A Learning Agent For A Multi-Agent System For Project Scheduling In Construction

2016 ◽  
Author(s):  
Florian Wenzler ◽  
Willibald A. Guenthner
Keyword(s):  
Project Scheduling ◽  
Multi Agent System ◽  
Agent System ◽  
Learning Agent ◽  
Multi Agent

A market-based multi-agent system model for decentralized multi-project scheduling

2006 ◽  
Vol 150 (1) ◽  
pp. 115-135 ◽  
Author(s):  
Giuseppe Confessore ◽  
Stefano Giordani ◽  
Silvia Rismondo
Keyword(s):  
Project Scheduling ◽  
System Model ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals A-Team Solving Distributed Resource-Constrained Multi-Project Scheduling Problem

2019 ◽  
Vol 06 (04) ◽  
pp. 423-437
Author(s):  
Piotr Jędrzejowicz ◽  
Ewa Ratajczak-Ropel
Keyword(s):  
Global Optimization ◽  
Project Scheduling ◽  
Optimization Problems ◽  
Global Solutions ◽  
Scheduling Problem ◽  
Multi Agent System ◽  
Resource Constrained ◽  
Agent System ◽  
Multi Agent ◽  
Project Scheduling Problem

In this paper, a multi-agent system (MAS) based on the A-Team concept is proposed to solve the Distributed Resource-Constrained Multi-Project Scheduling Problem (DRCMPSP). In the DRCMPSP, multiple distributed projects are considered. Hence, the local task schedule for each project and a coordination of the shared decisions are considered. The DRCMPSP belongs to the class of the strongly NP-hard optimization problems. Multi-agent system seems the natural way of solving such problems. The A-Team MAS, proposed in this paper, has been built using the JABAT environment where two types of the optimization agents are used: local and global. Local optimization agents are used to find solutions for the local projects, and global optimization agents are responsible for the coordination of the local projects and for finding the global solutions. The approach has been tested experimentally using 140 benchmark problem instances from MPSPLIB library with minimizing the Average Project Delay (APD) as global optimization criterion.

scholarly journals Deep Q-Learning and Preference Based Multi-Agent System for Sustainable Agricultural Market

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5276
Author(s):  
María E. Pérez-Pons ◽  
Ricardo S. Alonso ◽  
Oscar García ◽  
Goreti Marreiros ◽  
Juan Manuel Corchado
Keyword(s):  
Market Price ◽  
Environmental Indicators ◽  
User Preferences ◽  
Future Market ◽  
Price Setting ◽  
Multi Agent System ◽  
Agent System ◽  
Q Learning ◽  
Learning Agent ◽  
Multi Agent

Yearly population growth will lead to a significant increase in agricultural production in the coming years. Twenty-first century agricultural producers will be facing the challenge of achieving food security and efficiency. This must be achieved while ensuring sustainable agricultural systems and overcoming the problems posed by climate change, depletion of water resources, and the potential for increased erosion and loss of productivity due to extreme weather conditions. Those environmental consequences will directly affect the price setting process. In view of the price oscillations and the lack of transparent information for buyers, a multi-agent system (MAS) is presented in this article. It supports the making of decisions in the purchase of sustainable agricultural products. The proposed MAS consists of a system that supports decision-making when choosing a supplier on the basis of certain preference-based parameters aimed at measuring the sustainability of a supplier and a deep Q-learning agent for agricultural future market price forecast. Therefore, different agri-environmental indicators (AEIs) have been considered, as well as the use of edge computing technologies to reduce costs of data transfer to the cloud. The presented MAS combines price setting optimizations and user preferences in regards to accessing, filtering, and integrating information. The agents filter and fuse information relevant to a user according to supplier attributes and a dynamic environment. The results presented in this paper allow a user to choose the supplier that best suits their preferences as well as to gain insight on agricultural future markets price oscillations through a deep Q-learning agent.

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