Summary
This chapter proposes a natural stigmergic computational technique Bee Colony for process scheduling and optimization problems developed by mimicking social insects’ behavior. The case study considered in the chapter is a milk production center, where process scheduling, supply chain network etc. are crucial, as slight deviation in scheduling may lead to perish out the item causing financial loss of the plant. The process scheduling of such plants extensively deals with multi-objective conflicting criteria, hence the concept of Pareto Dominance has been introduced in the form of Pareto Bee Colony Optimization. Some facts about social insects namely bees are presented with an emphasis on how they could interact and self organized for solving real world problems. Finally, a performance simulation and comparison has been accomplished envisaging other similar bio-inspired algorithms.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Key words
References
Box, G.E.P., Hunter, W.G. & Hunter, J.S. (1978) Statistics for experimenters: an introduction to design, data analysis, and model building, Wiley, New York.
Myers, R.H. & Montgomery, D.C. (2002) Response surface methodology: process and product optimization using designed experiments, Wiley, New York
den Besten, L.M. Simple Meta-heuristics for Scheduling. An empirical investigation into the application of iterated local search to deterministic scheduling problems with tardiness penalties, PhD Thesis, October 2004.
Nareyek, A. (2000): AI Planning in a Constraint Programming Framework, in Proceedings of the Third International Workshop on Communication-Based Systems (CBS-2000)
Kreipl, S., Pinedo, M. Planning and Scheduling in Supply Chains: An Overview of Issues in Practice, Production and Operations Management Vol. 13, No. 1, spring 2004, pp. 77-92.
Chopra, S. and Meindl, P. Supply Chain Management: Strategy, Planning, and Operations: Prentice Hall College, 2001
Harrison, T.P., Lee, H.L. and Neale, J.J. The Practice of Supply ChainManagement: Kluwer Academic Publishing, 2003.
Truong T.H and Azadivar, F. Simulation based optimization for supply chain configuration design, presented at the Winter Simulation Conference, Piscataway, NJ, 2003.
Joines, J.A., Kupta, D., Gokce, M.A., King, R.E. Supply Chain Multi-Objective Simulation Optimization, presented at the 2002 Winter Simulation Conference, 2002.
Al-Mutawah, K., Lee, V., Cheung, Y. Modeling Supply Chain Complexity using a Distributed Multi-objective Genetic Algorithm, presented at The 2006 International Conference on Computational Science and its Applications ICCSA’06, Glasgow, Scotland, 2006.
Simchi-Levi D., Kaminsky, P. and Simchi-Levi, E. Designing and Managing the Supply Chain, McGraw-Hill, 2000.
Ribeiro, R. and Loureno, H.R. A multi-objective model for a multi-period distribution management problem Economic Working papers Series, Universitat Pompeu Fabra, Spain, 2001.
Bonabeau, E., Dorigo, M., Theraulaz, G.: Swarm Intelligence: From Natural to Articial Systems. Oxford University Press (1999).
Sumpter, D., Pratt, S. A modelling framework for understanding social insect foraging. Behavioral Ecology and Sociobiology 53 (2003) 131-144.
Goss, S., Deneubourg, J.L. The self-organising clock pattern of Messor pergandei (Formicidae, Myrmicinae). Insectes Soc. 36:339-346, 1989.
Bartholdi, J.J., Seeley, T.D., Tovey, C.A., Vande Vate J.H. The pattern and effectiveness of forager allocation among flower patches by honey bee colonies. J. Theor. Biol. 160:23-40, 1993.
Beckers, R., Deneubourg, J.L., Goss, S., Pasteels, J. Collective decision making through food recruitment. Insectes Soc. 37:258-267, 1990.
Beckers, R., Deneubourg, J.L., Goss, S. Modulation of trail laying in the ant Lasius niger (Hymenoptera: Formicidae) and its role in the collective selection of a food source. J Insect Behav 6:751-759, 1993.
Beekman, M., Sumpter, D.J.T., Ratnieks, F.L.W. Phase transition between disordered and ordered foraging in Pharoah’s ants. Proc. Natl. Acad. Sci. USA 98:9703-9706, 2001.
