Proposal of Multi-Agent based Model for Dynamic Scheduling in Manufacturing

Size: px
Start display at page:

Download "Proposal of Multi-Agent based Model for Dynamic Scheduling in Manufacturing"

Transcription

1 Proposal of Multi-Agent based Model for Dynamic Scheduling in Manufacturing ANA MADUREIRA JOAQUIM SANTOS Computer Science Department Institute of Engineering - Polytechnic of Porto GECAD Knowledge Engineering and Decision Support Research Group Porto, Portugal Abstract: - 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. Key-Words: - Multi-Agent Systems, Dynamic and Distributed Scheduling, Meta-Heuristics, Manufacturing 1 Introduction During the last years it was felt a need to deal with new challenges on Manufacturing Systems. Market globalization and E-business trends are making it critical for manufacturers to integrate sales-order entry and production scheduling. Current global and highly competitive market, enterprises must be aware of momentary market opportunities, and quickly and properly react to customers' demands. These directions include the reduction of order's dimension; increasing product's variety and complexity; client's participation in the development process, providing individual solutions oriented to customer s specific needs, the reduction of product's life cycles; and concurrent execution of manufacturing process activities. Real world scheduling requirements are related with complex systems operated in dynamic environments. This means that they are frequently subject to several kinds of random occurrences and perturbations, such as new job arrivals, machine breakdowns, employee s sickness, jobs cancellation and due date and time processing changes, causing prepared schedules becoming easily outdated and unsuitable. Scheduling under this environment is known as dynamic. Traditional scheduling methods, encounter great difficulties when they are applied to some real-world situations. Several attempts have been made to modify algorithms, to tune them for optimization in a changing environment. A major challenge in the area of global market economy is to develop new techniques for solving real world scheduling problems. Indeed, any industrial organization can only be economically visible by maximizing customer services, maintaining efficient, low cost operations and minimizing total investment. The dynamic scheduling problem is a distributed problem, from logic and structural point of view. In a structural view of the manufacturing systems, the system involves several resources (machines, CNC s, AGV s, conveyors, robots) and several tasks can be carried out at the same time. From a logic point of view this kind of system can be compared to a distributed problem, due to needs of tasks simultaneous processing. Since the manufacturing process is intrinsically dynamic, it is impossible to know the exact structure or topology of the system in advance. The number of products and orders, as well as different alternative production routes, account for the highly complex nature of manufacturing systems. All of the above

2 make the scheduling of manufacturing systems an excellent candidate for the application of agent-based technology. Thus, the requirements for today's and future computer-supported manufacturing systems suggest autonomy, distribution, decentralization and flexibility, while stressing the need for coordination among production units. It is expected that rigid, static and centralized manufacturing systems will give way to systems that are more adaptable to rapid change. The Multi-Agent paradigm represents one of the most promising approaches to building complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature [6][9]. This paper will describe and discuss the application of Multi-Agent Systems (MAS) to the resolution of dynamic and distributed scheduling problems. The remaining sections are organized as follows: Section 2 summarizes some related work and the research on the use of multi-agent technology for dynamic scheduling resolution. In section 3 the scheduling problem under consideration ( Extended Job-Shop Scheduling Problem) is described. Section 4, presents some terms and definitions related with MAS context, and presents the Multi-Agent Model for Dynamic Manufacturing Scheduling. Finally, the paper presents some conclusions and puts forward some ideas for future work. 2 Related Work During the last decades, the organization of mass production has been evolving towards flexible manufacturing and customized products. From a technological point of view, it has been observed that current manufacturing systems have several drawbacks, in particular excessive rigidity and centralization. Furthermore, future manufacturing systems are expected to be characterized by globally distributed production units, small quantities of a large variety of products, the provision of individual solutions tailored to each customer s specific needs, and concurrent execution of all the activities in the manufacturing process [14]. In the case of scheduling systems we need to operate through the cooperation of many interacting subsystems, each with its own objective, and modes of operation. Multi-Agent approach appears to be well suited to complex scheduling problems, especially those with a good deal of interaction between components, and for which static methods cannot provide efficient results. One important issue in the manufacturing research area is that the schedule itself is only valid until the first disturbance (new job arrivals, machine breakdowns, employees sickness, jobs cancellation and due date and time processing change, etc.). Since manufacturing control and execution is a real time application, the need to find a feasible solution, incorporating the recent perturbations, is much greater than to find a optimal solution. The system as a whole must reach a stable and feasible schedule without too much interruption of the shop floor. The problem of dynamic scheduling is one that is receiving increasing attention amongst both researchers and practitioners. In spite of all the previous trials the scheduling problem still known to be NP-complete. This fact incites researchers to explore new directions. Multi-Agent technology has been considered as an important approach for developing industrial distributed systems. Considering the complexity inherent to the manufacturing systems, the dynamic scheduling is considered an excellent candidate for the application of agent-based technology. Ramos [3] proposed a structure in which task agents and resource agents are coordinated by a task manager and a resource manager, respectively. Shaw et al. [9] were one of the first to propose the use of agents in manufacturing for scheduling and factory control. Yams has presented is another of the earliest agent based manufacturing system which assigns an agent to each node in a control hierarchy [4]. Rabelo et al. [12], have proposed a multiagent autonomous architecture for dynamic scheduling in FMS where resources are represented by agents. The resource agents are responsible for scheduling the resources and they have no control over each other. Henseler [5] proposed the distribution of the schedule among several independently running agents: order agents and machine agents. Sousa [11] proposed a holonic architecture for scheduling in manufacturing systems in which tasks and resources are represented by holons and used the CNP for scheduling/rescheduling of tasks. Many multi-agent based approaches have been developed. For further works developed on MAS for dynamic scheduling, see for example, [4][5][6][7][10]. 3 Extended Job-Shop Scheduling Problem Definition Most real-world multi-operation scheduling problems can be described as dynamic and extended versions of the classic or basic Job-Shop scheduling combinatorial optimization problem. The general

