طبقه بندی موجودی با استفاده از بهینه سازی جمعی راه حل های چندهدفه

ABC 􀃉􀁼􀃀􀁝 􀃄􀂬􀁞􀂗 􀂵􀁼􀂻 􀃁􀁻 􀂪􀃌􀂨􀂸􀁥 􀀬􀁿􀃂􀂐􀃀􀂻 􀀬􀃃􀁻􀁙􀂁 􀂶􀃌􀂟􀁚􀂼􀂇􀁙 􀀭􀁻􀁙􀁻􀂀􀃆􀂻 􀀬􀃊􀂋􀃂􀃅􀁼􀂻 􀀭􀁼􀃌􀂼􀁶􀂷􀁙􀁼􀁞􀂟 􀀬􀃊􀃋􀁚􀂨􀂏 [1]
.149-133􀂎􀂏 􀀬1387 􀀬57 􀃉 􀃃􀁿􀁚􀂼􀂋 􀀬􀁤􀃋􀂀􀃋􀁼􀂻 􀁣􀁚􀂠􀂷􀁚􀂘􀂻 􀃉 􀃄􀂻􀁚􀃀􀂸􀂐􀂧 􀀭􀃉􀁻􀃂􀁭􀃂􀂻 􀃉 􀃃􀁿􀁚􀃌􀂠􀂻 􀁼􀃀􀁱
􀃉 􀃃􀁿􀁚􀃌􀂠􀂻 􀁼􀃀􀁱 ABC 􀃉􀁼􀃀􀁝 􀃄􀂬􀁞􀂗 􀂦􀂸􀁦􀁺􀂻 􀃉􀁚􀃅 􀂉􀃁􀁿 􀃉 􀃄􀂈􀃋􀁚􀂬􀂻 􀀬􀁿􀃂􀂐􀃀􀂻 􀀬􀃃􀁻􀁙􀂁 􀂶􀃌􀂟􀁚􀂼􀂇􀁙 􀀭􀂀􀂨􀂠􀁭 􀀬􀃊􀃋􀁚􀂓􀁿 [2]
.22- 1 􀂎􀂏 􀀬1388 􀀬17 􀃉 􀃃􀁿􀁚􀂼􀂋 􀀬􀃊􀁦􀂠􀃀􀂏 􀁤􀃋􀂀􀃋􀁼􀂻 􀁣􀁚􀂠􀂷􀁚􀂘􀂻 􀃊􀂌􀃅􀃁􀂄􀁡 􀀯 􀃊􀂼􀂸􀂟 􀃉 􀃄􀂻􀁚􀃀􀂸􀂐􀂧 􀀭􀃉􀁻􀃂􀁭􀃂􀂻
[3] Weihrich, H., Koontz, H., Management: A Global Perspective, McGraw-hill, 11th
Edition, 2006.
[4] Guvenir, H.A., Erel, E., Multi criteria inventory classification using a genetic
algorithm, European Journal of Operational Research, vol. 105, 1998. pp. 29-37.
[5] Silver, E.A., Pyke, D.F., Peterson, R., Inventory Management and Production
Planning and Scheduling, John Wiley & Sons, New York, Third ed., 1998.
[6] Cohen, M.A., Ernst, R., Multi-item classification and generic inventory stock control
policies, Production and Inventory Management Journal, vol. 29(3), 1988. pp. 6-8.
[7] Partovi, F.Y., Burton, J., Using the analytic hierarchy process for ABC analysis,
International Journal of Productions Management, vol. 13(9), 1993. pp. 29-44.
[8] Flores, B.E., Whybark, D.C., Implementing multiple criteria ABC analysis, Journal
of Opration Management, vol. 7(1), 1987. pp. 79-84.
[9] Partovi, F.Y., Anandarajan, M., Classification inventory using an artificial neural
network approach, Computer & industrial Engineering, vol. 41, 2005. pp. 389-404.
[10] Kennedy, J., Eberhart R.C., Particle swarm optimization. In: Proceedings of the
IEEE conference on neural networks, Perth, Australia, 1995. pp. 1942–8.
[11] Eberhart, R.C., Kennedy, J., A new optimizer using particle swarm theory, in:
Proceedings of the Sixth International Symposium on Micro Machine and Human
Science, IEEE Press, Piscataway, NJ, 1995. pp. 39–43.
[12] Eberhart, R.C., Shi, Y., Computational Intelligence: Concepts to Implementations,
Morgan Kaufmann, 2003.
