Research on Hybrid Particle Swarm Optimization for Automobile Logistics Network Design Problem

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1 : (2006) ,3, 2 3, (11, , , ) :,,,,, : : TP18 C934 : A esearch on Hybrid Particle Swarm Optimization for Automobile Logistics Network Design Problem QIN Xu2wei 1,3, FAN Yu2shun 2, YIN Chao2wan 3 (11School of Business Administration, Northeastern University, Shenyang , China 21Department of Automation, Tsinghua University, Beijing , China 31Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang , China) Abstract: According to the practical operation characteristic of automobile logistics network, the integrated optimization model is presented, which provides an integrated view of transportation economies - of - scale, inventory and facility costs as well as service quality The solution combined the flow prediction algorithm and particle swarm optimization ( PSO) is presented In this solution, PSO is used to search feasible structure of logistics network, while flow prediction algorithm is used to decide its optimal transportation route Evolution operation such as crossover and mutation is also embedded to avoid the common defect of premature convergence Simulations are given to confirm this hybrid particle swarm optimization work efficiency and the probability of finding the global optimal value are enhanced Key words : automobile logistics flow prediction algorithm hybrid particle swarm optimization 0,, Nozick, (S - 1,S), [1 2 ], [2 ] Hall, [3 ] [4 ] 2, 2 (PSO),Kennedy Eberhart, : : ( ) : ( ),,,, ( ),,,,, China Academic Journal Electronic Publishing House All rights reserved

2 [5 ] PSO,, [6 Mauricec PSO ] Salman PSO [7 ], PSO [8 ], PSO [9 ],, 1 P D C, 1, I,,, (S - 1,S),,, 2 2 2,1,, : ( ) : min D I h di S di i = 1 P D p = 1 C + c c = 1 k pd c Y p( i) d i I p( i) = p D + d d P C + p = 1 D + dr k dr c = 1 k pc < I i = 1 w dri V p( i) c i I p( i) = p ri D + C D + q dr c = 1 I dr i = 1 ri, k cd U cdi + D s t V p( i) c = U cdi c = 1,2,, C i = 1,2,, I (1) Y p( i) d V p( i) c C + U cdi = c = 1 c di = dr S di = min S D N min d dr ri,2,, D i = 1,2,, I (2) ri c = 1,2,, C i = 1,2,, I (3) ri,2,, D i = 1,2,, I (4) S - 1 k = 0 e - di di ( k! di di ) k di,2,, D i = 1,2,, I (5) d,2,, (6) dr N max d,2,, D (7) c = {0,1} c = 1,2,, C (8) i I China Academic Journal Electronic Publishing House All rights reserved

3 7 49 d = {0,1},2,, D (9) dr = {0,1},2,, D,2,, (10) Y p( i) d 0 U cdi 0 V p( i) c 0 c = 1,2,, C i = 1,2,, I,2,, D (11) : : p c d i r p ( i) p i : c d dr 0-1, 1 c d d r Y p( i) d i p d U cdi c d i V p( i) c i p c S di i d, (5) : k pd k pc k cd k dr p d p c c d d r ri r i w dri i d r c d c d di d h di i d q dr 0-1,1 r d V pci U cdi i I Y pdi i I p( i) = p < i I p( i) = p p c c d p d, (1) (2) (3), (4) (5) (6) (7), 2 D,n, i D X i = ( x i1, x i2,, x id ), i = 1,2,, n,x i, X i i D = ( v i1, v i2,, v id ) i V i = ( p i1, p i2,, p id ), P g = ( p g1, p g2,, p gd ), : v id ( t + 1) = wv id ( t) + c 1 rand () [ p id ( t) - x id ( t) ] + c 2 rand () [ p gd ( t) - x id ( t) ], (12) x id ( t + 1) = x id ( t) + v id ( t + 1), 1 i n 1 d D, (13), c 1, c 2 rand() [0,1 ] w, w (exploration), w (exploitation) x id [ - X max d, X max d ], v id [ - V max d, V max d ], () ( Global) (local),kennedy,ing Wheel Star, [10 ] PSO, China Academic Journal Electronic Publishing House All rights reserved P i

50 2006 7 3 -, 2 NP,, -,,, - 2 311 2 Hall,, : 1) 3 2) 2 3) 4) 2 Hall 3, 5) 4), 6) 5), Hall 2, 7) 2 6) 4), 6), 3)

5 X A X B, X A D, u d = ( Int (random(0, C) ),Int ( ),random( ) 0 C, X B, y r = Int (random(1, d) ), d X A, r d,3,5,10 : [ ] : , [ u 1,, u d,, u D y 1,, y r,, y ], X A X B, 4 (3) (6) (7), 313,, v id ( t + 1) = Int{ wv id ( t) + c 1 rand ( ) [ p id ( t) - x id ( t) ] + c 2 rand ( ) [ p gd ( t) - x id ( t) ]}, x id ( t + 1) = Int (andom(0, T) ) x id ( t + 1) > T, Abs[ x id ( t) + v id ( t + 1) ) ] x id ( t + 1) T : Int ( ) Abs ( ) X A, T C, andom(0, T) 0 C, T X A d,andom(1, T) 1 d w,: X B w = ( w int - w end ) ( S max - t)πs max + w end S max, w int, w end, :,, = 1 - e - t S max, :X B n X A d,d > n,x A ( d - n) d < n,x A ( n - d) 1 d S max, PS, c 1 c 2, w int w end : 1) 2) X A 0 - C, X A d, X B 1 - d 3) V A ( - C,C),V B ( - d, d) 4),,,, 5) P i, P l P g 2, 1) = 50 %( S max - t)πs max 2, China Academic Journal Electronic Publishing House All rights reserved

