The enhancement of solving the distributed constraint satisfaction problem for cooperative supply chains using multi-agent systems

2008 ◽  
Vol 45 (4) ◽  
pp. 795-810 ◽  
Author(s):  
Fu-ren Lin ◽  
Hui-chun Kuo ◽  
Shyh-ming Lin
Keyword(s):  
Supply Chains ◽  
Constraint Satisfaction ◽  
Constraint Satisfaction Problem ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Constraint Satisfaction ◽  
Multi Agent

scholarly journals Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

2006 ◽  
Vol 25 ◽  
pp. 529-576 ◽  
Author(s):  
R. Mailler ◽  
V. R. Lesser
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Mediation Process ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Constraint Satisfaction ◽  
Systems Research ◽  
Multi Agent ◽  
Critical Paths ◽  
Real World Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques.

scholarly journals A multi-agent based system with big data processing for enhanced supply chain agility

2016 ◽  
Vol 29 (5) ◽  
pp. 706-727 ◽  
Author(s):  
Mihalis Giannakis ◽  
Michalis Louis
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Semantic Web ◽  
Supply Chains ◽  
Multi Agent Systems ◽  
Content Type ◽  
Agent Based ◽  
Agent Systems ◽  
Supply Chain Agility ◽  
Multi Agent

Purpose Decision support systems are becoming an indispensable tool for managing complex supply chains. The purpose of this paper is to develop a multi-agent-based supply chain management system that incorporates big data analytics that can exert autonomous corrective control actions. The effects of the system on supply chain agility are explored. Design/methodology/approach For the development of the architecture of the system, a sequential approach is adopted. First three fundamental dimensions of supply chain agility are identified – responsiveness, flexibility and speed. Then the organisational design of the system is developed. The roles for each of the agents within the framework are defined and the interactions among these agents are modelled. Findings Applications of the model are discussed, to show how the proposed model can potentially provide enhanced levels in each of the dimensions of supply chain agility. Research limitations/implications The study shows how the multi-agent systems can assist to overcome the trade-off between supply chain agility and complexity of global supply chains. It also opens up a new research agenda for incorporation of big data and semantic web applications for the design of supply chain information systems. Practical implications The proposed information system provides integrated capabilities for production, supply chain event and disruption risk management under a collaborative basis. Originality/value A novel aspect in the design of multi-agent systems is introduced for inter-organisational processes, which incorporates semantic web information and a big data ontology in the agent society.

A New Hybrid Distributed Double Guided Genetic Swarm Algorithm for Optimization and Constraint Reasoning

2012 ◽  
Vol 3 (2) ◽  
pp. 63-74 ◽  
Author(s):  
Asma Khadhraoui ◽  
Sadok Bouamama
Keyword(s):  
Particle Swarm Optimization ◽  
Constraint Satisfaction ◽  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Constraint Satisfaction Problems ◽  
Multi Agent Systems ◽  
Swarm Optimization ◽  
Agent Systems ◽  
Constraint Reasoning ◽  
Multi Agent

In this paper the authors propose a new distributed double guided hybrid algorithm combining the particle swarm optimization (PSO) with genetic algorithms (GA) to resolve maximal constraint satisfaction problems (Max-CSPs). It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization (GSO). Their approach consists of a set of evolutionary agents dynamically created and cooperating in order to find an optimal solution. Each agent executes its own hybrid algorithm and it is able to compute its own parameters. The authors’ approach is compared to the GSO. It demonstrates its superiority. They reached these results thanks to the distribution using multi-agent systems, diversification and intensification mechanisms.

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