A deep q-learning-based optimization of the inventory control in a linear process chain
MA Dittrich, S Fohlmeister - Production Engineering, 2021 - Springer
Due to growing globalized markets and the resulting globalization of production networks
across different companies, inventory and order optimization is becoming increasingly …
across different companies, inventory and order optimization is becoming increasingly …
Inventory control of multiple perishable goods using deep reinforcement learning for sustainable environment
Perishable goods like fresh produce are growing part of today's e-commerce projects. Due
to their perishable nature, traditionally they have accounted for a high share in the inventory …
to their perishable nature, traditionally they have accounted for a high share in the inventory …
Reinforcement learning approach for multi-period inventory with stochastic demand
M Shakya, HY Ng, DJ Ong, BS Lee - IFIP International Conference on …, 2022 - Springer
Finding an optimal solution to multi-period inventory ordering decision problems with
uncertain demand is important for any manufacturing organization. Moreover, these …
uncertain demand is important for any manufacturing organization. Moreover, these …
Adaptive inventory replenishment using structured reinforcement learning by exploiting a policy structure
We consider an inventory replenishment problem with unknown and non-stationary
demand. We design a structured reinforcement learning algorithm that efficiently adapts the …
demand. We design a structured reinforcement learning algorithm that efficiently adapts the …
Applying and comparing policy gradient methods to multi-echelon supply chains with uncertain demands and lead times
In the present work, we have applied and compared Deep Reinforcement Learning
techniques to solve a problem usually addressed with Operations Research tools. State-of …
techniques to solve a problem usually addressed with Operations Research tools. State-of …
Deep reinforcement learning approach for capacitated supply chain optimization under demand uncertainty
With the global trade competition becoming further intensified, Supply Chain Management
(SCM) technology has become critical to maintain competitive advantages for enterprises …
(SCM) technology has become critical to maintain competitive advantages for enterprises …
A policy gradient based reinforcement learning method for supply chain management
Y Hachaïchi, Y Chemingui… - 2020 4th International …, 2020 - ieeexplore.ieee.org
Technological advances of the recent decades have significantly affected the business
world, the retail business in particular. Retailers need to innovate to maintain a competitive …
world, the retail business in particular. Retailers need to innovate to maintain a competitive …
[HTML][HTML] An analysis of multi-agent reinforcement learning for decentralized inventory control systems
M Mousa, D van de Berg, N Kotecha… - Computers & Chemical …, 2024 - Elsevier
Most solutions to the inventory management problem assume a centralization of information
that is incompatible with organizational constraints in supply chain networks. The problem …
that is incompatible with organizational constraints in supply chain networks. The problem …
Learning to Minimize Cost to Serve for Multi-Node Multi-Product Order Fulfilment in Electronic Commerce
In the retail industry, electronic commerce (e-commerce) has grown quickly in the last
decade and has further accelerated as a result of movement restrictions during the …
decade and has further accelerated as a result of movement restrictions during the …
A minibatch-sgd-based learning meta-policy for inventory systems with myopic optimal policy
Stochastic gradient descent (SGD) has proven effective in solving many inventory control
problems with demand learning. However, it often faces the pitfall of an infeasible target …
problems with demand learning. However, it often faces the pitfall of an infeasible target …