Using linear programming to analyze and optimize stochastic flow lines
Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discre...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Helber, Stefan [verfasserIn] |
|---|
| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2010 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC 2010 |
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| Übergeordnetes Werk: |
Enthalten in: Annals of operations research - Springer US, 1984, 182(2010), 1 vom: 11. Feb., Seite 193-211 |
|---|---|
| Übergeordnetes Werk: |
volume:182 ; year:2010 ; number:1 ; day:11 ; month:02 ; pages:193-211 |
| Links: |
|---|
| DOI / URN: |
10.1007/s10479-010-0692-3 |
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| Katalog-ID: |
OLC2111147249 |
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| 520 | |a Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. | ||
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10.1007/s10479-010-0692-3 doi (DE-627)OLC2111147249 (DE-He213)s10479-010-0692-3-p DE-627 ger DE-627 rakwb eng 004 VZ 3,2 ssgn Helber, Stefan verfasserin aut Using linear programming to analyze and optimize stochastic flow lines 2010 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC 2010 Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. Buffer Size Inventory Level Buffer Space Buffer Allocation Longe Line Schimmelpfeng, Katja aut Stolletz, Raik aut Lagershausen, Svenja aut Enthalten in Annals of operations research Springer US, 1984 182(2010), 1 vom: 11. Feb., Seite 193-211 (DE-627)12964370X (DE-600)252629-3 (DE-576)018141862 0254-5330 volume:182 year:2010 number:1 day:11 month:02 pages:193-211 https://doi.org/10.1007/s10479-010-0692-3 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-WIW SSG-OLC-MAT GBV_ILN_26 GBV_ILN_4029 GBV_ILN_4317 AR 182 2010 1 11 02 193-211 |
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Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. © Springer Science+Business Media, LLC 2010 |
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Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. © Springer Science+Business Media, LLC 2010 |
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Abstract This paper presents a linear programming approach to analyze and optimize flow lines with limited buffer capacities and stochastic processing times. The basic idea is to solve a huge but simple linear program that models an entire simulation run of a multi-stage production process in discrete time, to determine a production rate estimate. As our methodology is purely numerical, it offers the full modeling flexibility of stochastic simulation with respect to the probability distribution of processing times. However, unlike discrete-event simulation models, it also offers the optimization power of linear programming and hence allows us to solve buffer allocation problems. We show under which conditions our method works well by comparing its results to exact values for two-machine models and approximate simulation results for longer lines. © Springer Science+Business Media, LLC 2010 |
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