A Multi Agent Structural Design For Production Scheduling Using Distributed Case Based Reasoning (original) (raw)

A Distributed Case based reasoning with Decision Support System for Production Scheduling

— The intelligent agents are core field of artificial intelligence. They are used in a number of complex problems. The group of Intelligent Agents named as Multi agent system are most suitable for implementing distributed applications widely used in engineering, Medical, and management etc. They provide a lot of features that helps to optimize the complex problems. The main characteristics of such agents are on decision taking capabilities. In additional multi agents system are more suitable as their autonomous nature. They are capable of capturing information from the existing environment particular they deserve. With the help of Case based reasoning Multi agent system is capable of implementing human reasoning along with utilization of existing solution. In this paper we proposed Distributed Case based reasoning model for utilizing the experience of multiple entities working in the concerned environment. The proposed model Enhance decision taking capabilities of proposed system in terms of the solution suggested with the help of multiple case based. The proposed system is more reliable and efficient in comparison of additional systems. In this paper the proposed model is applied to implement production scheduling.

Multi-agent system for an integrated distributed production planning : an innovative approach based on technological support

2020

Production planning is considered as crucial for the success of operation in production management and represents an important role in the performance of the supply chain. Production planning as a critical phase of supply chain management in general and in manufacturing system in particular is a multi-parameters decision-making issue. Generally, the process of production planning the most suitable and appropriate planning has multiple complexities and obstacles that holds multiple-parameters. This paper handles the production planning problem in a Moroccan Marble company. The present study presents an integrated distributed manufacturing planning approach based on multi-agent system methods in order to solve the issues related to the production planning procedure within Marble company with the help of the Agile Production Planning model and Collaborative Production Planning model within the context of DPP. The study starts with reviewing the previous works of multi-parameters manufa...

Comparative Study of Different Multi- Agent based Decision Support System

There has been growing on big data since last decade for discovering useful trends or patterns that are used in production and decision making. Multi agent based decision support system an automated judgment that supports decision making is composed of human and computer interaction to help in decision making accuracy. Also multi-agent systems (MAS) are collections of independent intelligent entities that collaborate in the joint resolution of a complex problem. Multi-agent based decision support systems can be used to solve large-scale convention problem. This paper is a survey of the recent research in multi-agent based decision support systems to support for classification problems. The purpose of the survey described in this paper is the development of a novel large-scale production system based on Multi-agent based Decision Support System (MDSS) for distributed Case-base.

A Multi-Agent Decision Support System for Supply Chain Management

2009

This paper presents an extended abstract of the author's doctoral research project on developing a multi-agent intelligent system for automatic managing supply chains. Supply chain management (SCM) is a very complex and dynamic environment. The doctoral work, which started in October 2005, is dedicated to finding better solutions for successful performance in the domain of real-time SCM.

Distributed production planning and control agent-based system

International Journal of Production Research, 2006

A model of an Agent based Production Planning and Control (PPC) system able to be dynamically adaptable to local and distributed utilization of production resources and materials is presented. The PPC system is based on the selection of resources to deal with one order of different quantities of one product each time. In this way it is build one scheduling solution for that particular order. The production resources are selected and scheduled using a multiagent system supported by an implementation of the Smith Contract Net, using Java Spaces technology. The multiagent system is based on three main agents: Client, Resource and Manager. These agents negotiate the final product, and the correspondent components, requested by the client. An order for each product (component) triggers a process of dynamic design of a production system to fulfill that particular order. This system exists till the end of the order.

Intelligent and collaborative Multi-Agent System to generate and schedule production orders

2008

The authors present a Multi-Agent System for constructing and releasing production orders. In a manufacturing enterprise, the generation of production orders consists in a set of coordinated tasks among departments. This has been achieved traditionally as a module of the Production Activity Control (PAC) system. However, classic PAC modules lack collaborative techniques and intelligent behaviour. Moreover, in real-life situations experienced planners take over traditional PAC systems, since the range of possibilities to actually build production orders increases exponentially. To contribute to production planning, we present an intelligent and collaborative Multi-Agent System (MAS), having coordinated two forms to emulate intelligence. The learning capability is achieved by means of a Feed-forward Artificial Neural Network (FANN) with the back-propagation algorithm. The FANN is embedded within a machine agent whose objective is to obtain the appropriate machine in order to comply with requirements coming from the sales department. Also, an expert system is provided to a tool agent, which in turn is in charge of inferring the right tooling. The MAS also consists of a coordinator and a spy. The coordinator agent has the responsibility to control the flow of messages among the agents, whereas the spy agent is constantly reading the Enterprise Information System. Finally, a scheduler agent schedules the production orders. The resul

The Design of an Agent-Based Production Scheduling Software Framework for Improving Planning-Scheduling Collaboration

In the operation management environment, the process of production scheduling is responsible for detailing operating activities by indicating a set of methods and tools that are conditioned, among other restrictions, by the tactical decisions that are made in the production planning environment. Although, theoretically, a bi-directional flow of information should exist among both environments of decisions that permit those who are involved to coordinate both levels in practice, such does not occur because of a structured decision-making tool gap. This document proposes an architecture that is based on agents software designed with INGENIAS methodology and proposed from an analysis of requirements that is based on CIMOSA. Once it is implemented, a prototype that employs JADE has been carried out to test and verify its suitable operation.

A Multi-Agent Decision Support System for Dynamic Supply Chain Organization

In this work, a multi-agent system (MAS) for supply chain dynamic configuration is proposed. The brain of each agent is composed of a Bayesian Decision Network (BDN); this choice allows the agent for taking the best decisions estimating benefits and potential risks of different strategies, analyzing and managing uncertain information about the collaborating companies. Each agent collects information about customer's orders and current market prices, and analyzes previous experiences of collaborations with trading partners. The agent therefore performs a probabilistic inferential reasoning to filter information modeled in its knowledge base in order to achieve the best performance in the supply chain organization.

A Multi-Agent System for Supply Chain Management

In today's globalization of manufacturing operations managing the supply chain has become an extremely challenging task as it implies tracking order fulfillment, due dates and billing between partners working at different time-zones, with different currencies, tax systems and way to represent information. To achieve on-line control over the status of an order receipt/delivery in a global manufacturing environment the only possibility is to use latest network technologies for accessing and exchanging electronic information. Latest research in distributed artificial intelligence offers excellent tools for modeling the interactions between different nodes of the supply-chain network. This paper presents a multi-agent system approach to the design of global supply chain management networks. The main advantages of this system in unifying heterogeneous information and offering easy tractability for the manager are illustrated on a particular implementation.