Agent-based dynamic scheduling model for product-driven production (original) (raw)
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Agent-based Approach for Manufacturing Dynamic Scheduling
2006 International Conference on Service Systems and Service Management, 2006
The paper provides a basic introduction to the scheduling and control of production systems using a hierarchical structure of a decision making (DM). A DM process created on the MAS basis may consider an intelligent and adaptive agent on the higher level and an autonomous agent in the lower level of the DM according to the system requirements. An example for the optimal or near optimal selection of the control strategy is presented here.
Proposal of Multi-Agent based Model for Dynamic Scheduling in Manufacturing
2005
Some recent trends in manufacturing in particular and business in general, lead to new approaches regarding the organisation and software architecture, mainly adopting distributed solutions. Such organisations imply organisational and technological evolution through agility, distribution, decentralisation, reactivity and flexibility. New organisational and technological paradigms are needed in order to reply to the modern manufacturing systems challenges. The Multi-Agent paradigm represents one of the most promising approaches to build complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature. Modelling the Scheduling of Manufacturing Systems by means of two technologies like Meta-Heuristics and Multi-Agent Systems seems to be an interesting way to see Industrial Systems in the future. A multi-agent based model for support dynamic scheduling in manufacturing environments is proposed.
A Multi-agent Based Dynamic Scheduling of Flexible Manufacturing Systems
Global Journal of Flexible Systems Management, 2019
Ongoing market requirements and real-time demands have led to intense competiveness in the manufacturing industry. Hence competitors are bound to employ newer means of manufacturing systems that can handle the ongoing market conditions in a flexible and efficient manner. To tackle these problems manufacturing control systems have evolved to the distributed manufacturing control system by exploiting their control architectures. These distributed control architectures provide an efficient mechanism that gives reactive and dynamically optimized system performance. This paper studies the impact of design and control factors on the performance of flexible manufacturing system. The system is evaluated on the basis of makespan, average machine utilization and the average waiting time of parts at the queue. Discrete-event based simulation models are developed to conduct simulation experiments. The results obtained were subjected to multiresponse optimization as per Grey based Taguchi methodology. The effect of control architecture was statistically significant on the performance of flexible manufacturing system. Keywords Distributed control architectures Á Flexible manufacturing systems Á Grey relational analysis Á Heterarchical control architectures Á Multi-agent systems Á Taguchi experiment design
Multi-agent system for dynamic scheduling
2020 International Joint Conference on Neural Networks (IJCNN), 2020
This paper proposes a flexible manufacturing system based on intelligent computational agents. A Multi-Agent System composed of 4 types of reactive agents was designed to control the operation of a real implementation in the Intelligent Automation Lab at Instituto Superior Técnico. This implementation was based and constructed analogously to a known benchmark, AIP-PRIMECA. The agents were modelled using Petri nets and agent communications were defined through the combination of FIPA Interaction Protocols. The system was tested under the conditions of static and dynamic scenarios, having its performance validated whenever possible by comparison with results from a Potential Fields Approach in the same benchmark. Overall, the performance exhibited by the proposed MAS was slightly better and it is worth highlighting the simple behaviour of each agent and ability to respond in real-time to all the dynamic scenarios tested.
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.
Computers in Industry, 2001
This research introduces an intelligent approach for identifying the optimal production schedule to satisfy product and manufacturing constraints. In this approach, product constraints are modeled using a feature-based product representation scheme. Manufacturing constraints are described as available resources including facilities and persons. Manufacturing requirements for producing the products, including tasks and sequential constraints for conducting these tasks, are represented as part of the product feature descriptions. The optimal production process and its timing parameter values are identi®ed using constraint-based search and agent-based collaboration. The intelligent optimal production scheduling system was implemented using Smalltalk, an object oriented programming language.
A Multi Agent Structural Design For Production Scheduling Using Distributed Case Based Reasoning
In these days, the production system of particular items must deal with globalization, proliferating product variety, organizational barriers, and quick information sharing. Consequently, appropriate tools are needed to efficient support supply chain management. We believe that multi-agents are good candidates to overcome these challenges. In this research work, we propose MAS based Decision support system (DSS) which is a Multi-Agent System (MAS) to run the production activities efficiently. This research work is totally focused on the problem of designing of the Decision support system with aiming of running the production activities effortlessly. Such system is a well-known indication of coordination difficulty in conservative production system.
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.
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...
Multi-agent systems negotiation to deal with dynamic scheduling in disturbed industrial context
Journal of Intelligent Manufacturing, 2019
It is now accepted that using multi-agent systems (MAS) improve the reactivity to treat perturbation(s) within flexible manufacturing system. Intelligent algorithms shall be used to address these perturbation(s) and all smart decision entities within their environment have to continuously negotiate until their common and final goal is achieved. This paper proposes a negotiation-based control approach to deal with variability on a manufacturing system. It has initially formulated and modeled an environment in which all contributing entities or agents operate, communicate, and interact with each other productively. Then after, simulation and applicability implementation experiments on the basis of full-sized academic experimental platform have been conducted to validate the proposed control approach. Product and resource entities negotiate considering different key performance measures in order to set best priority-based product sequencing. This has been done with expectations that the applicability of the negotiation-based decision-making will be more adaptable to deal with perturbation(s) than another alternative decision-making approach called pure reactive control approach. The result showed that negotiation among the decisional entities has brought significant improvement in reducing makespan and hence conveyed better global performance of a manufacturing system.