Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy
Management of electric equipment has a direct impact on companies’ performance and profitability. Considering the critical role that electric power materials play in supporting maintenance operations and preventing equipment failure, it is essential to maintain an inventory to a reasonable level. Ho...
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doaj-63a13536ae1942648633b1ee2ffdc2dd2020-11-25T00:26:48ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472018-01-01201810.1155/2018/60853426085342Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) PolicyAiping Jiang0Kwok Leung Tam1Yingzi Bao2Jialing Lu3SHU-UTS SILC Business School, Shanghai University, 20 Chengzhong Road, Jiading District, Shanghai 201899, ChinaSchool of Mathematics and Statistics, UNSW, Sydney, NSW 2052, AustraliaSHU-UTS SILC Business School, Shanghai University, 20 Chengzhong Road, Jiading District, Shanghai 201899, ChinaSHU-UTS SILC Business School, Shanghai University, 20 Chengzhong Road, Jiading District, Shanghai 201899, ChinaManagement of electric equipment has a direct impact on companies’ performance and profitability. Considering the critical role that electric power materials play in supporting maintenance operations and preventing equipment failure, it is essential to maintain an inventory to a reasonable level. However, a majority of these electric power materials exhibit an intermittent demand pattern characterized by random arrivals interspersed with time periods with no demand at all. These characteristics cause additional difficulty for companies in managing these electric power material inventories. In response to the above problem, this paper, based on the electric power material demand data of Shanghai Electric Power Company, develops a new method to determine the optimal order quantity Q⁎ in a cost-oriented periodic review (T,Q) system, whereby unsatisfied demands are backordered and demand follows a compound Erlang distribution. Q⁎corresponds to the value of Q that gives the minimum expected total inventory holding and backordering cost. Subsequently, an empirical investigation is conducted to compare this method with the Newsvendor model. Results verify its superiority in cost savings. Ultimately, considering the complicated calculation and low efficiency of that algorithm, this paper proposes an approximation and a heuristic algorithm which have a higher level of utility in a real industrial context. The approximation algorithm simplifies the calculation process by reducing iterative times while the heuristic algorithm achieves it by generalizing the relationship between the optimal order quantity Q⁎ and mean demand interarrival rate λ.http://dx.doi.org/10.1155/2018/6085342 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Aiping Jiang Kwok Leung Tam Yingzi Bao Jialing Lu |
spellingShingle |
Aiping Jiang Kwok Leung Tam Yingzi Bao Jialing Lu Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy Mathematical Problems in Engineering |
author_facet |
Aiping Jiang Kwok Leung Tam Yingzi Bao Jialing Lu |
author_sort |
Aiping Jiang |
title |
Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy |
title_short |
Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy |
title_full |
Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy |
title_fullStr |
Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy |
title_full_unstemmed |
Determining the Optimal Order Quantity with Compound Erlang Demand under (T,Q) Policy |
title_sort |
determining the optimal order quantity with compound erlang demand under (t,q) policy |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2018-01-01 |
description |
Management of electric equipment has a direct impact on companies’ performance and profitability. Considering the critical role that electric power materials play in supporting maintenance operations and preventing equipment failure, it is essential to maintain an inventory to a reasonable level. However, a majority of these electric power materials exhibit an intermittent demand pattern characterized by random arrivals interspersed with time periods with no demand at all. These characteristics cause additional difficulty for companies in managing these electric power material inventories. In response to the above problem, this paper, based on the electric power material demand data of Shanghai Electric Power Company, develops a new method to determine the optimal order quantity Q⁎ in a cost-oriented periodic review (T,Q) system, whereby unsatisfied demands are backordered and demand follows a compound Erlang distribution. Q⁎corresponds to the value of Q that gives the minimum expected total inventory holding and backordering cost. Subsequently, an empirical investigation is conducted to compare this method with the Newsvendor model. Results verify its superiority in cost savings. Ultimately, considering the complicated calculation and low efficiency of that algorithm, this paper proposes an approximation and a heuristic algorithm which have a higher level of utility in a real industrial context. The approximation algorithm simplifies the calculation process by reducing iterative times while the heuristic algorithm achieves it by generalizing the relationship between the optimal order quantity Q⁎ and mean demand interarrival rate λ. |
url |
http://dx.doi.org/10.1155/2018/6085342 |
work_keys_str_mv |
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