6 ) 3), P i, P l P g , 2, 5, 70, 90 % 30 V f ( V) = e V 8500, 15 1 ( ) 2 (Π) C01 C02 P P C01 C02 D01 D02 D03 D04 D05 D06 cost ( ) D01 D02 D03 D04 D05 D06 P P C C (Π) P P P ( ) D D D D D D JBuilder 9 0,Pentium 1 6G,512M, : n = 60, 4 w int = 0190, w end = 012, c1 = c2 = ,, 7 C02 D04 D05 D06, C02 D05 D06, D04, 413 D04 C02, D03,, China Academic Journal Electronic Publishing House All rights reserved

7 XA : XB : C01 C02 D01 D02 D04 D05 D , 20,, 018, 0105, 60, (s) () PSO 76 % PSO 88 % % % , PSO,,,, PSO,,, ( ) 46, 90 % 5,,, : [ 1 ] Nozick L K, Turnquist M A Integrating inventory impacts into a fixed charge model for locating distribution centers [J ] Transportation esearch Part E, 1998, 43(3) : [ 2 ],, [J ],2004,19(1) :59-66 Tan Ling, GAO Junjun, WANG Yingjun Study on problem of distribution center location based on inventory cost optimization[j ] Chinese Journal of System Engineering, 2004, 19(1) : [ 3 ] Hall W Direct versus terminal freight routing on a network with concave costs [J ] Transportation esearch Part B, 1987, 21 (4) : [ 4 ], GA [J ],2004,44(11) : Tian Qing, Miao Lixin, Zhang Li Logistics network design based on transport planning and combined GA[J ] Journal of Tsinghua University (Sci &Tech), 2004, 44(11) : [ 5 ] Kennedy J, Eberhart C Particle swarm optimization [A] IEEE Int Conf on Neural Networks[ C] Perth, ( 85 ) China Academic Journal Electronic Publishing House All rights reserved

8 7 85 [ 7 ] Madhavan A, ichardson M, oomans M Why do security prices change? A transaction level analysis of NYSE stocks [J ] eview of Financial Studies, 1997, 10 : [ 8 ] Madhavan, Smidt A bayesian model of intraday specialist pricing [J ]Journal of Financial Economics, 1991, 30 : [ 9 ] Huang, Stoll H The components of the bid2ask spread : A general approach[ ] Working paper 94-33, 1994, Owen Graduate School of Management, Vanderbilt University [10 ] Lyons K Tests of microstructural hypotheses in the foreign exchange market [J ] Journal of Financial Economics, 1995, 39 : [11 ] Manaster, Mann Life in the pits : Competitive market making and inventory control [J ] eview of Financial Studies, 1996, 9 : [12 ] Chan Yue Cheong Adverse selection, inventory cost and market depth : An empirical analysis of intraday price movement in the stock exchange of Hong Kong[D] The Hong Kong University of Science and Technology, 2000 [13 ] De Jong,Nijman Price effects of trading and components of the bid2ask spread on the paris bourse [J ] Journal of Empirical Finance, 1996, 3 : [14 ] Glosten L Is the electronic open limit order book inevitable [J ] Journal of Finance, 1994, 49 : [15 ],, [J ],2004, 13(3) : Mu qiguo, Wu chongfeng, Liu hailong Components of the bid2ask spread in the Shenzhen stock exchange [ J ] Systems Engineering - Theory Methodology Application, 2004, 13(3) : [16 ] [D] :,2000 Yang zhishu Theoretical and Empirical Evidence of the Liquidity in China Stock Markets2a Market Microstructure Analysis [ D ] Beijing :Tsinghua University,2000 [17 ] Easley D, O Hara M Price, Trade size and information in securities markets[j ] Journal of Finance, 1987,47 : [18 ] Kyle A continuous auction and insider trading [J ] Econometrica, 1985, 53 : [19 ] Glosten L Components of the bid2ask spread and the statistical properties of transaction prices [J ] Journal of Finance, 1987, 42 : [20 ] Hausman J, Lo A, MacKinlay A An ordered probit analysis of transaction stock prices [J ] Journal of Financial Economics, 1992, 31 : [21 ] Lee G, eady M J Inferring trading direction from intraday data [J ] Journal of Finance, 1991, 46 : ( 53 ) [ 6 ] Maurice Clerc Discrete particle swarm optimization by the traveling salesman problem [ DB ] http :ΠΠwww Mauriceclerc net, 2000 [ 7 ] Salmen A, Ahmad I, Al2Madani B Particle swarm optimization for task assignment problem [ J ] Microprocessors and Microsystems, 2002, 26(4) : [ 8 ],, [J ],2004,14 : Li Ning, Zou Tong, Sun Debao Particle swarm optimization for vehicle routing problem with time windows [J ] J of Systems Engineering, 2004, 4 : [ 9 ],, [J ],2004,19(5) : Wan Fucai, Wang Dingwei, Li Yanping Particle swarm optimization of correlative product combinatorial introduction model [J ] Control and Decision, 19(5) : [10 ] Kenney J Small worlds and mega2minds : Effects of neighborhood topology on particle swarm performance [A] Proceedings of the Congress on Evolutionary Computation[ C] Piscataway, NJ : IEEE Press, China Academic Journal Electronic Publishing House All rights reserved

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