Improved inventory models with service level and lead time

Chu, Peter C.; Yang, Kuang-Yao; Chen, Patrick S.

doi:10.1016/j.cor.2003.07.001

Cited by 47 publications

(14 citation statements)

References 11 publications

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“…2c If A i ≥ A 0 and π xi < π 0 , then for given L i , set A * * = A 0 and evaluate new ( Q i , k i , π xi ) from (11), (12), and (13) by the same procedure as described in step 1. If π xi < π 0 , then the near optimal solution is (Q * * , K * * , π x * * , A * * ) = ( Q i , k i , π 0 , A i ), and go to step 4.…”

Section: Solution Algorithmmentioning

confidence: 99%

“…1a Set π xi1 = π 0 , A i1 = A 0 and k i1 = 0 1b Substitute π xi1 , A i1 and k i1 into (11), evaluate Q i1 . 1c Utilizing Q i1 , obtain k i1 from (12). 1d Utilizing Q i1 , determine π xi2 and A i2 from (13) and (14), respectively.…”

Section: Solution Algorithmmentioning

confidence: 99%

“…In their model, they divided lead time into three main components: setup time, productive time, and non-production time. [12] developed a mixed inventory problem with both lead time and order quantity as decision variables. Further, [13] considered a min-max distribution free procedure for an integrated inventory model with defective goods and stochastic lead time demand.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Distribution-Free Model with Variable Setup Cost, Backorder Price Discount and Controllable Lead Time

Malik¹,

Sarkar²

2018

DJ JEAM

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The purpose of this study is to investigate an inventory model for variable setup cost under stochastic conditions, in which the order quantity, reorder point, lead time and backorder price-discount rate are decision variables. The lead time demand is stochastic and exact distribution is unknown. Therefore, the distributionfree approach for the lead time demand with known mean and standard deviation is discussed. The proposed method is validated based on the numerical example and sensitivity analysis, and the results are compared with the existing studies in literature. The numerical results demonstrate that the proposed distribution free approach is more economical and saves significant cost.

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Section: Solution Algorithmmentioning

confidence: 99%

Section: Solution Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Distribution-Free Model with Variable Setup Cost, Backorder Price Discount and Controllable Lead Time

Malik¹,

Sarkar²

2018

DJ JEAM

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“…The literature proposes numerous works concerning stochastic (r,Q) models. In this regard, there exist studies involving Gaussian lead-time demand (e.g., [27][28][29][30]) or exploiting the minimax approach (e.g., [31][32][33]), as well as models that consider single-(e.g., [28,31,32]) or multi-echelon (e.g., [27,29,30]) systems. However, to the best of our knowledge, the MaxEnt principle has never been implemented into the (r,Q) policy.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the problem can only be approached adopting a suitable approximation. In this regard, the procedure that is typically adopted in the literature (see, e.g., [27][28][29][30][31][32][33]) consists in assuming that the density of X is Gaussian or is derived according to the minimax method. We take here a different perspective, which is based on adopting the MaxEnt principle.…”

mentioning

confidence: 99%

Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

Castellano

2015

Entropy

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This paper takes into account the continuous-review reorder point-lot size (r,Q) inventory model under stochastic demand, with the backorders-lost sales mixture. Moreover, to reflect the practical circumstance in which full information about the demand distribution lacks, we assume that only an estimate of the mean and of the variance is available. Contrarily to the typical approach in which the lead-time demand is supposed Gaussian or is obtained according to the so-called minimax procedure, we take a different perspective. That is, we adopt the maximum entropy principle to model the lead-time demand distribution. In particular, we consider the density that maximizes the entropy over all distributions with given mean and variance. With the aim of minimizing the expected total cost per time unit, we then propose an exact algorithm and a heuristic procedure. The heuristic method exploits an approximated expression of the total cost function achieved by means of an ad hoc first-order Taylor polynomial. We finally carry out numerical experiments with a twofold objective. On the one hand we examine the efficiency of the approximated solution procedure. On the other hand we investigate the performance of the maximum entropy principle in approximating the true lead-time demand distribution.

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A Case Study on One-Piece-Flow Production Mode Designing

Wang

2013

International Asia Conference on Industrial Engineering and Management Innovation (IEMI2012) Proceedings

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Improved inventory models with service level and lead time

Cited by 47 publications

References 11 publications

A Distribution-Free Model with Variable Setup Cost, Backorder Price Discount and Controllable Lead Time

A Distribution-Free Model with Variable Setup Cost, Backorder Price Discount and Controllable Lead Time

Stochastic Reorder Point-Lot Size (r,Q) Inventory Model under Maximum Entropy Principle

A Case Study on One-Piece-Flow Production Mode Designing

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