3 Job-Shop Scheduling Problem (JSSP) of size n x m can generally be described as a decision-making process concerning about the allocation of a limited set of m={0, 1,,m} resources over time to perform a set of n={0, 1,, n} tasks or jobs. Most real-world multi-operation scheduling problems can be described as dynamic and extended versions of the classic or basic Job-Shop scheduling combinatorial optimization problem. In this work we consider several extensions and additional constraints to the classic JSSP, namely: the existence of different job release dates; the existence of different job due dates; the possibility of job priorities; a machine could process more than one operation in the same job (recirculation); the existence of alternative machines; precedence constraints among operations of the different jobs are common because, most of the times, mainly in discrete manufacturing, products are made of several components that can be seen as different jobs whose manufacturing must be coordinated; the existence of operations on the same job, on different parts and components, processed simultaneously on different machines, followed by components assembly operations, which characterizes Extended Job-Shop Scheduling Problem (EJSSP)[1][2]. Therefore, in this work, we define a job as a manufacturing order for a final item that could be Simple or Complex. It may be simple, like a part, requiring a set of operations to be processed. We call it a Simple Product or Simple Final Item. Complex Final Items, requiring processing of several operations on a number of parts followed by assembly operations at several stages, are also dealt with. Moreover, in practice, scheduling environment tend to be dynamic, i.e. new jobs arrive at unpredictable intervals, machines breakdown, jobs are cancelled and due dates and processing times change frequently. 4 Multi-Agent based Scheduling System Considering that centralized scheduling is therefore large, complex, and difficult to maintain and reconfigure. On the other hand, the inherent nature of much industrial and service process is distributed we will try solving the complex dynamic scheduling problems in a distributed way using the Multi-Agent paradigm. Modeling 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. In Multi-Agent System for Dynamic Manufacturing Scheduling using Meta-Heuristics (MADynScheMH) System we will model a Manufacturing Systems by means of a Multi-Agent Systems, where each agent may represent a processing entity (e.g. a machine). The system has as objective to deal with the complex problem of Dynamic Scheduling in Manufacturing Systems. We want to prove that a good global solution for a scheduling problem may emerge from a community of machine agents solving locally their schedules and cooperating with other machine agents that share some relations between the operations/jobs (e.g. a precedence relation). Meta-Heuristics (Tabu Search or Genetic Algorithms) can be adapted to deal with dynamic problems, reusing and changing solutions/populations in accordance with the dynamism of the Manufacturing System. The self-parameterization of the Meta-Heuristics allows a better adaptation to the situation being considered. The idea is that each agent adopt and provides the self-parameterization in accordance with the problem being solved (the method and parameters can change in run-time). Meta-Heuristics (e.g. Tabu Search and Genetic Algorithms) are adequate for static problems, however real scheduling problems are quite dynamic (new orders arriving, being cancelled, machine delays or faults, etc.). 4.1 MAS Terms and Definitions The definition of the term Agent has not common consent. In the last few years most authors agreed that this definition depends on the domain where agents are used. In Ferber [6] is proposed a definition: An agent is a virtual or physical autonomous entity which performs a given task using information gleaned from its environment to act in a suitable manner so as to complete the task successfully. The agent should be able to adapt itself based on changes occurring in its environment, so that a change in circumstances will still yield the intended result." An agent is generally characterized by the following properties [6] [8][10]: Autonomy - agents can work without a constant external intervention. They are able to take decisions by their own and to maintain control about their actions and internal state. Social ability - agents interact or communicate with other agents, in order to obtain better solutions for their problems. Reactivity - agents have the capability of acquire information and knowledge from their environment. In presence of this kind of

4 information they can respond to it in order to improve is own performance. Pro-activity - agents can have their own objectives. In order to accomplish them they are able to adapt their own behavior to take the initiative. Agents are capable of handle heavy and complex tasks. All the important decisions about how and where to perform their own tasks must be made by the agent Temporal continuity: agents are continuously running processes. Mobility: an agent has the ability to transport itself from one computer to another, retaining its current state. A Multi-Agent System (MAS) can be defined as a system composed by population of autonomous agents, which cooperate with each other to reach common objectives, while simultaneously each agent pursues individual objectives" [6]. The main advantages of a Multi-Agent system are the abilities of coordination and cooperation in order to accomplish a common objective. We can see MAS like a society of agents that cooperates to work in the best way possible. With this we gain the ability of solve complex problems like dynamic and distributed scheduling. Considering the complexity inherent to the manufacturing systems, the dynamic scheduling is considered an excellent candidate for the application of agent-based technology. In many implementations of multi-agent systems for manufacturing scheduling, the agents model the resources of the system and the tasks scheduling is done in a distributed way by means of cooperation and coordination amongst agents [8]. There are also approaches that use a single agent for scheduling (centralized scheduling algorithm) that defines the schedules that the resource agents will execute [4][8][11]. When responding to disturbances, the distributed nature of multi-agent systems can also be a benefit to the rescheduling algorithm by involving only the agents directly affected, without disturbance to the rest of the community that can continue with their work. 4.2 Multi-Agent based Scheduling System The agent technology can be considered an important approach when we are trying to solve dynamic and distributed scheduling systems. Multi-Agent systems can easily respond to scheduling disruptions, adapting the entire plan in order to turn them feasible and with the capacity of respond to the organization needs. This kind of approach is able of generate good global solutions, because they operate like an organized society, where each one knows what must do. Their capabilities of change the behavior in order to take the initiative of perform something without any human intervention; allow the creation of intelligent autonomous systems that are able of conducting their own activities to fulfill their directives and objectives. As we can see in the Figure 1, the use of Multi- Agent systems to solve dynamic and distributed scheduling, permits to obtain a cooperative society of agents which purpose is searching for the best schedule. Figure 1 Proposed Multi-Agent Model As objectives of the Multi-Agent System for Dynamic Manufacturing Scheduling using Meta- Heuristics (MADynScheMH) we identify the following assertions: Manufacturing Systems are well modeled by means of Multi-Agent Systems, where each agent may represent a processing entity in the Manufacturing System (e.g. a machine) A global solution for a scheduling problem may emerge from a community of machine agents solving locally their schedules and cooperating with other machine agents that shares some relations between the operations/jobs (e.g. a precedence relation) Meta-Heuristics can be adapted to deal with dynamic problems, reusing and adapting individuals/populations in accordance with the dynamism of the Manufacturing System The self parameterization of the Meta-Heuristics will allow a better adaptation to the situation being considered. The main purpose is to create a Multi-Agent system where each agent represents a resource (machine) in a Manufacturing System (Machine Agents). Each machine agent is able to obtain

5 schedules for manufacturing orders based on Meta- Heuristics (e.g. Genetic Algorithms or Tabu Search). Each machine agent is able to change the parameters of the basic algorithms according to the current situation or even to commutate from one algorithm to the other if the last start to be preferable. Meta- Heuristics algorithms used in the Machine Agents will be prepared to handle dynamism (new jobs arriving, cancelled jobs, changing jobs attributes) by adapting the solutions or population elements. A coordination mechanism will be established between machine agents involved in the execution of operations (jobs) with precedence relations in order to deal with the feasibility of the generated schedules. In a real manufacturing system a product is produced, step by step, passing in several machines. In each machine it will be performed one operation (job) of the process plan. Figure 2 Multi-Agent Architecture In this model we will use a Repair Approach, where the system wait for the solutions obtained by the machine agents and then apply a repair mechanism to shift some operations in the generated schedules till a feasible solution is obtained because we want to validate the system evolution. These method allow us to repair the integrated solution, in order to turn it feasible. It implicates the evaluation of all sequences in order to coordinate them. These coordination mechanisms are prepared to accept agents subjected to dynamism (new jobs arriving, cancelled jobs, changing jobs attributes, etc.). Coordination and Repair Mechanisms are used to guarantee the feasibility of schedules. Notice that solving locally problems and joining them will not guarantee the feasibility of schedules (e.g. precedence relations could not be guaranteed). The coordination mechanism will be established between machine agents involved in the execution of operations (jobs) with precedence relations in order to deal with the feasibility of the generated schedules in run-time. A repair mechanism will allow solving problems found in the feasibility of the schedules, when these problems are not solved by the coordination mechanism. The proposed architecture involves three types of agents: the Task Manager, The Resource Agent and the Coordinator Agent. The Task Manager Agent (TMA) is responsible for the coordination of all tasks in the system. The resource agent (RA) has the capability of generate scheduling plans for him. The generation of the plan can be made with the application of some Meta-Heuristics that can help this process. This plan is generated in accordance with the knowledge that he has about is own processing capabilities and the needs of the system. The coordinator agent (CA) is responsible for the coordination of a group of agent s resources. This corresponds to a distributed architecture where the generation of the global plan is made in small pieces, distributed for all the agents in the system. A distributed approach like this has some important problems to solve, like can all the pieces be joined without the application of o validation method, that can say if the plan is feasible or not. Most of the times the scheduling plans generated by each resource agent are not compatible because when it was generated the information of all the other resources in the system was not considered. To solve this problem the coordinator agent is able of change and repair the plans of is own group of agent in order to obtain a feasible plan. If in any time one agent obtains new information that can possibly turn the schedule obsolete, it will generate a new scheduling plan and it will communicate it with is own coordinator that will take all the needed measure to create a new global feasible scheduling plan. This task can be made in two different ways: the resource agent creates a totally new plan able of responds to new challenges made by the system. This can be called rescheduling. This way can be problematic if the system is exposed to many disruptions in short periods of time, because the agent will be obliged to generate several plans. The other possible way is the application of repair methods to the existing plan. This oblige that the agent must evaluate the existing plan to decide what changes must be made to respond to the real time information acquired. This can be characterized like dynamic approach that is always ready to adapt and create plan to accomplish real time information that is arrived at the system [1][2].

