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Inventory Models under m¿- measure (2015)
DE PB NW RP
ISBN: 9783659741692 bzw. 3659741698, in Deutsch, LAP Lambert Academic Publishing Jul 2015, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, AHA-BUCH GmbH [51283250], Einbeck, Germany.
This item is printed on demand - Print on Demand Titel. Neuware - The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models. 96 pp. Englisch.
This item is printed on demand - Print on Demand Titel. Neuware - The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models. 96 pp. Englisch.
2
Inventory Models under m¿- measure (2015)
~EN PB NW
ISBN: 9783659741692 bzw. 3659741698, vermutlich in Englisch, LAP LAMBERT Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Lieferbar in 2 - 3 Tage.
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m¿- measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m¿-measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m¿- measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models. Taschenbuch, 06.07.2015.
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m¿- measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m¿-measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m¿- measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models. Taschenbuch, 06.07.2015.
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Symbolbild
Inventory Models Under M - Measure (Paperback) (2015)
DE PB NW RP
ISBN: 9783659741692 bzw. 3659741698, in Deutsch, LAP Lambert Academic Publishing, United States, Taschenbuch, neu, Nachdruck.
Von Händler/Antiquariat, The Book Depository EURO [60485773], London, United Kingdom.
Language: English Brand New Book ***** Print on Demand *****.The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
Language: English Brand New Book ***** Print on Demand *****.The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
4
Inventory Models under m¿- measure
~EN PB NW
ISBN: 9783659741692 bzw. 3659741698, vermutlich in Englisch, LAP Lambert Academic Publishing, Taschenbuch, neu.
Lieferung aus: Deutschland, Versandkostenfrei.
Inventory Models under m¿- measure: The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models. Englisch, Taschenbuch.
Inventory Models under m¿- measure: The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models. Englisch, Taschenbuch.
5
Symbolbild
Inventory Models under m¿- measure
DE NW
ISBN: 9783659741692 bzw. 3659741698, in Deutsch, neu.
Lieferung aus: Deutschland, zzgl. Versandkosten.
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
6
Inventory Models under m - measure
~EN NW AB
ISBN: 9783659741692 bzw. 3659741698, vermutlich in Englisch, neu, Hörbuch.
Lieferung aus: Niederlande, Lieferzeit: 5 Tage, zzgl. Versandkosten.
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
The classical EOQ/EPQ models are mainly developed in deterministic environment, wherein the demand and cost parameters are considered as constant. However, due to floating nature of marketplaces, the demand rate and cost parameters are not always fixed, and they often have slight deviation from one cycle to another. In most cases, the gauge of demand and cost parameters is often based on the experiences and subjective judgments of the inventory planner, and presented as linguistic expressions. Thus, to quantify non-stochastic uncertainty in inventory system, it is better to make use of possibility theory rather than probability theory. In this book some inventory models under m - measure which is linear combination of possibility and necessity measure, are discussed. The preliminary concept for fuzzy theory is explained in detail. Making use of m -measure, fuzzy CCP inventory models are constructed to determine optimistic and pessimistic returns. An objective function is optimized with some predefined degree of m - measure and accordingly the problem is transferred to an equivalent crisp problem and an analytical approach is proposed to resolve the reduced models.
7
Inventory Models under m - measure
EN NW
ISBN: 9783659741692 bzw. 3659741698, in Englisch, OmniScriptum GmbH & Co. KG, OmniScriptum GmbH & Co. KG, neu.
Lieferung aus: Vereinigte Staaten von Amerika, zzgl. Versandkosten, Free Shipping on eligible orders over $25, in-stock.
Soni Hardik, Paperback, English-language edition, Pub by OmniScriptum GmbH & Co. KG.
Soni Hardik, Paperback, English-language edition, Pub by OmniScriptum GmbH & Co. KG.
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