Separable programming for aggregate production planning: A high-order cost case

Many production managers are faced with the problem of planning production, inventory and work-force under the constraint of limited resources to meet a seasonal demand. Considerable research has been done on this planning problem and various planning models have been introduced. In those cases wher...

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Main Author: J. T. Meij
Format: Article
Language:English
Published: AOSIS 1982-03-01
Series:South African Journal of Business Management
Online Access:https://sajbm.org/index.php/sajbm/article/view/1166
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spelling doaj-a1f32572a1524079954a36a5048a40962021-02-02T06:07:15ZengAOSISSouth African Journal of Business Management2078-55852078-59761982-03-01131182210.4102/sajbm.v13i1.1166881Separable programming for aggregate production planning: A high-order cost caseJ. T. Meij0Department of Mechanical Engineering, University of StellenboschMany production managers are faced with the problem of planning production, inventory and work-force under the constraint of limited resources to meet a seasonal demand. Considerable research has been done on this planning problem and various planning models have been introduced. In those cases where linearity of the cost functions of an undertaking may reasonably be assumed, an ordinary linear programming model suffices. In many cases, however, this simple linear approach to certain essentially non-linear cost functions is unacceptable owing to the gross approximation made. Separable programming (SEP) is introduced as a solution methodology to this aggregate production planning problem in a complex, high-order cost structure case. The cost structure was used by Goodman for the application of goal programming (GP) in this field. The Goodman GP model makes provision for positive or negative slack for the production level, work-force level and inventory level with penalty costs for these slack-deviations. Goodman also made use of a 'sectioning search' model for this high-order cost case to serve as a measure for his GP model. A comparison is made between the results of these three approaches. SEP offered an improvement of more than 4% in total cost in comparison with the sectioning search model, and performs 26% better than the GP model.https://sajbm.org/index.php/sajbm/article/view/1166
collection DOAJ
language English
format Article
sources DOAJ
author J. T. Meij
spellingShingle J. T. Meij
Separable programming for aggregate production planning: A high-order cost case
South African Journal of Business Management
author_facet J. T. Meij
author_sort J. T. Meij
title Separable programming for aggregate production planning: A high-order cost case
title_short Separable programming for aggregate production planning: A high-order cost case
title_full Separable programming for aggregate production planning: A high-order cost case
title_fullStr Separable programming for aggregate production planning: A high-order cost case
title_full_unstemmed Separable programming for aggregate production planning: A high-order cost case
title_sort separable programming for aggregate production planning: a high-order cost case
publisher AOSIS
series South African Journal of Business Management
issn 2078-5585
2078-5976
publishDate 1982-03-01
description Many production managers are faced with the problem of planning production, inventory and work-force under the constraint of limited resources to meet a seasonal demand. Considerable research has been done on this planning problem and various planning models have been introduced. In those cases where linearity of the cost functions of an undertaking may reasonably be assumed, an ordinary linear programming model suffices. In many cases, however, this simple linear approach to certain essentially non-linear cost functions is unacceptable owing to the gross approximation made. Separable programming (SEP) is introduced as a solution methodology to this aggregate production planning problem in a complex, high-order cost structure case. The cost structure was used by Goodman for the application of goal programming (GP) in this field. The Goodman GP model makes provision for positive or negative slack for the production level, work-force level and inventory level with penalty costs for these slack-deviations. Goodman also made use of a 'sectioning search' model for this high-order cost case to serve as a measure for his GP model. A comparison is made between the results of these three approaches. SEP offered an improvement of more than 4% in total cost in comparison with the sectioning search model, and performs 26% better than the GP model.
url https://sajbm.org/index.php/sajbm/article/view/1166
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