scholarly journals Optimal Replenishment Strategy for Inventory Mechanism with a Known Price Increase and Backordering in Finite Horizon

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Lixia Zhang ◽  
Bo Feng
Keyword(s):  
Minimum Cost ◽  
Finite Horizon ◽  
Price Increase ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Inventory Cost ◽  
Closed Form Solutions ◽  
Ordering Policy ◽  
Optimal Ordering Policy ◽  
Optimal Ordering

For the finite horizon inventory mechanism with a known price increase and backordering, based on minimizing the inventory cost, we establish two mixed integer optimization models. By buyer’s cost analysis, we present the closed-form solutions to the models, and by comparing the minimum cost of the two strategies, we provide an optimal ordering policy to the buyer. Numerical examples are presented to illustrate the validity of the model, and sensitivity analysis on major parameters is also made to show some insights to the inventory model.

A SIMPLE SOLUTION METHOD FOR THE FINITE HORIZON EOQ MODEL FOR DETERIORATING ITEMS WITH COST CHANGES

2011 ◽  
Vol 28 (06) ◽  
pp. 689-704 ◽  
Author(s):  
HORNG-JINH CHANG ◽  
WEN-FENG LIN
Keyword(s):  
Closed Form Solution ◽  
Minimum Cost ◽  
Form Solution ◽  
Inventory System ◽  
Finite Horizon ◽  
Eoq Model ◽  
Ordering Policy ◽  
Optimal Ordering Policy ◽  
Cost Parameters ◽  
The Cost

In this article, we generalize Lev and Weiss's (1990) finite horizon economic order quantity (EOQ) model with cost change to the inventory system with deterioration. Supplier announces some or all of cost parameters may change after a decided time. Depending on whether the inventory is depleted at the time of the last opportunity to purchase before some or all of the cost parameters may change, there are two types of inventory models to be discussed. The main objective of this paper is to identify the optimal ordering policy of the inventory system by comparing the minimum cost of the two types of models. We suggest a finite horizon EOQ model to combine the above two types and propose a theorem that can quickly identify the optimal policy of the suggested model. In considering temporary price discount problem and discrete-time EOQ problem, in general, there are integer operators in mathematical models, but our approach offers a closed-form solution to these kinds of problems. Numerical examples are presented to demonstrate the results of the proposed properties and theorem.

Formulation of an optimal ordering policy with generalised time-dependent demand, quadratic holding cost and partial backlogging

2021 ◽  
Vol 42 (5) ◽  
pp. 1163-1179
Author(s):  
Prachi Swain ◽  
Chittaranjan Mallick ◽  
Trailokyanath Singh ◽  
Pandit Jagatananda Mishra ◽  
Hadibandhu Pattanayak
Keyword(s):  
Time Dependent ◽  
Partial Backlogging ◽  
Ordering Policy ◽  
Holding Cost ◽  
Optimal Ordering Policy ◽  
Optimal Ordering ◽  
Dependent Demand

Retail Ordering Policy Risk

2017 ◽  
pp. 197-225
Keyword(s):  
Simulation Models ◽  
Stochastic Optimisation ◽  
Control Stage ◽  
Ordering Policy ◽  
Stochastic Techniques ◽  
Ordering Policies ◽  
Optimal Ordering Policy ◽  
Policy Risk ◽  
Associated Risk Factors ◽  
Optimal Ordering

The method is applied to Retail Ordering Policy to manage the associated risk. DMAIC framework applies stochastic techniques. Stochastic optimisation determines the optimal retail ordering policies to maximise profit. Simulate every determined optimal ordering policy and calculate profits, risks, and Six Sigma metrics to measure against specified target limits. Analyse simulation results and identify and quantify the main contributors to the profits variability by using sensitivity analysis. The optimal retail ordering policies are ranked based on their profits and associated risk factors. The technically best optimal retail ordering policy is recommended to the management for implementation. Control stage is elaborated by reusing the data and presented stochastic optimisation and simulation models for ongoing management of the optimal strategy. Some changes are applied to the data and models however, in order to emulate the scenario of an implemented strategy.

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