Fast Solution and Detection of Minimal Forecast Horizons in Dynamic Programs with a Single Indicator of the Future: Applications to Dynamic Lot-Sizing Models

In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e., information pertaining to a limited set of adjacent or nearby stages. This holds in particular for planning problems over time, where an optimal decision in a given perio...

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Vydané v:Management science Ročník 41; číslo 5; s. 874 - 893
Hlavní autori: Federgruen, Awi, Tzur, Michal
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Linthicum, MD INFORMS 01.05.1995
Institute for Operations Research and the Management Sciences
Edícia:Management Science
Predmet:
Algorithms
applications to lotsizing
Applied sciences
Carrying costs
Cost efficiency
Cost functions
Cost structure
Dynamic modeling
Dynamic programming
Dynamics
Exact sciences and technology
fast solution and detection
Forecasting
Forecasting models
Forecasts
Information
Integers
Inventory control, production control. Distribution
Management
Management science
Mathematical independent variables
Mathematical models
minimal forecast horizons
Operational research and scientific management
Operational research. Management science
Roots of functions
Studies
Algorithms
Material requirements planning
Production control
Supply
Lot sizing
Optimal planning
Fast algorithm
Dynamic model
Forecast model
Modeling
Method study
ISSN:0025-1909, 1526-5501
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Abstract In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e., information pertaining to a limited set of adjacent or nearby stages. This holds in particular for planning problems over time, where an optimal decision in a given period depends on information related to a limited future time horizon, a so-called forecast horizon, only. In this paper we identify a general class of dynamic programs in which an efficient forward algorithm can be designed to solve the problem and to identify minimal forecast horizons. Such a procedure specifies necessary and sufficient conditions for a stage to arise as a forecast horizon. This class of dynamic programs includes the single-item dynamic lot-sizing model with general concave costs, both with and without backlogging, to which special attention is given.
AbstractList In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e., information pertaining to a limited set of adjacent or nearby stages. This holds in particular for planning problems over time, where an optimal decision in a given period depends on information related to a limited future time horizon, a so-called forecast horizon, only. In this paper we identify a general class of dynamic programs in which an efficient forward algorithm can be designed to solve the problem and to identify minimal forecast horizons. Such a procedure specifies necessary and sufficient conditions for a stage to arise as a forecast horizon. This class of dynamic programs includes the single-item dynamic lot-sizing model with general concave costs, both with and without backlogging, to which special attention is given.
In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e., information pertaining to a limited set of adjacent or nearby stages. This holds in particular for planning problems over time, where an optimal decision in a given period depends on information related to a limited future time horizon, a so-called forecast horizon, only. A general class of dynamic programs in which an efficient forward algorithm can be designed to solve the problem and to identify minimal forecast horizons. Such a procedure specifies necessary and sufficient conditions for a stage to arise as a forecast horizon. This class of dynamic programs includes the single-item dynamic lot-sizing model with general concave costs, both with and without backlogging to which special attention is given.
In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e., information pertaining to a limited set of adjacent or nearby stages. This holds in particular for planning problems over time, where an optimal decision in a given period depends on information related to a limited future time horizon, a so-called forecast horizon, only. In this paper we identify a general class of dynamic programs in which an efficient forward algorithm can be designed to solve the problem and to identify minimal forecast horizons. Such a procedure specifies necessary and sufficient conditions for a stage to arise as a forecast horizon. This class of dynamic programs includes the single-item dynamic lot-sizing model with general concave costs, both with and without backlogging, to which special attention is given.
Author Tzur, Michal
Federgruen, Awi
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1995 INIST-CNRS
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Issue 5
Keywords Algorithms
Material requirements planning
Production control
Supply
Lot sizing
Optimal planning
Fast algorithm
Dynamic model
Forecast model
Modeling
Method study
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Snippet In most dynamic planning problems, one observes that an optimal decision at any given stage depends on limited information, i.e., information pertaining to a...
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SubjectTerms Algorithms
applications to lotsizing
Applied sciences
Carrying costs
Cost efficiency
Cost functions
Cost structure
Dynamic modeling
Dynamic programming
Dynamics
Exact sciences and technology
fast solution and detection
Forecasting
Forecasting models
Forecasts
Information
Integers
Inventory control, production control. Distribution
Management
Management science
Mathematical independent variables
Mathematical models
minimal forecast horizons
Operational research and scientific management
Operational research. Management science
Roots of functions
Studies
Title Fast Solution and Detection of Minimal Forecast Horizons in Dynamic Programs with a Single Indicator of the Future: Applications to Dynamic Lot-Sizing Models
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