Processing, Please wait...

  • Home
  • About Us
  • Search:
  • Advanced Search

Growing Science » Decision Science Letters » Multi-objective scheduling in an agent based Holonic manufacturing system

Journals

  • IJIEC (777)
  • MSL (2643)
  • DSL (690)
  • CCL (528)
  • USCM (1099)
  • ESM (421)
  • AC (562)
  • JPM (293)
  • IJDS (952)
  • JFS (101)
  • HE (37)
  • SCI (36)

DSL Volumes

    • Volume 1 (10)
      • Issue 1 (5)
      • Issue 2 (5)
    • Volume 2 (30)
      • Issue 1 (5)
      • Issue 2 (6)
      • Issue 3 (9)
      • Issue 4 (10)
    • Volume 3 (53)
      • Issue 1 (15)
      • Issue 2 (10)
      • Issue 3 (19)
      • Issue 4 (9)
    • Volume 4 (48)
      • Issue 1 (10)
      • Issue 2 (12)
      • Issue 3 (14)
      • Issue 4 (12)
    • Volume 5 (39)
      • Issue 1 (12)
      • Issue 2 (10)
      • Issue 3 (8)
      • Issue 4 (9)
    • Volume 6 (30)
      • Issue 1 (8)
      • Issue 2 (6)
      • Issue 3 (9)
      • Issue 4 (7)
    • Volume 7 (41)
      • Issue 1 (8)
      • Issue 2 (8)
      • Issue 3 (8)
      • Issue 4 (17)
    • Volume 8 (38)
      • Issue 1 (8)
      • Issue 2 (6)
      • Issue 3 (14)
      • Issue 4 (10)
    • Volume 9 (39)
      • Issue 1 (8)
      • Issue 2 (9)
      • Issue 3 (14)
      • Issue 4 (8)
    • Volume 10 (43)
      • Issue 1 (7)
      • Issue 2 (8)
      • Issue 3 (20)
      • Issue 4 (8)
    • Volume 11 (49)
      • Issue 1 (9)
      • Issue 2 (9)
      • Issue 3 (14)
      • Issue 4 (17)
    • Volume 12 (64)
      • Issue 1 (12)
      • Issue 2 (24)
      • Issue 3 (13)
      • Issue 4 (15)
    • Volume 13 (78)
      • Issue 1 (21)
      • Issue 2 (18)
      • Issue 3 (19)
      • Issue 4 (20)
    • Volume 14 (87)
      • Issue 1 (21)
      • Issue 2 (23)
      • Issue 3 (25)
      • Issue 4 (18)
    • Volume 15 (41)
      • Issue 1 (19)
      • Issue 2 (22)

Keywords

Supply chain management(168)
Jordan(165)
Vietnam(151)
Customer satisfaction(120)
Performance(115)
Supply chain(112)
Service quality(98)
Competitive advantage(97)
Tehran Stock Exchange(94)
SMEs(89)
optimization(87)
Sustainability(87)
Artificial intelligence(86)
Financial performance(84)
Trust(83)
TOPSIS(83)
Job satisfaction(81)
Knowledge Management(79)
Social media(78)
Factor analysis(78)


» Show all keywords

Authors

Naser Azad(82)
Zeplin Jiwa Husada Tarigan(66)
Mohammad Reza Iravani(64)
Endri Endri(45)
Muhammad Alshurideh(42)
Hotlan Siagian(40)
Dmaithan Almajali(37)
Jumadil Saputra(36)
Muhammad Turki Alshurideh(35)
Ahmad Makui(33)
Barween Al Kurdi(32)
Hassan Ghodrati(31)
Basrowi Basrowi(31)
Sautma Ronni Basana(31)
Mohammad Khodaei Valahzaghard(30)
Shankar Chakraborty(29)
Ni Nyoman Kerti Yasa(29)
Haitham M. Alzoubi(28)
Sulieman Ibraheem Shelash Al-Hawary(28)
Prasadja Ricardianto(28)


» Show all authors

Countries

Iran(2198)
Indonesia(1311)
Jordan(815)
India(798)
Vietnam(510)
Saudi Arabia(478)
Malaysia(446)
China(231)
United Arab Emirates(226)
Thailand(160)
United States(115)
Turkey(112)
Ukraine(110)
Egypt(106)
Peru(94)
Canada(93)
Morocco(86)
Pakistan(85)
United Kingdom(80)
Nigeria(78)


» Show all countries

Decision Science Letters

ISSN 1929-5812 (Online) - ISSN 1929-5804 (Print)
Quarterly Publication
Volume 3 Issue 1 pp. 1-16 , 2014

Multi-objective scheduling in an agent based Holonic manufacturing system Pages 1-16 Right click to download the paper Download PDF

Authors: T. K. Jana, B. Bairagi, S. Paul, Sk. Sahnawaj, B. Sarkar, J. Saha

Keywords: Holonic manufacturing system, Multi agent system, Multi criteria decision making, Multi-objective scheduling, Priority rule, Scheduling rule