6 4.3 Communication System All agents present in the system can communicate using a communications network, but they are not able to take decisions about any internal problem of other agents [1]. Each agent is completely autonomous in their own decisions, but if a decision changes something that can be important for the other agents in the system, the agent must give that information to the others. The communication method can be considered similar to the method proposed by Hino [13]. Event notification from previous operation Reaction to the perturbation in the previous operation Event or perturbation Agent MH1 Event notification to next operation Reaction to the perturbation in the next operation Figure 3 Information Processing in an Agent [1] 5 Concluding Remarks and Future Work Manufacturing systems are changing its structure and organization. Supply chains are evolving to more coupled organizations like virtual enterprises, though maintaining the single entities autonomy, adaptability and dynamism properties. The Multi-Agent paradigm represents one of the most promising approaches for building complex, flexible, and cost-effective scheduling systems because of its distributed and dynamic nature. This paper have described and discussed the application of Multi-Agent systems to the resolution of dynamic and distributed scheduling problems. In this paper a multi-agent model for support dynamic scheduling in manufacturing environments has been proposed. In MADynScheMH system we model a Manufacturing Systems by means of a Multi- Agent Systems, where each agent may represent a processing entity (resource). We consider that a good global solution for a scheduling problem may emerge from a community of machine agents solving locally their schedules and cooperating with other machine agents that shares some relations between the operations/jobs). The self parameterization of the Meta-Heuristics will allow a better adaptation to the situation being considered (related with the problem dimension and neighbourhood/population size). References: [1] Ana M. Madureira, Meta-Heuristics Application to Scheduling in Dynamic Environments of Discrete Manufacturing, PhD Thesis, University of Minho, 2003 (in portuguese). [2] Ana Madureira, Carlos Ramos, Silvio C. Silva, Toward Dynamic Scheduling Through Evolutionary Computing, WSEAS Transactions on Systems, Issue 4, Volume 3, ISSN , pp , [3] Carlos Ramos, An architecture and a negation protocol for the dynamic scheduling of Manufacturing Systems, IEEE Int. Conference on robotics and automation, Vol. 4, [4] H. Van Parunak, Manufacturing experience with contract net, Distributed Artificial Intelligence Huhns, M,N., ed., Pitman, pp , [5] H. Henseler, From reactive to active scheduling by using multi-agents, University Oldenburg, Germany, [6] J. Ferber, Les Sístemes multi-agents: versune intelligence collective. Interedition, [7] J. Liu and K.P. Sycara, Distributed problem solving through coordination in a society of agents, In proceedings of the 13 th International workshop on DAI, [8] M. Wooldridge, An Introduction to Multiagent Systems, John Wiley and Sons, [9] M.J. Shaw and A.B. Whinston, Distributed planning in cellular flexible manufacturing systems, Tech Report, Management Information Research Center, Purdue University, [10] P. Cowling, D, Ouelhadj, S. Petrovic, Multi- Agent Systems for Dynamic Scheduling, [11] P. Sousa, C. Ramos, and J. Neves, The Fabricare scheduling prototype suite: Agent interaction and knowledge base, Journal of Intelligent Manufacturing, 14(5): , [12] R. Rabelo, L. M. Camarinha-Matos and H. Afsarmanesh, Multi agent perspectives to agile scheduling, Intelligent Systems for Manufacturing, Multi-agent Systems and Virtual Organizations, edited by Luis M. Camararinha-Matos Hamideh, Afsarmanesh Vladimir Marik, [13] Rei Hino, Kouichi Izuhara, Toshimichi Moriwaki, Message Exchange Method for Decentralized Scheduling, 4th IEEE International Symposium on Assembly and Task Planning, Japan, , [14] Visionary Manufacturing Challenges for 2020, Committee on Visionary Manufacturing Challenges, National Research Council, National Academic Press, 1999.

Cooperative Intelligent System for Manufacturing Scheduling

Cooperative Intelligent System for Manufacturing Scheduling Cooperative Intelligent System for Manufacturing Scheduling ANA MADUREIRA JOAQUIM SANTOS IVO PEREIRA GECAD Knowledge Engineering and Decision Support Group Institute of Engineering Polytechnic of Porto

More information

Optimizing Dynamic Flexible Job Shop Scheduling Problem Based on Genetic Algorithm

Optimizing Dynamic Flexible Job Shop Scheduling Problem Based on Genetic Algorithm International Journal of Current Engineering and Technology E-ISSN 2277 4106, P-ISSN 2347 5161 2017 INPRESSCO, All Rights Reserved Available at http://inpressco.com/category/ijcet Research Article Optimizing

More information

Using Genetic Algorithms for Dynamic Scheduling

Using Genetic Algorithms for Dynamic Scheduling Using Genetic Algorithms for Dynamic Scheduling Ana Madureira * Carlos Ramos * Sílvio do Carmo Silva anamadur@dei.isep.ipp.pt,, csr@dei.isep.ipp.pt, scarmo@dps.uminho.pt 1 Institute of Engineering Polytechnic

More information

An Agent-Based Scheduling Framework for Flexible Manufacturing Systems

An Agent-Based Scheduling Framework for Flexible Manufacturing Systems An Agent-Based Scheduling Framework for Flexible Manufacturing Systems Iman Badr International Science Index, Industrial and Manufacturing Engineering waset.org/publication/2311 Abstract The concept of

More information

A Simulation Platform for Multiagent Systems in Logistics

A Simulation Platform for Multiagent Systems in Logistics A Simulation Platform for Multiagent Systems in Logistics Heinz Ulrich, Swiss Federal Institute of Technology, Zürich Summary: The challenges in today s global economy are flexibility and fast reactions

