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

Castellano, Davide

doi:10.3390/e18010016

Cited by 8 publications

(10 citation statements)

References 49 publications

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“…(r, Q) model is also known as reorder point-lot size model [34]. In the (r, Q) model, r represents the inventory point while Q represents the quantity of the production or lot size [35].…”

Section: 𝐴 = { 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑞 < 𝑠 𝑑𝑜 𝑛𝑜𝑡ℎ𝑖𝑛𝑔 𝑞 ≥ 𝑠mentioning

confidence: 99%

Weighted Round Robin (WRR) Based Replenishment Model in Vendor Managed Inventory (VMI) System

Kusuma

2022

IJIE

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Vendor managed inventory (VMI) is a popular supply chain system where vendor or supplier take responsibility and decision in managing its customers’ inventory. Two important goals of the VMI are improving service level and maintaining inventory still low and available. Many studies in VMI compare their performance with the traditional system. Unfortunately, studies in improving VMI performance are rare. This work aims to improve VMI by implementing Weighted Round Robin (WRR), a popular scheduling model in computer system, in the replenishment model in VMI. WRR is popular because of its load balancing nature. Environment in this work is two-echelon supply chain. The vendor is a multi-product manufacturer. The customers are retailers. This WRR based replenishment model is then compared with two common replenishment models: (s, S) model and (r, Q) model. In this work, we observe two performance parameters: sales and inventory condition. Based on the simulation result, it is shown that the WRR model performs better than the existing (s, S) model and (r, Q) model and it occurs in most of the observed variables. In the certain condition, performance of the WRR model compared with the (s, S) model and the (r, Q) model is as follows. The WRR model performs 31 percent better than the (s, S) model and 12 percent better than the (r, Q) model in success ratio. Manufacturer’s stock in the WRR model is only 36 percent than in the (s, S) model and 40 percent than in the (r, Q) model. Total stock in the supply chain in the WRR model is only 63 percent than in the (s, S) model and 89 percent than in the (r, Q) model.

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“…(r, Q) model is also known as reorder point-lot size model [34]. In the (r, Q) model, r represents the inventory point while Q represents the quantity of the production or lot size [35].…”

Section: 𝐴 = { 𝑝𝑟𝑜𝑑𝑢𝑐𝑒 𝑞 < 𝑠 𝑑𝑜 𝑛𝑜𝑡ℎ𝑖𝑛𝑔 𝑞 ≥ 𝑠mentioning

confidence: 99%

Weighted Round Robin (WRR) Based Replenishment Model in Vendor Managed Inventory (VMI) System

Kusuma

2022

IJIE

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show abstract

“…These variables have their purpose or behavior. Higher r can avoid stockout probability but increase higher inventory space [17]. Larger Q can reduce replenishment frequency but increase inventory level [17].…”

Section: Continuous Review (R Q) Replenishment Policymentioning

confidence: 99%

“…Higher r can avoid stockout probability but increase higher inventory space [17]. Larger Q can reduce replenishment frequency but increase inventory level [17]. There are two types of inventory review in this model: continuous (real time) and periodic [18].…”

Section: Continuous Review (R Q) Replenishment Policymentioning

confidence: 99%

Collaborative vendor managed inventory model by using multi agent system and continuous review (r, Q) replenishment policy

Daru¹,

Kallista²

2022

J Appl Eng Science

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In the vendor-managed inventory (VMI) system, the vendor takes over responsibility for managing customer inventory so that delivery is not based on the order but the customer's inventory condition. It makes the vendor becomes a dominant entity, and customers are supplied by its own vendor exclusively. That is why most studies in VMI implement a single-vendor-single-customer or single-vendor-multi-customer scenario. In certain conditions, this exclusiveness can increase lost sales. Besides, most of them implement a single product scenario. In this work, we develop VMI model for the multi-vendor-customer-product scenario. This model is developed based on the collaborative multi-agent system. The relationship between vendors and customers is many-to-many. This work aims to reduce lost sales and maintain efficiency in the inventory. The continuous review (r, Q) policy is used as the replenishment model. The simulation result shows that the collaborative model creates higher sales, lower lost sales, and competitive inventory than the non-collaborative one. The lost sales is 50 to 75 percent lower. The sales percentage is 17 to 27 percent higher. The total retailers' stock is 20 to 38 percent higher. The total vendors' stock is 11 to 30 percent lower. The total stock in the supply chain in the collaborative model is 2 to 16 percent higher. The number of retailers is directly proportional to the total vendor's stock and total supply chain stock gaps; inversely proportional to the lost sales gap; and not related to the sales percentage and total retailers' stock gaps.

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“…For each fuzzy case, they investigated a computing schema for the modified continuous review inventory model and develop an algorithm to find the optimal inventory strategy. [2] in his work developed a mathematical model for continuous-review reorder quantity (r, Q) inventory system. In his paper, he took into account the continuous-review reorder point-lot size under stochastic demand, with the backorders-lost sales mixture.…”

Section: Introductionmentioning

confidence: 99%

“…its Q & R derivatives. Thus obtaining the new expressions for Q and as follows;By completing squares on and solving for Q, we have Dividing through by d to get the new standardised expression for Q, we have is the equivalence of equation(2) in the model by[1]. Now, we apply the new model to the same Gamma distributed demands as follows; If lead time demand follows a -ve exponential distribution, i.e.…”

mentioning

confidence: 99%

Reorder quantities for (Q,R) inventory models

Adamu¹

2017

imf

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The reorder level R and the reorder quantity Q are the parameters to be decided in a continuous review inventory policy. Their optimal values can be approached through iterative methods, but these are tedious and inconvenient for control routines. A frequent practice is to set Q as the economic reorder quantity and compute R accordingly. Yet this practice may introduce a substantial cost penalty. Existing literature demonstrated attempts to rearrange conventional theoretical expressions to facilitate the use of numerical approximations to help find an optimal solution in a continuous review inventory policy, however, the cost function derived and used in the rearrangement were faulty. This paper re-visits the formulation of the cost function by stating explicitly necessary assumptions, and obtained the correct cost function. Expressions for the optimising parameters R & were re-obtained based on the correct cost function. The new optimal expressions for R & and the cost function were implemented. The result of this work showed that, the reviewed model gives lower inventory cost with higher optimal order quantity than the model under review, and hence the reviewed model performs better when compared to the model under review.

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

Cited by 8 publications

References 49 publications

Weighted Round Robin (WRR) Based Replenishment Model in Vendor Managed Inventory (VMI) System

Weighted Round Robin (WRR) Based Replenishment Model in Vendor Managed Inventory (VMI) System

Collaborative vendor managed inventory model by using multi agent system and continuous review (r, Q) replenishment policy

Reorder quantities for (Q,R) inventory models

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