Optimal ordering policies and capital investment in setup reduction for continuous review inventory system with random yields

2003 ◽  
Vol 6 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Li-Chiao Lin ◽  
Kuo-Lung Hou
Keyword(s):  
Capital Investment ◽  
Inventory System ◽  
Continuous Review ◽  
Random Yields ◽  
Ordering Policies ◽  
Optimal Ordering ◽  
Setup Reduction

Multi-Location Inventory Model Considering Bidirectional and Unidirectional Transshipments Simultaneously

2013 ◽  
Vol 694-697 ◽  
pp. 2742-2745
Author(s):  
Jin Hong Zhong ◽  
Yun Zhou
Keyword(s):  
Process Model ◽  
Approximate Calculation ◽  
Inventory Model ◽  
Inventory System ◽  
Death Process ◽  
Birth And Death Process ◽  
Inventory Models ◽  
Continuous Review ◽  
Poisson Demand ◽  
Queue Theory

Abstract. A cross-regional multi-site inventory system with independent Poisson demand and continuous review (S-1,S) policy, in which there is bidirectional transshipment between the locations at the same area, and unidirectional transshipment between the locations at the different area. According to the M/G/S/S queue theory, birth and death process model and approximate calculation policy, we established inventory models respectively for the loss sales case and backorder case, and designed corresponding procedures to solve them. Finally, we verify the effectiveness of proposed models and methods by means of a lot of contrast experiments.

scholarly journals Optimal ordering policies and sourcing strategies with supply disruption

2014 ◽  
Vol 10 (4) ◽  
pp. 1147-1168 ◽  
Author(s):  
Xiaoming Yan ◽  
◽  
Minghui Zhang ◽  
Ke Liu ◽  
Yong Wang ◽  
...  
Keyword(s):  
Supply Disruption ◽  
Sourcing Strategies ◽  
Ordering Policies ◽  
Optimal Ordering

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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