Queueing-Inventory System with Two Commodities
Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type o...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Benny, Binitha [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© The Indian Society for Probability and Statistics (ISPS) 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of the Indian Society for Probability and Statistics - [New Delhi] : Springer India, 2015, 19(2018), 2 vom: 10. Sept., Seite 437-454 |
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| Übergeordnetes Werk: |
volume:19 ; year:2018 ; number:2 ; day:10 ; month:09 ; pages:437-454 |
| Links: |
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| DOI / URN: |
10.1007/s41096-018-0052-1 |
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| Katalog-ID: |
SPR038243504 |
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| 520 | |a Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. | ||
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| 700 | 1 | |a Chakravarthy, S. R. |4 aut | |
| 700 | 1 | |a Krishnamoorthy, A. |4 aut | |
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10.1007/s41096-018-0052-1 doi (DE-627)SPR038243504 (SPR)s41096-018-0052-1-e DE-627 ger DE-627 rakwb eng Benny, Binitha verfasserin aut Queueing-Inventory System with Two Commodities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Society for Probability and Statistics (ISPS) 2018 Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. Queueing-inventory (dpeaa)DE-He213 -type policy (dpeaa)DE-He213 GI/M/1-type queues (dpeaa)DE-He213 Algorithmic probability (dpeaa)DE-He213 Chakravarthy, S. R. aut Krishnamoorthy, A. aut Enthalten in Journal of the Indian Society for Probability and Statistics [New Delhi] : Springer India, 2015 19(2018), 2 vom: 10. Sept., Seite 437-454 (DE-627)844386308 (DE-600)2843083-9 2364-9569 nnns volume:19 year:2018 number:2 day:10 month:09 pages:437-454 https://dx.doi.org/10.1007/s41096-018-0052-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2018 2 10 09 437-454 |
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10.1007/s41096-018-0052-1 doi (DE-627)SPR038243504 (SPR)s41096-018-0052-1-e DE-627 ger DE-627 rakwb eng Benny, Binitha verfasserin aut Queueing-Inventory System with Two Commodities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Society for Probability and Statistics (ISPS) 2018 Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. Queueing-inventory (dpeaa)DE-He213 -type policy (dpeaa)DE-He213 GI/M/1-type queues (dpeaa)DE-He213 Algorithmic probability (dpeaa)DE-He213 Chakravarthy, S. R. aut Krishnamoorthy, A. aut Enthalten in Journal of the Indian Society for Probability and Statistics [New Delhi] : Springer India, 2015 19(2018), 2 vom: 10. Sept., Seite 437-454 (DE-627)844386308 (DE-600)2843083-9 2364-9569 nnns volume:19 year:2018 number:2 day:10 month:09 pages:437-454 https://dx.doi.org/10.1007/s41096-018-0052-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2018 2 10 09 437-454 |
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10.1007/s41096-018-0052-1 doi (DE-627)SPR038243504 (SPR)s41096-018-0052-1-e DE-627 ger DE-627 rakwb eng Benny, Binitha verfasserin aut Queueing-Inventory System with Two Commodities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Society for Probability and Statistics (ISPS) 2018 Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. Queueing-inventory (dpeaa)DE-He213 -type policy (dpeaa)DE-He213 GI/M/1-type queues (dpeaa)DE-He213 Algorithmic probability (dpeaa)DE-He213 Chakravarthy, S. R. aut Krishnamoorthy, A. aut Enthalten in Journal of the Indian Society for Probability and Statistics [New Delhi] : Springer India, 2015 19(2018), 2 vom: 10. Sept., Seite 437-454 (DE-627)844386308 (DE-600)2843083-9 2364-9569 nnns volume:19 year:2018 number:2 day:10 month:09 pages:437-454 https://dx.doi.org/10.1007/s41096-018-0052-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2018 2 10 09 437-454 |
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10.1007/s41096-018-0052-1 doi (DE-627)SPR038243504 (SPR)s41096-018-0052-1-e DE-627 ger DE-627 rakwb eng Benny, Binitha verfasserin aut Queueing-Inventory System with Two Commodities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Society for Probability and Statistics (ISPS) 2018 Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. Queueing-inventory (dpeaa)DE-He213 -type policy (dpeaa)DE-He213 GI/M/1-type queues (dpeaa)DE-He213 Algorithmic probability (dpeaa)DE-He213 Chakravarthy, S. R. aut Krishnamoorthy, A. aut Enthalten in Journal of the Indian Society for Probability and Statistics [New Delhi] : Springer India, 2015 19(2018), 2 vom: 10. Sept., Seite 437-454 (DE-627)844386308 (DE-600)2843083-9 2364-9569 nnns volume:19 year:2018 number:2 day:10 month:09 pages:437-454 https://dx.doi.org/10.1007/s41096-018-0052-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2018 2 10 09 437-454 |
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10.1007/s41096-018-0052-1 doi (DE-627)SPR038243504 (SPR)s41096-018-0052-1-e DE-627 ger DE-627 rakwb eng Benny, Binitha verfasserin aut Queueing-Inventory System with Two Commodities 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Indian Society for Probability and Statistics (ISPS) 2018 Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. Queueing-inventory (dpeaa)DE-He213 -type policy (dpeaa)DE-He213 GI/M/1-type queues (dpeaa)DE-He213 Algorithmic probability (dpeaa)DE-He213 Chakravarthy, S. R. aut Krishnamoorthy, A. aut Enthalten in Journal of the Indian Society for Probability and Statistics [New Delhi] : Springer India, 2015 19(2018), 2 vom: 10. Sept., Seite 437-454 (DE-627)844386308 (DE-600)2843083-9 2364-9569 nnns volume:19 year:2018 number:2 day:10 month:09 pages:437-454 https://dx.doi.org/10.1007/s41096-018-0052-1 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2018 2 10 09 437-454 |
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Benny, Binitha |
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Queueing-Inventory System with Two Commodities |
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queueing-inventory system with two commodities |
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Queueing-Inventory System with Two Commodities |
| abstract |
Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. © The Indian Society for Probability and Statistics (ISPS) 2018 |
| abstractGer |
Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. © The Indian Society for Probability and Statistics (ISPS) 2018 |
| abstract_unstemmed |
Abstract A two-commodity inventory system with a single server is considered in this paper. We assume that the capacity of the buffers (to store the two types of commodities) to be finite. Customers (or demands) arrive according to a Poisson Process and the requirement for either type or both type of commodities are modelled using certain probabilities. Customers are lost when their demands are not met due to shortage only at the time of service offerings as opposed to getting lost when the inventory level is zero at the time of arrival. This is to allow the possibility of inventory being replenished prior to offering services to those who arrive when the inventory level is zero. A customer’s demand for both items may be met with only one item should a situation in which there is only one type of inventory is positive and the other is zero at the time of initiating a service occurs. The processing time for meeting the demands are random and modelled using exponential distribution with parameters depending on the type of demands being processed. We adopt (s, S)-type replenishment policy which depends on the type of commodity. Assuming the lead times to be exponentially distributed with parameters depending on the type of commodity, we employ matrix-analytic methods to study the queueing inventory system and report interesting results including an optimization dealing with various costs. © The Indian Society for Probability and Statistics (ISPS) 2018 |
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Queueing-Inventory System with Two Commodities |
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Chakravarthy, S. R. Krishnamoorthy, A. |
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