More information

Designing an Effective Scheduling Scheme Considering Multi-level BOM in Hybrid Job Shop

Designing an Effective Scheduling Scheme Considering Multi-level BOM in Hybrid Job Shop Proceedings of the 2012 International Conference on Industrial Engineering and Operations Management Istanbul, Turkey, July 3 6, 2012 Designing an Effective Scheduling Scheme Considering Multi-level BOM

More information

HOLONIC CONTROL OF AN ENGINE ASSEMBLY PLANT AN INDUSTRIAL EVALUATION

HOLONIC CONTROL OF AN ENGINE ASSEMBLY PLANT AN INDUSTRIAL EVALUATION HOLONIC CONTROL OF AN ENGINE ASSEMBLY PLANT AN INDUSTRIAL EVALUATION Stefan BUSSMANN and Jörg SIEVERDING DaimlerChrysler AG Research and Technology 3 Alt-Moabit 96a, 10559 Berlin, Germany {Stefan.Bussmann,

More information

MEDIATOR-BASED COMMUNICATION, NEGOTIATION AND SCHEDULING FOR DECENTRALISED PRODUCTION MANAGEMENT

MEDIATOR-BASED COMMUNICATION, NEGOTIATION AND SCHEDULING FOR DECENTRALISED PRODUCTION MANAGEMENT MEDIATOR-BASED COMMUNICATION, NEGOTIATION AND SCHEDULING FOR DECENTRALISED PRODUCTION MANAGEMENT I. Seilonen 1, G. Teunis 2, P. Leitão 3 1 VTT Automation, ilkka.seilonen@vtt.fi, Finland 2 University of

More information

FACULTATEA DE INGINERIE HERRMANN OBERTH MASTER-PROGRAM EMBEDDED SYSTEMS. Plant Control

FACULTATEA DE INGINERIE HERRMANN OBERTH MASTER-PROGRAM EMBEDDED SYSTEMS. Plant Control FACULTATEA DE INGINERIE HERRMANN OBERTH MASTER-PROGRAM EMBEDDED SYSTEMS Plant Control PROFESSOR: PROF. DR. ING. ZAMFIRESCU STUDENT: STEFAN FEILMEIER - 03.12.2014 - Agent-based modeling and simulation of

More information

Report with the Requirements of Multi-Agent Architecture for Line-production Systems and Production on Demand

Report with the Requirements of Multi-Agent Architecture for Line-production Systems and Production on Demand integration of process and quality Control using multi-agent technology Work Package 1 Multi-Agent Architecture Deliverable D1.1 Report with the Requirements of Multi-Agent Architecture for Line-production

More information

We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors

We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists. International authors and editors We are IntechOpen, the world s leading publisher of Open Access books Built by scientists, for scientists 4,000 116,000 120M Open access books available International authors and editors Downloads Our

More information

Analysis of Agile and Multi-Agent Based Process Scheduling Model

Analysis of Agile and Multi-Agent Based Process Scheduling Model International Refereed Journal of Engineering and Science (IRJES) ISSN (Online) 2319-183X, (Print) 2319-1821 Volume 4, Issue 8 (August 2015), PP.23-31 Analysis of Agile and Multi-Agent Based Process Scheduling

More information

Paper 30 Centralized versus Market-based Task Allocation in the Presence of Uncertainty

Paper 30 Centralized versus Market-based Task Allocation in the Presence of Uncertainty Paper 30 Centralized versus Market-based Task Allocation in the Presence of Uncertainty Abstract While there have been some efforts to compare centralized versus market based approaches to general task

More information

Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds.

Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds. Proceedings of the 2012 Winter Simulation Conference C. Laroque, J. Himmelspach, R. Pasupathy, O. Rose, and A. M. Uhrmacher, eds. A SIMULATION-BASED LEAN PRODUCTION APPROACH AT A LOW-VOLUME PARTS MANUFACTURER

More information

W911NF Project - Mid-term Report

W911NF Project - Mid-term Report W911NF-08-1-0041 Project - Mid-term Report Agent Technology Center, Czech Technical University in Prague Michal Pechoucek 1 Accomplishments for the First 6 Months 1.1 Scenario and Demos During the first

More information

SimBa: A Simulation and Balancing System for Manual Production Lines

SimBa: A Simulation and Balancing System for Manual Production Lines 19 SimBa: A Simulation and Balancing System for Manual Production Lines Isabel C. Pra9a, Adriano S. Carvalho Faculdade de Engenharia da Universidade do Porto Instituto de Sistemas e Rob6tica - Grupo de

More information

Computational Complexity and Agent-based Software Engineering

Computational Complexity and Agent-based Software Engineering Srinivasan Karthikeyan Course: 609-22 (AB-SENG) Page 1 Course Number: SENG 609.22 Session: Fall, 2003 Course Name: Agent-based Software Engineering Department: Electrical and Computer Engineering Document

More information

International Journal of Advanced Engineering Technology E-ISSN

International Journal of Advanced Engineering Technology E-ISSN International Journal of Advanced Engineering Technology E-ISSN 976-3945 Research Paper A SIMULATION STUDY AND ANALYSIS OF JOB RELEASE POLICIES IN SCHEDULING A DYNAMIC FLEXIBLE JOB SHOP PRODUCTION SYSTEM

More information

Towards an Approach to Model Business Processes using Workflow Modeling Techniques in Production Systems

Towards an Approach to Model Business Processes using Workflow Modeling Techniques in Production Systems Proceedings of the 34th Hawaii International Conference on System Sciences - 200 Towards an Approach to Model Business Processes using Workflow Modeling Techniques in Production Systems Ricardo M. Bastos,

More information

A BIO-INSPIRED SOLUTION FOR MANUFACTURING CONTROL SYSTEMS

A BIO-INSPIRED SOLUTION FOR MANUFACTURING CONTROL SYSTEMS 33 A BIO-INSPIRED SOLUTION FOR MANUFACTURING CONTROL SYSTEMS Paulo Leitão Polytechnic Institute of Bragança, Quinta Sta Apolónia, Apartado 1134, 5301-857 Bragança, Portugal, pleitao@ipb.pt Manufacturing

More information

TRENDS IN MODELLING SUPPLY CHAIN AND LOGISTIC NETWORKS

TRENDS IN MODELLING SUPPLY CHAIN AND LOGISTIC NETWORKS Advanced OR and AI Methods in Transportation TRENDS IN MODELLING SUPPLY CHAIN AND LOGISTIC NETWORKS Maurizio BIELLI, Mariagrazia MECOLI Abstract. According to the new tendencies in marketplace, such as

More information

Intelligent Workflow Management: Architecture and Technologies

Intelligent Workflow Management: Architecture and Technologies Proceedings of The Third International Conference on Electronic Commerce(ICeCE2003), Hangzhou Oct. 2003, pp.995-999 Intelligent Workflow Management: Architecture and Technologies Chen Huang a, Yushun Fan

More information

Implementation and Validation of a Holonic Manufacturing Control System

Implementation and Validation of a Holonic Manufacturing Control System 400 Flexible Automation & Intelligent Manufacturing, FAIM2005, Bilbao, Spain Implementation and Validation of a Holonic Manufacturing Control System Paulo Leitão 1, Francisco Restivo 2 1 Department of