Bonabeau, E. Comment on Phase transitions in instigated collective decision making. Adapt. Behav. 5:99-105, 1997.
Deneubourg, J.L., Aron, S., Goss, S., Pasteels, J.M. The self-organizing exploratory pattern of the Argentine ant. J Insect Behav 3:159-168, 1990.
Goss, S., Deneubourg, J.L. The self-organizing clock pattern of Messor pergandei (Formicidae, Myrmicinae). Insectes Soc 36:339-346, 1989.
Nicolis, S.C., Deneubourg, J.L. Emerging patterns and food recruitment in ants: an analytical study. J. Theor. Biol. 198: 575-592, 1999.
Biesmeijer, J., de Vries, H. Exploration and exploitation of food sources by social insect colonies: a revision of the scout recruit concept. Behav. Ecol. Sociobiol 49:89-99, 2001.
Traniello, J.F.A. Recruitment behavior, orientation, and the organization of foraging in the carpenter ant Camponotus pennsylvanicus DeGeer (Hymenoptera: Formicidae). Behav Ecol Sociobiol 2:61-79, 1997.
Holdobler, B., Stanton, R.C., Markl, H. Recruitment and food retrieving behavior in Novomessor (Formicidae, Hymenoptera), I. Chemical signals. Behav. Ecol. Sociobiol 4:163-181, 1978.
Reuter, M., Keller, L. Sex ratio conflict and worker production. Am. Nat. 158:166-177, 2001.
Hall, R.W. Driving the Productivity Machine: Production Planning and Control in Japan. American Production and Inventory Control Society, Falls Church, Vancouver, 1981.
Deb, K. Multiobjective Evolutionary Algorithms: Introducing Bias among Pareto-Optimal Solutions. In A.Ghosh and S. Tsutsui(Eds.), Theory and Applications of Evolutionary Computation: Recent Trends, Spinger -Verlag, London, 2002.
Blum, C. ACO applied to Group Shop Scheduling: A case study on Intensification and Diversification. In Proceedings of the 3rd International Workshop on Ant Algorithms (ANTS 2002), 2002. Also available as technical report TR/IRIDIA/2002-08, IRIDIA, Universite Libre de Bruxelles.
Teodorvic, D., Dell’orco, M. Bee Colony optimization- A Cooperative Learning approach to Complex Transporation Problem, Advanced OR and AI Methods in Transportation, 2005, pp 51-60.
Dorigo, M., Maniezzo, Vittorio, Colorni, Alberto, Ant system: optimization by a colony of cooperating agents. IEEE Transactions on Systems, Man, and Cybernetics,Part B: Cybernetics, 1996. 26(1): p. 29-41.
Nowicki, E. and Smutnicki, C., A fast taboo search algorithm for the job shop problem, Management Science, Vol. 42, No. 6 (1996), pp. 797-813.
Aytug, H., Lawley, M. A., McKay, K., Mohan, S., Uzsoy, R. M., 2005. Executing production schedules in the face of uncertainties: A review and some future directions. European Journal of Operational Research 161, pp. 86-110.
Lee, H. L., Padmanabhan, V., Whang, S., 1997. The bullwhip effect in supply chains. Sloan Management Review 38, pp.93-102.
Blackhurst, J., Wu, T., O’Grady, P. Network-based approach to modeling uncertainty in a supply chain. International Journal of Production Research 42 (8), pp.1639-1658, 2004.
Fu, M. C. Simulation optimization. In: Peters, B. A., Smith, J. S., Medeiros, D. J., Rohrer, M. W. (Eds.), Proceedings of the 2001 Winter Simulation Conference.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
Banerjee, S., Dangayach, G.S., Mukherjee, S.K., Mohanti, P.K. (2008). Modelling Process and Supply Chain Scheduling Using Hybrid Meta-heuristics. In: Xhafa, F., Abraham, A. (eds) Metaheuristics for Scheduling in Industrial and Manufacturing Applications. Studies in Computational Intelligence, vol 128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78985-7_11
Download citation
DOI: https://doi.org/10.1007/978-3-540-78985-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-78984-0
Online ISBN: 978-3-540-78985-7
eBook Packages: EngineeringEngineering (R0)