Dynamic Joint Decision on Price and Delivery Date in MTO Manufacturer Based on Agent

2013 ◽  
Vol 860-863 ◽  
pp. 2812-2816
Author(s):  
Juan Hao ◽  
Jian Jun Yu ◽  
Mian Can Wu
Keyword(s):  
Queuing Theory ◽  
Learning Algorithm ◽  
Production Capacity ◽  
Service Level ◽  
Joint Decision ◽  
New Approach ◽  
Q Learning ◽  
Make To Order ◽  
Delivery Date ◽  
Total Profit

In order to maximize the total profit and improve the service level, based on the perspective of queuing theory, a new approach for dynamic joint decision on price and delivery date in Make-to-order (MTO) manufacturing firms using Q-learning algorithm was proposed. Compared with static price and delivery date policy, the simulation results show that the proposed algorithm performs better in total profit and service level. The total profit does not increase with the growing number of accepted orders and the number of accepted orders must match the production capacity.

scholarly journals MOTIVATION OF PARTICIPANTS IN CROWDSOURCING PLATFORMS USING INTELLIGENT AGENTS

2020 ◽  
pp. 78-87
Author(s):  
Vijayalakshmi Anand ◽  
Chittaranjan Hota
Keyword(s):  
Reinforcement Learning ◽  
Intelligent Agents ◽  
Learning Algorithm ◽  
Action Plan ◽  
User Participation ◽  
New Approach ◽  
Q Learning ◽  
Internet Users ◽  
Optimal Action ◽  
Reinforcement Learning Algorithm

Crowdsourcing is a model where individuals or organizations receive services from a large group of Internet users including ideas, finances, completing a complex task, etc. Several crowdsourcing websites have failed due to lack of user participation; hence, the success of crowdsourcing platforms is manifested by the mass of user participation. However, an issue of motivating users to participate in crowdsourcing platform stays challenging. We have proposed a new approach, i.e., reinforcement learning-based gamification method to motivate users. Gamification has been a practical approach to engaging users in many fields, but still, it needs an improvement in the Crowdsourcing platform. In this paper, the gamification approach is strengthened by a reinforcement learning algorithm. We have created an intelligent agent using the Reinforcement learning algorithm (Q-learning). This agent suggests an optimal action plan that yields maximum reward points to the users for their active participation in the Crowdsourcing application. Also, its performance is compared with the SARSA algorithm (On- policy learning), which is another Reinforcement learning algorithm.

scholarly journals A New Approach Based on Fuzzy-Q-Learning Algorithm to Control 3 Level T-Type Voltage Source Converter

2020 ◽  
Vol 53 (6) ◽  
pp. 845-852
Author(s):  
Abderrahmane Berkani ◽  
Mohamed Bey ◽  
Rabah Araria ◽  
Tayeb Allaoui
Keyword(s):  
Fuzzy Controller ◽  
Learning Algorithm ◽  
Voltage Source ◽  
Voltage Source Converter ◽  
Dynamic Mode ◽  
New Approach ◽  
Q Learning

In order to perform the control of a voltage source converter and enhance its capability of working, in this work, a new approach based on Fuzzy-Q-Learning algorithm is applied. The utilized method makes the fuzzy controller in dynamic mode that means this controller will be adapted in each variation in the system by changing the different values depends on the change in the system. By perform the DPC technique, the obtained results show that this new fuzzy reinforcement gives better performance to control a voltage source converter.

scholarly journals An Adaptive Deployment Algorithm for IaaS Cloud Virtual Machines Based on Q Learning Mechanism

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Shuguang Chen
Keyword(s):  
Energy Consumption ◽  
Virtual Machines ◽  
Learning Algorithm ◽  
Service Level Agreement ◽  
Service Level ◽  
High Energy ◽  
Learning Mechanism ◽  
Q Learning ◽  
Sla Violation ◽  
Violation Rate

When deploying infrastructure as a service (IaaS) cloud virtual machines using the existing algorithms, the deployment process cannot be simplified, and the algorithm is difficult to be applied. This leads to the problems of high energy consumption, high number of migrations, and high average service-level agreement (SLA) violation rate. In order to solve the above problems, an adaptive deployment algorithm for IaaS cloud virtual machines based on Q learning mechanism is proposed in this research. Based on the deployment principle, the deployment characteristics of the IaaS cloud virtual machines are analyzed. The virtual machine scheduling problem is replaced with the Markov process. The multistep Q learning algorithm is used to schedule the virtual machines based on the Q learning mechanism to complete the adaptive deployment of the IaaS cloud virtual machines. Experimental results show that the proposed algorithm has low energy consumption, small number of migrations, and low average SLA violation rate.

Application of improved Q learning algorithm to job shop problem

2009 ◽  
Vol 28 (12) ◽  
pp. 3268-3270
Author(s):  
Chao WANG ◽  
Jing GUO ◽  
Zhen-qiang BAO
Keyword(s):  
Job Shop ◽  
Learning Algorithm ◽  
Q Learning

scholarly journals Aircraft Maintenance Check Scheduling Using Reinforcement Learning

Aerospace ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. 113
Author(s):  
Pedro Andrade ◽  
Catarina Silva ◽  
Bernardete Ribeiro ◽  
Bruno F. Santos
Keyword(s):  
Reinforcement Learning ◽  
Time Horizon ◽  
Learning Algorithm ◽  
Initial Conditions ◽  
Q Learning ◽  
Scheduling Policy ◽  
Real Scenario ◽  
Maintenance Plan ◽  
Small Disturbances

This paper presents a Reinforcement Learning (RL) approach to optimize the long-term scheduling of maintenance for an aircraft fleet. The problem considers fleet status, maintenance capacity, and other maintenance constraints to schedule hangar checks for a specified time horizon. The checks are scheduled within an interval, and the goal is to, schedule them as close as possible to their due date. In doing so, the number of checks is reduced, and the fleet availability increases. A Deep Q-learning algorithm is used to optimize the scheduling policy. The model is validated in a real scenario using maintenance data from 45 aircraft. The maintenance plan that is generated with our approach is compared with a previous study, which presented a Dynamic Programming (DP) based approach and airline estimations for the same period. The results show a reduction in the number of checks scheduled, which indicates the potential of RL in solving this problem. The adaptability of RL is also tested by introducing small disturbances in the initial conditions. After training the model with these simulated scenarios, the results show the robustness of the RL approach and its ability to generate efficient maintenance plans in only a few seconds.

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