More information

AMulti-AgentDesignforaHomeAutomation System dedicated to power management

AMulti-AgentDesignforaHomeAutomation System dedicated to power management AMulti-AgentDesignforaHomeAutomation System dedicated to power management Shadi ABRAS 1,Stéphane PLOIX 2,SylviePESTY 1,andMireille JACOMINO 2 1 Laboratoire LIG-Institut IMAG,CNRS, UMR5217, 2 Laboratoire

More information

Iterative Constraint-based Repair for Multiagent Scheduling

Iterative Constraint-based Repair for Multiagent Scheduling From: AAAI Technical Report WS-97-05. Compilation copyright 1997, AAAI (www.aaai.org). All rights reserved. Iterative Constraint-based Repair for Multiagent Scheduling Kazuo Miyashita ETL (Electrotechnical

More information

DEADLOCK AVOIDANCE AND RE-ROUTING OF AUTOMATED GUIDED VEHICLES (AGVS) IN FLEXIBLE MANUFACTURING SYSTEMS (FMS)

DEADLOCK AVOIDANCE AND RE-ROUTING OF AUTOMATED GUIDED VEHICLES (AGVS) IN FLEXIBLE MANUFACTURING SYSTEMS (FMS) DEADLOCK AVOIDANCE AND RE-ROUTING OF AUTOMATED GUIDED VEHICLES (AGVS) IN FLEXIBLE MANUFACTURING SYSTEMS (FMS) MD. Saddam Hussain 1, B. Satish Kumar 2, Dr. G.Janardhana Raju 3 Email: 1 Saddam.mohd321@gmail.com,

More information

Decision support system for virtual organization management

Decision support system for virtual organization management Decision support system for virtual organization management J. Hodík a, J. Vokřínek a, R. Hofman b a Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University, Technická

More information

INTEGRATING PROCUREMENT, PRODUCTION PLANNING, AND INVENTORY MANAGEMENT PROCESSES THROUGH NEGOTIATION INFORMATION

INTEGRATING PROCUREMENT, PRODUCTION PLANNING, AND INVENTORY MANAGEMENT PROCESSES THROUGH NEGOTIATION INFORMATION 26 INTEGRATING PROCUREMENT, PRODUCTION PLANNING, AND INVENTORY MANAGEMENT PROCESSES THROUGH NEGOTIATION INFORMATION Giuseppe Confessore 1, Silvia Rismondo 1,2 and Giuseppe Stecca 1,2 1 Istituto di Tecnologie

More information

A Case Study of Capacitated Scheduling

A Case Study of Capacitated Scheduling A Case Study of Capacitated Scheduling Rosana Beatriz Baptista Haddad rosana.haddad@cenpra.gov.br; Marcius Fabius Henriques de Carvalho marcius.carvalho@cenpra.gov.br Department of Production Management

More information

ISE480 Sequencing and Scheduling

ISE480 Sequencing and Scheduling ISE480 Sequencing and Scheduling INTRODUCTION ISE480 Sequencing and Scheduling 2012 2013 Spring term What is Scheduling About? Planning (deciding what to do) and scheduling (setting an order and time for

More information

Multilevel Order Decomposition in Distributed Production

Multilevel Order Decomposition in Distributed Production Multilevel Order Decomposition in Distributed Production Daniela Wünsch SAP AG, SAP Research CEC Dresden Chemnitzer Straße 48 D-01159 Dresden, Germany daniela.wuensch@sap.com Aleksey Bratukhin Austrian

More information

A Dynamic Multi Agent based scheduling for flexible flow line manufacturing system accompanied by dynamic customer demand

A Dynamic Multi Agent based scheduling for flexible flow line manufacturing system accompanied by dynamic customer demand A Dynamic Multi Agent based scheduling for flexible flow line manufacturing system accompanied by dynamic customer demand Danial Roudi Department of Mechanical Engineering, Eastern Mediterranean University,

More information

Proactive approach to address robust batch process scheduling under short-term uncertainties

Proactive approach to address robust batch process scheduling under short-term uncertainties European Symposium on Computer Arded Aided Process Engineering 15 L. Puigjaner and A. Espuña (Editors) 2005 Elsevier Science B.V. All rights reserved. Proactive approach to address robust batch process

More information

Production Management Modelling Based on MAS

Production Management Modelling Based on MAS International Journal of Automation and Computing 7(3), August 2010, 336-341 DOI: 10.1007/s11633-010-0512-x Production Management Modelling Based on MAS Li He 1 Zheng-Hao Wang 2 Ke-Long Zhang 3 1 School

More information

Analysis and Modelling of Flexible Manufacturing System

Analysis and Modelling of Flexible Manufacturing System Analysis and Modelling of Flexible Manufacturing System Swetapadma Mishra 1, Biswabihari Rath 2, Aravind Tripathy 3 1,2,3Gandhi Institute For Technology,Bhubaneswar, Odisha, India --------------------------------------------------------------------***----------------------------------------------------------------------

More information

DEALING WITH MASS AUTOMATION

DEALING WITH MASS AUTOMATION IT TRANSFORMATION DEALING WITH MASS AUTOMATION There is a recognised shift in the industry towards widespread/mass automation. Although this is not a recent concept, the technologies and techniques to

More information

Multi-Agent Model for Power System Simulation

Multi-Agent Model for Power System Simulation Multi-Agent Model for Power System Simulation A.A.A. ESMIN A.R. AOKI C.R. LOPES JR. G. LAMBERT-TORRES Institute of Electrical Engineering Federal School of Engineering at Itajubá Av. BPS, 1303 Itajubá/MG

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 FACILITY LAYOUT DESIGN Layout design is nothing but the systematic arrangement of physical facilities such as production machines, equipments, tools, furniture etc. A plant

More information

Multiple Products Partner Selection Model of Virtual Enterprise based on Multi-agent Systems

Multiple Products Partner Selection Model of Virtual Enterprise based on Multi-agent Systems , July 6-8, 2011, London, U.K. Multiple Products Partner Selection Model of Virtual Enterprise based on Multi-agent Systems Chunxia Yu, T. N. Wong Abstract Partner selection of virtual enterprise is the

More information

University of Groningen. Effective monitoring and control with intelligent products Meyer, Gerben Gerald

University of Groningen. Effective monitoring and control with intelligent products Meyer, Gerben Gerald University of Groningen Effective monitoring and control with intelligent products Meyer, Gerben Gerald IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish

More information

A Real-Time Production Scheduling Framework based on Autonomous Agents

A Real-Time Production Scheduling Framework based on Autonomous Agents A Real-Time Production Scheduling Framework based on Autonomous Agents Kwan Hee Han, Yongsun Choi and Sung Moon Bae Abstract The function of production scheduling is to provide the release and execution

More information

Sequencing and Scheduling of Jobs and Tools in a Flexible Manufacturing System using Jaya Algorithm

Sequencing and Scheduling of Jobs and Tools in a Flexible Manufacturing System using Jaya Algorithm Sequencing and Scheduling of Jobs and Tools in a Flexible Manufacturing System using Jaya Algorithm Modapothula Chaithanya 1, N Siva Rami Reddy 2, P Ravindranatha Reddy, 1 PG Student, Dept of Mechanical,

