scholarly journals Simulation based optimization of a stochastic supply chain considering supplier disruption: An agent-based modeling and reinforcement learning

2018 ◽  
Vol 0 (0) ◽  
pp. 0-0 ◽  
Author(s):  
A. Aghaie ◽  
M. Hajian Heidary
Keyword(s):  
Supply Chain ◽  
Reinforcement Learning ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Simulation Based ◽  
Simulation Based Optimization

Simulation-based optimization of radiotherapy: Agent-based modeling and reinforcement learning

2017 ◽  
Vol 133 ◽  
pp. 235-248 ◽  
Author(s):  
Ammar Jalalimanesh ◽  
Hamidreza Shahabi Haghighi ◽  
Abbas Ahmadi ◽  
Madjid Soltani
Keyword(s):  
Reinforcement Learning ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Simulation Based ◽  
Simulation Based Optimization

Identification of Superior Improvement Trajectories for Production Lines via Simulation-Based Optimization with Reinforcement Learning

2021 ◽  
pp. 405-413
Author(s):  
Günther Schuh ◽  
Andreas Gützlaff ◽  
Matthias Schmidhuber ◽  
Jan Maetschke ◽  
Max Barkhausen ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Production Lines ◽  
Simulation Based ◽  
Simulation Based Optimization

scholarly journals Simulation-Based Optimization of a Two-Echelon Continuous Review Inventory Model with Lot Size-Dependent Lead Time

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1014
Author(s):  
Ibrahim Alharkan ◽  
Mustafa Saleh ◽  
Mageed Ghaleb ◽  
Abdulsalam Farhan ◽  
Ahmed Badwelan
Keyword(s):  
Supply Chain ◽  
Lead Time ◽  
Shop Floor ◽  
Lot Size ◽  
Continuous Review ◽  
Inventory Costs ◽  
Size Dependent ◽  
Simulation Based ◽  
Simulation Based Optimization ◽  
Total Inventory

This study analyzes a stochastic continuous review inventory system (Q,r) using a simulation-based optimization model. The lead time depends on lot size, unit production time, setup time, and a shop floor factor that represents moving, waiting, and lot size inspection times. A simulation-based model is proposed for optimizing order quantity (Q) and reorder point (r) that minimize the total inventory costs (holding, backlogging, and ordering costs) in a two-echelon supply chain, which consists of two identical retailers, a distributor, and a supplier. The simulation model is created with Arena software and validated using an analytical model. The model is interfaced with the OptQuest optimization tool, which is embedded in the Arena software, to search for the least cost lot sizes and reorder points. The proposed model is designed for general demand distributions that are too complex to be solved analytically. Hence, for the first time, the present study considers the stochastic inventory continuous review policy (Q,r) in a two-echelon supply chain system with lot size-dependent lead time L(Q). An experimental study is conducted, and results are provided to assess the developed model. Results show that the optimized Q and r for different distributions of daily demand are not the same even if the associated total inventory costs are close to each other.

scholarly journals Exploring the emergence of a biojet fuel supply chain in Brazil: An agent‐based modeling approach

GCB Bioenergy ◽  
2019 ◽  
Vol 11 (6) ◽  
pp. 773-790 ◽  
Author(s):  
Jorge A. Moncada ◽  
Judith A. Verstegen ◽  
John A. Posada ◽  
Martin Junginger ◽  
Zofia Lukszo ◽  
...  
Keyword(s):  
Supply Chain ◽  
Agent Based Modeling ◽  
Agent Based ◽  
Fuel Supply ◽  
Modeling Approach
Sign in / Sign up

Export Citation Format

Share Document