[13] Zhu, H., et al., Particle Swarm Optimization (PSO) for the constrained portfolio
optimization problem, Expert Systems with Applications, 2011,
doi:10.1016/j.eswa.2011.02.075.
[14] Omkar, S.N., et al., Quantum behaved Particle Swarm Optimization (QPSO) for
multi objective design optimization of composite structures, Expert Systems with
Applications, vol. 36, 2009. pp. 11312–22.
[15] Tsai, C.Y., Yah, S.W., A multiple objective particle swarm optimization approach
for inventory classification, Int. J. Production Economics, vol. 114, 2008. pp. 656–66.
[16] Feng, Y., Zheng, B., Li. Z., Exploratory study of sorting particle swarm optimizer
for multi objective design optimization, Mathematical and Computer Modelling, vol.
52, 2010. pp. 1966-75.
[17] Li, X., et al., A nondominated sorting particle swarm optimizer for multi objective
optimization, in: Proceedings of the Genetic and Evolutionary Computation, Springer,
2003. pp. 37–48.
طبق هبندی موجودی با استفاده از بهین هسازی جمعی را هح لهای چندهدفه 55
[18] Raquel, C., Naval, P. Jr., An effective use of crowding distance in multi objective
particle swarm optimization, in: Proceedings of the Conference on Genetic and
Evolutionary Computation, ACM Press, New York, NY, USA, 2005. pp. 257–264.
[19] Fieldsend, J., Singh, S., A multi-objective algorithm based upon particle swarm
optimization, an efficient data structure and turbulence, in: Proceedings of The UK
Workshop on Computational Intelligence, UK, 2002. pp. 34–44.
[20] Xiaohui, H., Eberhart, R., Multi objective optimization using dynamic
neighborhood particle swarm optimization, in: Proceedings of the Congress on
Evolutionary Computation, vol. 2, 2002. pp. 1677–81.
[21] Pulido, G., Coello, C., Using clustering techniques to improve the performance of a
multi-objective particle swarm optimizer, in: Proceedings of the Genetic and
Evolutionary Computation Conference, Springer, 2004. pp. 225–37.
[22] Liu, D., et al., A multi objective memetic algorithm based on particle swarm
optimization, IEEE Transactions on Systems, Man and Cybernetics, Part B 37 (1), 2007.
pp. 42–50.
[23] Liu, D., et al., On solving multi objective bin packing problems using evolutionary
particle swarm optimization, European Journal of Operational Research, 2008. pp. 357–
82.
[24] Goldberg, D.E., Genetic Algorithms in Search, Optimization and Machine
Learning, Addison-Wesley, Reading, MA, 1989.
[25] Fogel, L.J., Marsh, A.J., Walsh, M.J., Artificial Intelligence through Simulated
Evolution, Wiley & Sons, New York, 1966.
[26] Reynolds, C.W., Flocks, herds and schools: a distributed behavioral model,
Comput, Graphics, vol. 21 (4), 1987. pp. 25–34.
[27] Heppner, F., Grenander, U., A stochastic nonlinear model for coordinated bird
flocks, in: S. Krasner (Ed.), The Ubiquity of Chaos, AAAS Publications, Washington,
DC, 1990.
[28] Wilson, E.O., Sociobiology: The New Synthesis, Belknap Press, Cambridge, MA,
1975.
[29] Suganthan, P.N., Particle swarm optimizer with neighborhood operator. In:
Proceedings of the 1999 congress of evolutionary computation, vol. 3. IEEE Press,
1999. pp. 1958–62.
[30] Shi, Y., Eberhart, R., Parameter selection in particle swarm optimization, in:
Proceedings of Evolutionary Programming, 1998. pp. 591–600.
[31] Alatas, B., Akin, E., Ozer, A.B., Chaos embedded particle swarm optimization
algorithms, Chaos, Solitons and Fractals, vol. 40, 2009. pp. 1715–34.
[32] Shi, Y., Eberhart, R.C., A modified particle swarm optimizer, IEEE
IntConfComputIntell, 1998. pp. 69–73.
[33] Shi, Y., Eberhart, R.C., Empirical study of particle swarm optimization. In:
Proceedings of the 1999 IEEE congress on evolutionary computation, Piscataway (NJ):
IEEE Press, 1999. pp. 1945–50.
[34] Zheng, Y.L., etal., On the convergence analysis and parameter selection in particle
swarm optimization, In: Proceedings of the 2003 IEEE international conference on
56 مطالعات مدیریت صنعتی، سال یازدهم، شماره 30 ، پاییز 1392
machine learning and cybernetics, Piscataway (NJ): IEEE Press, 2003. pp. 1802–7.