More information

SIMUL8-PLANNER FOR COMPOSITES MANUFACTURING

SIMUL8-PLANNER FOR COMPOSITES MANUFACTURING Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds. SIMUL8-PLANNER FOR COMPOSITES MANUFACTURING Kim Hindle Project

More information

SOA Concepts. Service Oriented Architecture Johns-Hopkins University

SOA Concepts. Service Oriented Architecture Johns-Hopkins University SOA Concepts Service Oriented Architecture Johns-Hopkins University 1 Lecture 2 Goals To learn the basic concepts behind SOA The roots of SOA: the history from XML to SOA, and the continuing evolution

More information

Strictly as per the compliance and regulations of:

Strictly as per the compliance and regulations of: Mechanical and Mechanics Engineering Volume 12 Issue 5 Version 1.0 Y ear 2012 Type: Double Blind Peer Reviewed International Research Journal Publisher: Global Journals Inc. (USA) Online ISSN: 2249-4596

More information

CAD/CAM CHAPTER ONE INTRODUCTION. Dr. Ibrahim Naimi

CAD/CAM CHAPTER ONE INTRODUCTION. Dr. Ibrahim Naimi CAD/CAM CHAPTER ONE INTRODUCTION Dr. Ibrahim Naimi Production System Facilities The facilities in the production system are the factory, production machines and tooling, material handling equipment,

More information

Lectures 2 & 3. Software Processes. Software Engineering, COMP201 Slide 1

Lectures 2 & 3. Software Processes. Software Engineering, COMP201 Slide 1 Lectures 2 & 3 Software Processes Software Engineering, COMP201 Slide 1 What is a Process? When we provide a service or create a product we always follow a sequence of steps to accomplish a set of tasks

More information

Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System

Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System International Journal of Automation and Computing 7(4), November 2010, 596-602 DOI: 10.1007/s11633-010-0545-1 Modeling of Agile Intelligent Manufacturing-oriented Production Scheduling System Zhong-Qi

More information

Modelling Languages Restrictions: A Comparative Study of ArchiMate and SOMF

Modelling Languages Restrictions: A Comparative Study of ArchiMate and SOMF Modelling Languages Restrictions: A Comparative Study of ArchiMate and SOMF João Gonçalves Henriques 1, Pedro Carmo Oliveira 2 and Miguel Mira da Silva 1 1 Instituto Superior Técnico, Portugal {joaoltghenriques,

More information

7/8/2017 CAD/CAM. Dr. Ibrahim Al-Naimi. Chapter one. Introduction

7/8/2017 CAD/CAM. Dr. Ibrahim Al-Naimi. Chapter one. Introduction CAD/CAM Dr. Ibrahim Al-Naimi Chapter one Introduction 1 2 3 Production System Facilities The facilities in the production system are the factory, production machines and tooling, material handling equipment,

More information

Integration of Process Planning and Scheduling Functions for Batch Manufacturing

Integration of Process Planning and Scheduling Functions for Batch Manufacturing Integration of Process Planning and Scheduling Functions for Batch Manufacturing A.N. Saravanan, Y.F. Zhang and J.Y.H. Fuh Department of Mechanical & Production Engineering, National University of Singapore,

More information

Analysis and design of production and control structures

Analysis and design of production and control structures Analysis and design of production and control structures M.J. Verweij and A.J.R. Zwegers Department of Technology Management Eindhoven University of Technology, Pav. U21 P.O. Box 513, 5600 MB Eindhoven,

More information

SCHEDULING is a major decision-making process in

SCHEDULING is a major decision-making process in 38 IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS: SYSTEMS, VOL. 43, NO. 1, JANUARY 2013 Agent-Based Interaction Protocols and Topologies for Manufacturing Task Allocation Mohammad Owliya, Mozafar

More information

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras

Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Operations and Supply Chain Management Prof. G. Srinivasan Department of Management Studies Indian Institute of Technology, Madras Lecture - 24 Sequencing and Scheduling - Assumptions, Objectives and Shop

More information

Multi Agent System for Micro Grid

Multi Agent System for Micro Grid Multi Agent System for Micro Grid Dr. Rajesh Kumar PhD, PDF (NUS, Singapore) SMIEEE, FIETE, MIE (I),LMCSI, SMIACSIT, LMISTE, MIAENG Associate Professor, Department of Electrical Engineering Adjunct Associate

More information

A system architecture for holonic manufacturing planning and control(etoplan)

A system architecture for holonic manufacturing planning and control(etoplan) Robotics and Computer Integrated Manufacturing 18 (2002) 313 318 A system architecture for holonic manufacturing planning and control(etoplan) G. Wullink a,b, *, M.M.T. Giebels a, H.J.J. Kals a a Department

More information

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING

A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING A HYBRID GENETIC ALGORITHM FOR JOB SHOP SCHEUDULING PROF. SARVADE KISHORI D. Computer Science and Engineering,SVERI S College Of Engineering Pandharpur,Pandharpur,India KALSHETTY Y.R. Assistant Professor

More information

The software process

The software process Software Processes The software process A structured set of activities required to develop a software system Specification; Design; Validation; Evolution. A software process model is an abstract representation

More information

Scheduling and Coordination of Distributed Design Projects

Scheduling and Coordination of Distributed Design Projects Scheduling and Coordination of Distributed Design Projects F. Liu, P.B. Luh University of Connecticut, Storrs, CT 06269-2157, USA B. Moser United Technologies Research Center, E. Hartford, CT 06108, USA

More information

STUDY NO 5 INTRODUCTION TO FLEXIBLE MANUFACTURING SYSTEM

STUDY NO 5 INTRODUCTION TO FLEXIBLE MANUFACTURING SYSTEM STUDY NO 5 INTRODUCTION TO FLEXIBLE MANUFACTURING SYSTEM A flexible manufacturing system (FMS) is in which there is some amount of flexibility that allows the system to react in case of changes, whether

More information

Book Outline. Software Testing and Analysis: Process, Principles, and Techniques

Book Outline. Software Testing and Analysis: Process, Principles, and Techniques Book Outline Software Testing and Analysis: Process, Principles, and Techniques Mauro PezzèandMichalYoung Working Outline as of March 2000 Software test and analysis are essential techniques for producing

More information

Introduction to Software Engineering

Introduction to Software Engineering CHAPTER 1 Introduction to Software Engineering Structure 1.1 Introduction Objectives 1.2 Basics of Software Engineering 1.3 Principles of Software Engineering 1.4 Software Characteristics 1.5 Software

More information

A Multi-Agent Design for a Home Automation System dedicated to power management

A Multi-Agent Design for a Home Automation System dedicated to power management A Multi-Agent Design for a Home Automation System dedicated to power management Shadi ABRAS 1, Stéphane PLOIX 2, Sylvie PESTY 1, and Mireille JACOMINO 2 1 Laboratoire LIG-Institut IMAG,CNRS, UMR5217, 2

More information

Supply chain management simulation: an overview

Supply chain management simulation: an overview Supply chain management simulation: an overview Caroline Thierry, Gérard Bel, André Thomas To cite this version: Caroline Thierry, Gérard Bel, André Thomas. Supply chain management simulation: an overview.

