A data‐driven maintenance policy for railway wheelset based on survival analysis and Markov decision process

2020 ◽  
Author(s):  
Mariana Almeida Costa ◽  
Joaquim Pedro Azevedo Peixoto Braga ◽  
António Ramos Andrade
Keyword(s):  
Survival Analysis ◽  
Markov Decision Process ◽  
Decision Process ◽  
Data Driven ◽  
Maintenance Policy ◽  
Markov Decision

scholarly journals Data-Driven Iron and Steel Inventory Control Policies

Mathematics ◽  
2019 ◽  
Vol 7 (8) ◽  
pp. 718
Author(s):  
Tseng ◽  
Yu
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Lead Time ◽  
Data Driven ◽  
Inventory Policy ◽  
Iron And Steel ◽  
Markov Decision ◽  
The Face ◽  
Optimal Material ◽  
In Transit

In this study, we investigated the optimal material inventory policy with regard to the iron and steel industry’s effort to reduce massive overstocking issues in the face of increased corporate competitiveness. We gathered actual data, including sales and inventory numbers, from a steel and iron company over a period of 216 weeks between January 2010 and February 2014. We then utilized the Markov decision process (MDP) to analyze this data for inventory problems, such as relevant reorder points and reorder quantity issues as they relate to lead time, stock on hand and the limitations of having stock in-transit. The purpose of the study was to determine the most effective method for minimizing costs by using the optimal inventory policy to calculate and verify the effectiveness of the results. The final 52 weeks of data were put aside, while the initial 164 weeks were used to create an inbound material receipt system to ultimately establish a yearly (52-week) policy based on the inventory and sales data for weeks 113–164. Finally, we verified the effectiveness of the policy using the data from the final 52 weeks. The results showed that our proposed categorization method was effective for reducing the quantity of inventory while still meeting quarterly demands.

scholarly journals Data-Driven Markov Decision Process Approximations for Personalized Hypertension Treatment Planning

2016 ◽  
Vol 1 (1) ◽  
pp. 238146831667421 ◽  
Author(s):  
Greggory J. Schell ◽  
Wesley J. Marrero ◽  
Mariel S. Lavieri ◽  
Jeremy B. Sussman ◽  
Rodney A. Hayward
Keyword(s):  
Treatment Planning ◽  
Markov Decision Process ◽  
Decision Process ◽  
Data Driven ◽  
Hypertension Treatment ◽  
Markov Decision

scholarly journals Development of a Shipment Policy for Collection Centers

Mathematics ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 1385
Author(s):  
Irais Mora-Ochomogo ◽  
Marco Serrato ◽  
Jaime Mora-Vargas ◽  
Raha Akhavan-Tabatabaei
Keyword(s):  
Climate Change ◽  
Natural Disasters ◽  
Markov Decision Process ◽  
Decision Process ◽  
Necessary Conditions ◽  
Decision Makers ◽  
Humanitarian Organizations ◽  
The World ◽  
Markov Decision ◽  
Unsatisfied Demand

Natural disasters represent a latent threat for every country in the world. Due to climate change and other factors, statistics show that they continue to be on the rise. This situation presents a challenge for the communities and the humanitarian organizations to be better prepared and react faster to natural disasters. In some countries, in-kind donations represent a high percentage of the supply for the operations, which presents additional challenges. This research proposes a Markov Decision Process (MDP) model to resemble operations in collection centers, where in-kind donations are received, sorted, packed, and sent to the affected areas. The decision addressed is when to send a shipment considering the uncertainty of the donations’ supply and the demand, as well as the logistics costs and the penalty of unsatisfied demand. As a result of the MDP a Monotone Optimal Non-Decreasing Policy (MONDP) is proposed, which provides valuable insights for decision-makers within this field. Moreover, the necessary conditions to prove the existence of such MONDP are presented.

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