[35] Zheng, Y.L., et al., Empirical study of particle swarm optimizer with an increasing
inertia weight, In: Proceedings of the 2003 IEEE congress on evolutionary computation,
Piscataway (NJ): IEEE Press, 2003. pp. 221–6.
[36] Jiao, B., et al., A dynamic inertia weight particle swarm optimization algorithm,
Chaos, Solitons& Fractals, vol. 37(3), 2008. pp. 698–705.
[37] Zhang, L., Yu, H., Hu, S., A new approach to improve particle swarm optimization,
GECCO 2003, LNCS, vol. 2723, 2003. pp. 134–9.
[38] Eberhart, E., Shi, Y., Tracking and optimizing dynamic systems with particle
swarms. In: Proceedings of the 2001 IEEE congress on evolutionary computation,
Piscataway (NJ): IEEE Press, 2001. pp. 94–100.
[39] Park, J.B., et al., An improved particle swarm optimization for economic dispatch
with valve-point effect, Int J Innov Energy System Power, vol. 1(1), 2006. pp. 1–7.
[40] Jiang, C., Etorre, B.A., Hybrid method of chaotic particle swarm optimization and
linear interior for reactive power optimization, Math Compute Simul, vol. 68, 2005. pp.
57–65.
[41] Ratnaweera, A., Halgamure, S.K., Watson, H.C., Self-organizing hierarchical
particle swarm optimizer with time-varying acceleration coefficients, IEEE Trans Evol
Compute, vol. 8 , 2004. pp. 240–55.
[42] Carlisle, A., Dozier, G., An off the-Shelf PSO, In: The particle swarm optimization
workshop, 2001. pp. 1–6.
[43] Coelho, L.S., Mariani, V.C., A novel chaotic particle swarm optimization approach
using He’non map and implicit filtering local search for economic load dispatch, Chaos,
Solitons& Fractals, vol. 39(2), 2009. pp. 510–8.
[44] Chuanwen, J., Bompard, E.A., Self-adaptive chaotic particle swarm algorithm for
short term hydroelectric system scheduling in deregulated environment, Energy
Converse Manage, vol. 46, 2005. pp. 2689–96.
[45] Chakravarty, A.K., Multi-item inventory grouping whit dependent set-up cost and
group overhead cost, Engineering Cost and Production Economics, 1986. pp. 13-23.
􀁬􀃋􀁚􀁦􀂿 􀃉 􀃄􀂈􀃋􀁚􀂬􀂻 􀃉􀁙􀂀􀁝 􀁼􀃋􀁼􀁭 􀃊􀁦􀂧􀁚􀃌􀃅􀁿 􀃉 􀃄􀁗􀁙􀁿􀁙 􀀬􀁿􀃂􀂐􀃀􀂻 􀀬􀃃􀁻􀁙􀂁 􀂶􀃌􀂟􀁚􀂼􀂇􀁙 􀀭􀁼􀃌􀂼􀁶􀂷􀁙􀁼􀁞􀂟 􀀬􀃊􀃋􀁚􀂨􀂏 [46]
􀀬(􀁚􀁢􀃋􀁚􀂇 􀁤􀂯􀂀􀂋 :􀃉􀁻􀁿􀃂􀂻 􀃉 􀃄􀂠􀂷􀁚􀂘􀂻) 􀃉􀁻􀃂􀁭􀃂􀂻 􀃉 􀃃􀁿􀁚􀃌􀂠􀂻 􀁼􀃀􀁱 ABC 􀃉􀁼􀃀􀁝 􀃄􀂬􀁞􀂗 􀃉􀁚􀃅 􀂵􀁼􀂻 􀃉􀂀􀃌􀂳􀁿􀁚􀂯 􀃄􀁝
.224- 207􀂎􀂏 􀀬1390 􀃃􀁚􀂻 􀂀􀃌􀁥 􀀬47-2 􀃉 􀃃􀁿􀁚􀂼􀂋 􀂺􀃅􀁼􀁮􀃅 􀂵􀁚􀂇 􀀬􀁼􀃅􀁚􀂋 􀃃􀁚􀂴􀂌􀂿􀁙􀁻 􀃊􀂌􀃅􀃁􀂄􀁡 􀀯 􀃊􀂼􀂸􀂟 􀃉 􀃄􀂻􀁚􀃀􀃅􀁚􀂻 􀃁􀁻