More information

CHAPTER 1 INTRODUCTION

CHAPTER 1 INTRODUCTION 1 CHAPTER 1 INTRODUCTION 1.1 MANUFACTURING SYSTEM Manufacturing, a branch of industry, is the application of tools and processes for the transformation of raw materials into finished products. The manufacturing

More information

1. For s, a, initialize Q ( s,

1. For s, a, initialize Q ( s, Proceedings of the 2006 Winter Simulation Conference L. F. Perrone, F. P. Wieland, J. Liu, B. G. Lawson, D. M. Nicol, and R. M. Fujimoto, eds. A REINFORCEMENT LEARNING ALGORITHM TO MINIMIZE THE MEAN TARDINESS

More information

Investigation into Self-Adaptive Software Agents Development

Investigation into Self-Adaptive Software Agents Development Liverpool John Moores University School of Computing and Mathematical Sciences Investigation into Self-Adaptive Software Agents Development E. Grishikashvili Distributed Multimedia Systems Engineering

More information

MES ERP. Critical Manufacturing, 2015

MES ERP. Critical Manufacturing, 2015 MES vs ERP Critical Manufacturing, 2015 Defining MES Loosening the categories The case for modular MES Modular MES in practice Strategic enterprise integration still matters 3 6 7 8 9 Originally written

More information

Genetic Algorithm for Flexible Job Shop Scheduling Problem - a Case Study

Genetic Algorithm for Flexible Job Shop Scheduling Problem - a Case Study Genetic Algorithm for Flexible Job Shop Scheduling Problem - a Case Study Gabriela Guevara, Ana I. Pereira,, Adriano Ferreira, José Barbosa and Paulo Leitão, Polytechnic Institute of Bragança, Campus Sta

More information

Dynamic Management Architecture for Project Based Production

Dynamic Management Architecture for Project Based Production Dynamic Management Architecture for Project Based Production Akira Tsumaya 1, Yuta Matoba 2, Hidefumi Wakamatsu 2 and Eiji Arai 2 1 Kobe University, Department of Mechanical Engineering, Graduate School

More information

DEVELOPMENT AND INVESTIGATION OF A SIMULATION BASED EXPERT SYSTEM FOR DYNAMIC RESCHEDULING OF AN INTEGRATED JOB SHOP

DEVELOPMENT AND INVESTIGATION OF A SIMULATION BASED EXPERT SYSTEM FOR DYNAMIC RESCHEDULING OF AN INTEGRATED JOB SHOP Association for Information Systems AIS Electronic Library (AISeL) AMCIS 2002 Proceedings Americas Conference on Information Systems (AMCIS) December 2002 DEVELOPMENT AND INVESTIGATION OF A SIMULATION

More information

Introduction to Systems Analysis and Design

Introduction to Systems Analysis and Design Introduction to Systems Analysis and Design What is a System? A system is a set of interrelated components that function together to achieve a common goal. The components of a system are called subsystems.

More information

Autonomic Approach to Planning and Scheduling in Networked Small Factories

Autonomic Approach to Planning and Scheduling in Networked Small Factories Autonomic Approach to Planning and Scheduling in Networked Small Factories Flavio Bonfatti 1, Luca Martinelli 1, and Paola Daniela Monari 2 1 DII, University of Modena and Reggio Emilia,Via Vignolese 905,

More information

Evaluation of Modeling Techniques for Agent- Based Systems

Evaluation of Modeling Techniques for Agent- Based Systems A tutorial report for SENG 609.22 Agent Based Software Engineering Course Instructor: Dr. Behrouz H. Far Evaluation of Modeling Techniques for Agent- Based Systems Prepared by: Wei Shen ABSTRACT To develop

More information

Downloaded from edlib.asdf.res.in

Downloaded from edlib.asdf.res.in ASDF India Proceedings of The Second Intl Conf on Human Machine Interaction 2014 [ICHMI 2014], India 12 A new strategy to the joint production scheduling and maintenance planning under unconventional constraints

More information

Service Oriented Architecture

Service Oriented Architecture Service Oriented Architecture Part I INTRODUCING SOA Service Oriented Architecture- Presented by Hassan.Tanabi@Gmail.com 2 Fundamental SOA 1. The term "service-oriented" has existed for some time, it has

More information

Agents-based Interaction Protocols and Topologies in Manufacturing Task Allocation

Agents-based Interaction Protocols and Topologies in Manufacturing Task Allocation 2010 5th International Conference on System of Systems Engineering Agents-based Interaction Protocols and Topologies in Manufacturing Task Allocation Mohammad Owliya School of Mechanical Engineering University

More information

PROCESS ACCOMPANYING SIMULATION A GENERAL APPROACH FOR THE CONTINUOUS OPTIMIZATION OF MANUFACTURING SCHEDULES IN ELECTRONICS PRODUCTION

PROCESS ACCOMPANYING SIMULATION A GENERAL APPROACH FOR THE CONTINUOUS OPTIMIZATION OF MANUFACTURING SCHEDULES IN ELECTRONICS PRODUCTION Proceedings of the 2002 Winter Simulation Conference E. Yücesan, C.-H. Chen, J. L. Snowdon, and J. M. Charnes, eds. PROCESS ACCOMPANYING SIMULATION A GENERAL APPROACH FOR THE CONTINUOUS OPTIMIZATION OF

More information

A Multi-Agent Design for a Home Automation System dedicated to power management

A Multi-Agent Design for a Home Automation System dedicated to power management A Multi-Agent Design for a Home Automation System dedicated to power management Shadi ABRAS\ Stephane PLOIX^ Sylvie PESTY\ and Mireille JACOMINO^ ^ Laboratoire LIG-Institut IMAG,CNRS, UMR5217, '^ Laboratoire

More information

Soa Readiness Assessment, a New Method

Soa Readiness Assessment, a New Method ISSN : 8-96, Vol., Issue 8( Version ), August 0, pp.- RESEARCH ARTICLE OPEN ACCESS Soa Readiness Assessment, a New Method Ali Mirarab, Najmeh Ghasemi Fard and Abdol Reza Rasouli Kenari Electrical and Computer

More information

OPTIMAL RECLOSER DEPLOYMENT TO LEVERAGE SELF-HEALING: A TECHNO-ECONOMIC ROBUSTNESS ASSESSMENT

OPTIMAL RECLOSER DEPLOYMENT TO LEVERAGE SELF-HEALING: A TECHNO-ECONOMIC ROBUSTNESS ASSESSMENT OPTIMAL RECLOSER DEPLOYMENT TO LEVERAGE SELF-HEALING: A TECHNO-ECONOMIC ROBUSTNESS ASSESSMENT Eduardo RODRIGUES Ismael MIRANDA Nuno SILVA EFACEC Portugal EFACEC Portugal EFACEC - Portugal eduardo.rodrigues@efacec.com

More information

SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES

SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES SCHEDULING AND CONTROLLING PRODUCTION ACTIVITIES Al-Naimi Assistant Professor Industrial Engineering Branch Department of Production Engineering and Metallurgy University of Technology Baghdad - Iraq dr.mahmoudalnaimi@uotechnology.edu.iq

More information

A Flexible Multi-Agent System Architecture for Plant Automation

A Flexible Multi-Agent System Architecture for Plant Automation A Flexible Multi- System Architecture for Plant Automation Quibin Feng, Aleksey Bratukhin, Albert Treytl, Thilo Sauter Abstract Flexibility has become a key factor for manufacturing to keep competitive.

