In distribution logistics low-but-bulk quantity orders are being replaced by multiple-but-diminutive orders, which need to be executed in very tight time schedules. In manufacturing, there is a marked repositioning to lesser lot-sizes, point-of-use delivery, and cycle time condensation. The order routing challenge (ORC) concerns the evaluation of how to knot orders and then to assign them to order-loaders. This research argues that efficiency can increase in the order loading process in such environments by catering to a group of orders instead of exclusive orders. The essential issue is, hence, to determine the set of orders the order-loader should serve in one trip to minimize the mean throughput time of a random order. This research focus on the derivation of a simple but efficient algorithm for the establishment of an optimal load batch size for order-loaders in a typical Twin-block warehouse. Moments are defined applied to approximate the waiting duration of a random order based on the analogous batch service queuing structure. The optimal order batch size is then established in a straightforward pattern.
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Dr V. Krishna Mohan is a Professor in DCMS, Andhra University, Visakhapatnam with 26 years experience and has published 5 books, 86 research papers and guided 62 doctoral students. He is serving as Registrar (on deputation) of Dr B.R.Ambedkar University, Srikakulam,AP, India on deputation and research interests are SCM and Marketing Econometrics.
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Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In distribution logistics low-but-bulk quantity orders are being replaced by multiple-but-diminutive orders, which need to be executed in very tight time schedules. In manufacturing, there is a marked repositioning to lesser lot-sizes, point-of-use delivery, and cycle time condensation. The order routing challenge (ORC) concerns the evaluation of how to knot orders and then to assign them to order-loaders. This research argues that efficiency can increase in the order loading process in such environments by catering to a group of orders instead of exclusive orders. The essential issue is, hence, to determine the set of orders the order-loader should serve in one trip to minimize the mean throughput time of a random order. This research focus on the derivation of a simple but efficient algorithm for the establishment of an optimal load batch size for order-loaders in a typical Twin-block warehouse. Moments are defined applied to approximate the waiting duration of a random order based on the analogous batch service queuing structure. The optimal order batch size is then established in a straightforward pattern. 60 pp. Englisch. Seller Inventory # 9783659237348
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Vaddadi Krishna MohanDr V. Krishna Mohan is a Professor in DCMS, Andhra University, Visakhapatnam with 26 years experience and has published 5 books, 86 research papers and guided 62 doctoral students. He is serving as Registrar (on . Seller Inventory # 5142041
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Taschenbuch. Condition: Neu. Neuware -In distribution logistics low-but-bulk quantity orders are being replaced by multiple-but-diminutive orders, which need to be executed in very tight time schedules. In manufacturing, there is a marked repositioning to lesser lot-sizes, point-of-use delivery, and cycle time condensation. The order routing challenge (ORC) concerns the evaluation of how to knot orders and then to assign them to order-loaders. This research argues that efficiency can increase in the order loading process in such environments by catering to a group of orders instead of exclusive orders. The essential issue is, hence, to determine the set of orders the order-loader should serve in one trip to minimize the mean throughput time of a random order. This research focus on the derivation of a simple but efficient algorithm for the establishment of an optimal load batch size for order-loaders in a typical Twin-block warehouse. Moments are defined applied to approximate the waiting duration of a random order based on the analogous batch service queuing structure. The optimal order batch size is then established in a straightforward pattern.Books on Demand GmbH, Überseering 33, 22297 Hamburg 60 pp. Englisch. Seller Inventory # 9783659237348
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In distribution logistics low-but-bulk quantity orders are being replaced by multiple-but-diminutive orders, which need to be executed in very tight time schedules. In manufacturing, there is a marked repositioning to lesser lot-sizes, point-of-use delivery, and cycle time condensation. The order routing challenge (ORC) concerns the evaluation of how to knot orders and then to assign them to order-loaders. This research argues that efficiency can increase in the order loading process in such environments by catering to a group of orders instead of exclusive orders. The essential issue is, hence, to determine the set of orders the order-loader should serve in one trip to minimize the mean throughput time of a random order. This research focus on the derivation of a simple but efficient algorithm for the establishment of an optimal load batch size for order-loaders in a typical Twin-block warehouse. Moments are defined applied to approximate the waiting duration of a random order based on the analogous batch service queuing structure. The optimal order batch size is then established in a straightforward pattern. Seller Inventory # 9783659237348
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Taschenbuch. Condition: Neu. Order Routing Challenge in Warehouse Operations - An Empirical Study | A Case Study with reference to the Perishable Goods Segment | Krishna Mohan Vaddadi (u. a.) | Taschenbuch | 60 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783659237348 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu. Seller Inventory # 106296609
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