Abstract: The present paper is aimed at multi-objective scheduling in an agent based holonic manufacturing system to satisfy the goal of several communities namely the product, the resource, and the organization simultaneously. In this attempt, first a multi criteria based priority rule is developed following Simple Additive Weight (SAW) method under Multi Criteria Decision Making (MCDM) environment to rank the products. Accordingly, the products are allowed to select a particular resource for execution by negotiation considering minimum time as criterion. The interests of different communities are accomplished by allocating the ordered rank of products to the ordered rank of resources. Conflict, if arises between products and resources, are resolved by introducing the concept of Early Finish Time (EFT) as criterion for task allocation. A scheduling algorithm is proposed for execution of the rule. In view of machine failure, a cooperation strategy is evolved that also optimizes reallocation of the incomplete task. It is concluded that the proposed scheduling algorithm together with the disturbance handling algorithm are poised to satisfy the agent’s local objective as well as organization’s global objective concurrently and are commensurable with multi agent paradigm.

How to cite this paper
Jana, T., Bairagi, B., Paul, S., Sahnawaj, S., Sarkar, B & Saha, J. (2014). Multi-objective scheduling in an agent based Holonic manufacturing system.Decision Science Letters , 3(1), 1-16.

Refrences
Alvarez, E & Diaz, F. (2007). Framework for the dynamic scheduling of complex job shops. International Journal of Manufacturing Technology and Management, 11 (3/4), 411-425.

Babiceanu, R.F & Chen, F.F. (2006). Development and applications of holonic manufacturing systems: a survey. Journal of Intelligent Manufacturing, 17, 111-131.

Badr, I. (2008). An agent-based scheduling framework for flexible manufacturing systems. World Academy of Science, Engineering and Technology, 40, 363-369.

Bashiri, M., Koosha, M., & Karimi, H. (2012). Permutation based decision making under fuzzy environment using Tabu search. International Journal of Industrial Engineering Computations, 3, 301–312.

Brussel, H.V., Wyns, J., Valckenaers, P., Bongaerts, L. & Peeters, P. (1998). Reference architecture for holonic manufacturing systems: PROSA. Computers in Industry, 37 (3), 255-276.

Brussel, H.V., Bongaerts, L., Wyns, J., Valckenaers, P. & Ginderachter, T.V. (1999). A conceptual framework for holonic manufacturing: Identification of manufacturing holons. Journal of Manufacturing Systems, 18 (1), 35-52.

Cao, Y., Yang, Y., Wang, H., & L. Yang, L. (2009). Intelligent job shop scheduling based on MAS and integrated routing wasp algorithm and scheduling wasp algorithm. Journal of Software, 4 (5), 487-494.

Christo, C. & Cardeira, C. (2007). Trends in intelligent manufacturing systems. International Symposium on Industrial Electronics, IEEE, Vigo, Spain, 3209- 3214.

Davis, R. & Smith, R. (1983). Negotiation as a metaphor for distributed problem solving. Artificial Intelligence, 20 (1), 63-109.

Duffle, N.A, & Prabhu, V.V. (1994). Real-time distributed scheduling of heterarchical manufacturing systems. Journal of Manufacturing Systems, 13 (2), 94-107.

Eguchi, T., Oba, F. & Toyooka, S. (2008). A robust scheduling rule using a neural network in dynamically changing job-shop environments. International Journal of Manufacturing Technology and Management, 14 (3-4), 266 – 288.

Giret, A and Botti, V. (2004). Holons and agents. Journal of Intelligent Manufacturing, 15, 645-659.

Glanzer, K., Hammerle, A. & Ralf Geurts, R. (2001). The Application of ZEUS agents in manufacturing environments. Proceedings of the 12th International Workshop of IEEE on Database and Expert Systems Applications, IEEE Computer Society, Munich, Germany, 628-632.

Gou, L., Luh, P.B. & Kyoya, Y. (1998). Holonic manufacturing scheduling: architecture, cooperation mechanism, and implementation. Computers in Industry, 37, 213-231.

Heragu, S.S., Graves, R.J., Kim, B. & Onge, A. (2002). Intelligent agent based framework for manufacturing systems control. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans. 32 (5), 560-573.

Iwamura, K., Okubo, N., Tanimizu, Y. & Sugimura, N. (2006). Real-time scheduling for holonic manufacturing systems based on estimation of future status. International Journal of Production Research, 44 (18-19), 3657–3675.

Janic, M., & Reggiani, A. (2002). An Application of the Multiple Criteria Decision Making (MCDM) Analysis to the selection of a new hub airport. European Journal of Transport and Infrastructure Research, 2 (2) 113-142.

Kutanoglu, E. & Sabuncuoglu, I. (1999). An analysis of heuristics in a dynamic job shop with weighted tardiness objectives. International Journal of Production Research, 37 (1), 165-187.

Li, Q., Qu, D. & Du, L. (2008). Research on hybrid-genetic algorithm for MAS based job-shop dynamic scheduling. Proceedings of the IEEE International Conference on Service Operations and Logistics, and Informatics, IEEE, Beijing, China, 1742-1745.