More information

A Metamodel for Collaboration Formalization

A Metamodel for Collaboration Formalization A Metamodel for Collaboration Formalization Loïc Bidoux 1,2, Frédérick Bénaben 1, and Jean-Paul Pignon 2 1 Mines Albi Université de Toulouse {loic.bidoux,frederick.benaben}@mines-albi.fr 2 Customer Innovation

More information

Dynamic Management Architecture for Project Based Production

Dynamic Management Architecture for Project Based Production Dynamic Management Architecture for Project Based Production Akira Tsumaya', Yuta Matoba^, Hidefiimi Wakamatsu^ and Eiji Arai^ 1 Kobe University, Department of Mechanical Engineering, Graduate School of

More information

Create a Standard Cost Estimate. Hint: The marking allowance is done by the instructor.

Create a Standard Cost Estimate. Hint: The marking allowance is done by the instructor. Unit 9 Exercise 41 479 Create a Standard Cost Estimate Business Example Now that you have changed the BOM and the routing you need to calculate the cost for your finished product T-F1## again. You run

More information

Adapter for Self-Learning Production Systems

Adapter for Self-Learning Production Systems Adapter for Self-Learning Production Systems Gonçalo Cândido 1, Giovanni Di Orio 1, José Barata 1, and Sebastian Scholze 2 1 CTS UNINOVA, Dep. de Eng. Electrotécnica, Faculdade de Ciências e Tecnologia,

More information

CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING

CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING 79 CHAPTER 4 PROPOSED HYBRID INTELLIGENT APPROCH FOR MULTIPROCESSOR SCHEDULING The present chapter proposes a hybrid intelligent approach (IPSO-AIS) using Improved Particle Swarm Optimization (IPSO) with

More information

ProActive Service Entity Framework: Improving Service Selection Chances within Large Senior Professional Virtual Community Scenario

ProActive Service Entity Framework: Improving Service Selection Chances within Large Senior Professional Virtual Community Scenario ProActive Service Entity Framework: Improving Service Selection Chances within Large Senior Professional Virtual Community Scenario Tiago Cardoso and Luis M. Camarinha-Matos Faculty of Science and Technology,

More information

A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture

A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture A Holonic Component-Based Approach to Reconfigurable Manufacturing Control Architecture Jin-Lung Chirn, Duncan C. McFarlane Institute for Manufacturing, University of Cambridge Mill Lane, Cambridge, CB2

More information

TechUpdate. TechUpdate is published quarterly and is available exclusively at By: Michael L. Gonzales HandsOn-BI, LLC Quarter 1, 2006

TechUpdate. TechUpdate is published quarterly and is available exclusively at  By: Michael L. Gonzales HandsOn-BI, LLC Quarter 1, 2006 TechUpdate TechUpdate is published quarterly and is available exclusively at www.tdwi.org. By: Michael L. Gonzales HandsOn-BI, LLC Quarter 1, 2006 See Technology Update Live! with Michael L. Gonzales at

More information

Transactions on Information and Communications Technologies vol 1, 1993 WIT Press, ISSN

Transactions on Information and Communications Technologies vol 1, 1993 WIT Press,   ISSN Strategic level interactive scheduling and operational level real-time scheduling for flexible manufacturing systems M. Tsukiyama, K. Mori & T. Fukuda Industrial Electronics and Systems Laboratory, Mitsubishi

More information

Article. Lean meets Industry 4.0. Incompatible or consequent further development?

Article. Lean meets Industry 4.0. Incompatible or consequent further development? Article Lean meets Industry 4.0 Incompatible or consequent further development? Lean approaches, especially in production are often considered outdated and antiquated. Especially, they don t fit in the

More information

An optimization framework for modeling and simulation of dynamic systems based on AIS

An optimization framework for modeling and simulation of dynamic systems based on AIS Title An optimization framework for modeling and simulation of dynamic systems based on AIS Author(s) Leung, CSK; Lau, HYK Citation The 18th IFAC World Congress (IFAC 2011), Milano, Italy, 28 August-2

More information

AGV Controlled FMS. The ITB Journal. Fergus G. Maughan. Volume 1 Issue 1 Article 5

AGV Controlled FMS. The ITB Journal. Fergus G. Maughan. Volume 1 Issue 1 Article 5 The ITB Journal Volume 1 Issue 1 Article 5 2000 AGV Controlled FMS Fergus G. Maughan Follow this and additional works at: http://arrow.dit.ie/itbj Part of the Other Operations Research, Systems Engineering

More information

A FLEXIBLE JOB SHOP ONLINE SCHEDULING APPROACH BASED ON PROCESS-TREE

A FLEXIBLE JOB SHOP ONLINE SCHEDULING APPROACH BASED ON PROCESS-TREE st October 0. Vol. 44 No. 005-0 JATIT & LLS. All rights reserved. ISSN: 99-8645 www.jatit.org E-ISSN: 87-95 A FLEXIBLE JOB SHOP ONLINE SCHEDULING APPROACH BASED ON PROCESS-TREE XIANGDE LIU, GENBAO ZHANG

More information

Xiaobing Zhao Ameya Shendarkar and Young-Jun Son

Xiaobing Zhao Ameya Shendarkar and Young-Jun Son BDI Agent-based Human Decision-Making Model and its Implementation in Agentin-the-loop, Human-in-the-loop, Hardware-in-the-loop, Distributed Simulation Xiaobing Zhao (xiaobing@email.arizona.edu), Ameya

More information

Multi Agent System-Based on Case Based Reasoning for Cloud Computing System

Multi Agent System-Based on Case Based Reasoning for Cloud Computing System Multi Agent System-Based on Case Based Reasoning for Cloud Computing System Amir Mohamed Talib and Nour Eldin Mohamed Elshaiekh Faculty of Computer Science, Software Engineering Department, Future University,

More information

Infor CloudSuite Industrial Whatever It Takes - Advanced Planning & Scheduling for Today s Manufacturer

Infor CloudSuite Industrial Whatever It Takes - Advanced Planning & Scheduling for Today s Manufacturer Infor CloudSuite Industrial Whatever It Takes - Advanced Planning & Scheduling for Today s Manufacturer May 2017 CloudSuite Industrial Where Did APS Come From? APS grew out of the convergence of two movements.

More information

Keywords: Strategic planning, multi-agent systems, mathematical optimization solver, what-if games.

Keywords: Strategic planning, multi-agent systems, mathematical optimization solver, what-if games. AGENT-BASED STRATEGIC PLANNER FOR THE PRODUCTION OF SAMLL LOTS OF COMPLEX PRODUCTS: THEORETICAL AND PRACTICAL PERSPECTIVES Paulo Leitão 1,2, José Barbosa 1 1 Polytechnic Institute of Bragança, Campus Sta.

More information