏ ºê·£µå³¢¸® »óÀÚ¿¡ ´ã±ä ÈÄ ¼±¹Ý¿¡ ÀÏ·Ä·Î º¸°üµÈ´Ù. »óÇ°ÀÇ ¹ÝÃâ ¹× »óÇ° °ü¸®ÀÇ ÆíÀÇ»ó, »óÀÚµéÀº µ¿ÀÏ ºê·£µå³¢¸® ¹­¿©Á® ¼±¹Ý¿¡ Áø¿­µÇ¾î ÀÖ¾î¾ß ÇÑ´Ù. µû¶ó¼­, »õ·Î¿î »óÇ°µéÀÌ ÀÔ°íµÉ °æ¿ì »óÇ°µéÀº µ¿ÀÏ ºê·£µå³¢¸® »óÀÚ¿¡ ´ã±ä ÈÄ ¼±¹Ý À§¿¡ ÀÖ´Â ±âÁ¸ »óÀÚµé Áß¿¡¼­ µ¿ÀÏ ºê·£µåÀÇ »óÀÚ ¿·¿¡ ¹èÄ¡µÇ¾î¾ß ÇÑ´Ù. ±×·±µ¥, ¼±¹Ý À§ÀÇ ºó °÷ÀÌ »õ·Î ÀÔ°íµÇ´Â »óÀÚ¸¦ ³ÖÀ» ¼ö ÀÖÀ» ¸¸Å­ ÃæºÐÇÏÁö ¾Ê´Ù¸é, ¿·ÀÇ ´Ù¸¥ ºê·£µåÀÇ »óÀÚµéÀ» ¿·À¸·Î ¹Ð¾î¼­ °ø°£À» È®º¸ÇÑ ÈÄ »õ·Î¿î »óÀÚ¸¦ ¹èÄ¡ÇÔÀ¸·Î½á µ¿ÀÏ ºê·£µå »óÀÚ³¢¸® ºÙ¾îÀÖµµ·Ï ÇØ¾ß ÇÑ´Ù. ¿ì¸®ÀÇ ¹®Á¦´Â ÀÌ¿Í °°ÀÌ »õ·Î¿î »óÇ°À» ÀÔ°íÇÒ ¶§ µ¿ÀÏ ºê·£µåÀÇ »óÀڵ鳢¸® ºÙ¾î ÀÖµµ·Ï Çϸ鼭 ´Ù¸¥ ºê·£µåÀÇ »óÀÚ¸¦ ¿·À¸·Î ¿Å±æ °æ¿ì, ±× Ƚ¼ö¸¦ ÃÖ¼ÒÈ­ÇÏ´Â °ÍÀÌ´Ù. ÀÌ ¹®Á¦ÀÇ ÃÖÀûÇظ¦ ±¸Çϱâ À§Çؼ­ ¿ì¸®´Â ÀÌ ¹®Á¦¸¦ ¿ì¼± Á¤¼ö°èȹ¹ýÀ¸·Î ¸ðÇüÈ­ÇÏ¿´´Ù. ±×·±µ¥, Á¤¼ö°èȹ¹ý ¹®Á¦´Â ºÐ±âÇÑÁ¤¹ý(Branch and Bound) ±â¹ýÀ¸·Î ÇØ°áÇÏ¿©¾ß Çϳª, ±× °æ¿ì ¹®Á¦ÇØ°á ½Ã°£ÀÌ ³Ê¹« ¿À·¡ °É¸®´Â ¹®Á¦°¡ ¹ß»ýÇÑ´Ù. µû¶ó¼­, º» ¿¬±¸¿¡¼­´Â À§ Àç¹èÄ¡ ¹®Á¦¸¦ ÇÒ´ç ¹®Á¦(Assignment Problem)·Î ¿ÏÈ­ÇÏ¿© ¸ðÇüÈ­ÇÔÀ¸·Î½á ¸¸Á·ÇÒ¸¸ÇÑ ÁØÃÖÀûÇظ¦ ±¸ÇÏ´Â ¹æ¹ý·ÐÀ» Á¦½ÃÇÏ°í, ½ÇÇè¿¡ ÀÇÇÏ¿© ±× Ÿ´ç¼ºÀ» °ËÅäÇÏ¿´´Ù. ¶ÇÇÑ, ÀÌ ¹æ¹ý·Ð ÇÏ¿¡¼­ ½ÇÁ¦ ÀÇ·ù ÀÎÅÍ³Ý ¼îÇθôÀÇ ÄÄÇ»Æà ȯ°æÀ» °í·ÁÇÒ ¶§ ÇØ°á °¡´ÉÇÑ ¹®Á¦ÀÇ ÃÖ´ë Å©±â¸¦ µµÃâÇÏ°í, ±× Å©±â À̳»¿¡¼­ ÀÔ°í °èȹÀ» »ý¼ºÇÏ´Â ½Ã½ºÅÛÀ» ±¸ÇöÇÏ¿´´Ù.

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This thesis addresses the feeding scheduling problem in a Naphtha Cracking Center. To tackle this problem, two mathematical programming models and one heuristic algorithm are suggested. Chapter I addresses a naphtha feeding problem for Naphtha Cracking Center (NCC). The rapid increase of petroleum prices compelled to petro-chemical industries to figure out ways to remove any potential redundancies in and out of their network. The increasing attention on integrating activities that have been addressed separately is in line with this trend. The naphtha feeding problem involves two key operations: delivering naphtha from refineries to NCC and blending naphtha in storage tanks before feeding it to NCC. While the first is concerned with selection sources and scheduling the loading and unloading of naphtha, the latter involves the transfer of the naphtha from storage tanks to a charging tank. The both issues are simultaneously considered by transforming them into a single mixed integer linear programming problem of minimizing the cost function of naphtha prices, shipping expenses, and unloading costs, etc. A numerical example of a real industrial case is presented to illustrate the applicability of the proposed mathematical model. In Chapter II, we propose a decision-supporting framework for a feeding problem in the petrochemical industry. The problem is concerned with delivering materials from suppliers to plants, unloading and storing in storage tanks, and mixing the materials before directly feeding into main processes. Most of the previous works in the literature have addressed these concepts, based on the assumption that the delivery of raw materials is given and fixed. From a joint investigation with industry partners, we have determined that the purchase of feedstock and its delivery also are critical issues in the feed scheduling problem of real-world plants. Thereafter, we takes into account previously addressed issues separately and simultaneously, to increase the overall efficiency. The corresponding decision-making problem is mathematically transformed to a mixed-integer nonlinear programming (MINLP) problem. The solution of the problem is computed using the iterative framework between that of a relaxed mixed-integer linear programming (MILP) problem and that of a nonlinear programming (NLP) problem, to prevent compositional discrepancy. An industry-coworked example of the naphtha case is presented, to illustrate the applicability of the proposed framework. In Chapter III, a heuristic method is developed to address the unloading and blending scheduling problem in NCC. This method is based on the rules which are applied to mange tanks in NCC. By applying this method, the massive scheduling problem is successfully solved in a short time compared to the mathematical programming method. By simulating the schedule solution generated with the proposed method, site-operators can estimate the long-term plans (vessel arrival events) predetermined by the head quarter. If the simulation shows the bad results, site-operators can require that the predetermined long-term plans should be modified in order to maintain NCC at stable and efficient condition. This is the additional advantage to apply the proposed method.

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