Leitao, P., & Restivo, F. (2002). Holonic adaptive production control systems. Proceedings of the 28th IEEE Industrial Electronics Society Annual Conference, IEEE, Sevilla, Spain, 2968-2973.

Leitao, P. (2009). Agent-based distributed manufacturing control: A state-of-the-art survey. Engineering Applications of Artificial Intelligence, 22, 979-991.

Miyashita, K. (1998). CAMPS: A constraint-based architecture for multi-agent planning and scheduling. Journal of Intelligent Manufacturing, 9 (2), 147-154.

Papakostas, N., and Chryssolouris, G. (2009). A scheduling policy for improving tardiness performance. Asian International Journal of Science and Technology in Production and Manufacturing Engineering, 2 (3), 79-89.

Priore, P., Fuente, D., Puente, J. & Parreno, J. (2006). A comparison of machine-learning algorithms for dynamic scheduling of flexible manufacturing systems. Engineering Applications of Artificial Intelligence, 19 (3), 247-255.

Rabelo, R.J., Camarinha-Matos, L.M. & Afsarmanesh, H. (1999). Multi-agent based agile scheduling. Robotics and Autonomous Systems, (27), 15-28.

Shen, W. (2002) Distributed manufacturing scheduling using intelligent agents. IEEE Intelligent Systems, 17 (1), 88-94.

Shen, W., Wang, L. & Hao, Q. (2006a). Agent-based distributed manufacturing process planning and scheduling: A State-of-the-Art Survey. IEEE Transactions on Systems, Man and Cybernetics – Part C: Applications and Reviews, 36 (4), 563-577.

Shen, W., Hao, Q., Yoon, H.J. & Norrie, D.H. (2006b). Applications of agent-based systems in intelligent manufacturing: An updated review. Advanced Engineering Informatics, (20), 415-431.

Shih, H. (2008). Incremental analysis for MCDM with an application to group TOPSIS. European Journal of Operational Research, 186, 720-734.

Smith, R.G. (1980). The Contract Net Protocol: High-Level Communication and Control in a Distributed Problem Solver. IEEE Transactions on Computer, C-29 (12), 1104-1113.

Sousa, P. & Ramos, C. (1999). A distributed architecture and negotiation protocol for scheduling in manufacturing systems. Computers in Industry, 38, 103-113.

Sudo, Y., Sakao, N. & Matsuda, M. (2010). An agent behavior technique in an autonomous decentralized manufacturing system. Journal of Advanced Mechanical Design, Systems and Manufacturing, 4 (3), 673-682.

Walker, S.S., Brennan, R.W. & Norrie, D.H. (2005). Holonic job shop scheduling using a multi agent system. IEEE Intelligent Systems, 20 (1), 50-57.

Wang, C., Ghenniwa, H. & Shen, W. (2008). Real time distributed shop floor scheduling using an agent-based service-oriented architecture. International Journal of Production Research, 46 (9), 2433-2452.

Wang, L., (2001). Integrated design-to-control approach for holonic manufacturing systems. Robotics and Computer Integrated Manufacturing, 17, 159-167.

Wang, T-C. & Chang T-H. (2007). Application of TOPSIS in evaluating initial training aircraft under a fuzzy environment. Expert Systems with Applications, 33, 870–880.

Wang, Y.C. & Usher, J.M. (2005). Application of reinforcement learning for agent-based production scheduling. Engineering Applications of Artificial Intelligence, 18, 73-82.

Wong, T.N., Leung, C.W., Mak, K.L. & Fung, R.Y.K (2006). Dynamic shop floor scheduling in multi-agent manufacturing systems. Expert Systems with Applications, 31, 486–494.

Wooldrigde, M. & Jennings, N.R. (1995). Intelligent agents: theory and practice. The Knowledge Engineering Review, 10 (2), 115-152.

Yang, C & Lin, J.S. (1998). The development of a hybrid hierarchical/heterarchical shop floor control system applying bidding method in job dispatching. Robotics and Computer-Integrated Manufacturing, 14, 199-217.
  • 0
  • 1
  • 2
  • 3
  • 4
  • 5

Journal: Decision Science Letters | Year: 2014 | Volume: 3 | Issue: 1 | Views: 3289 | Reviews: 0

Related Articles:
  • Optimizing combination of job shop scheduling and quadratic assignment prob ...
  • Evolutionary approaches for scheduling a flexible manufacturing system with ...
  • Heuristics for production scheduling problem with machining and assembly op ...
  • A proposition of a manufactronic network approach for intelligent and flexi ...
  • A new mathematical model for the job shop scheduling problem with uncertain ...

Add Reviews

Name:*
E-Mail:
Review:
Bold Italic Underline Strike | Align left Center Align right | Insert smilies Insert link URLInsert protected URL Select color | Add Hidden Text Insert Quote Convert selected text from selection to Cyrillic (Russian) alphabet Insert spoiler
winkwinkedsmileam
belayfeelfellowlaughing
lollovenorecourse
requestsadtonguewassat
cryingwhatbullyangry
Security Code: *
Include security image CAPCHA.
Refresh Code

® 2010-2026 GrowingScience.Com