A Physarum-inspired approach to supply chain network design

Science China Information Sciences

Volumn 59, Issue 5, 2016, Pages

  Zhang, Xiaoge ^a,e   Adamatzky, Andrew ^b   Yang, Xin She ^c   Yang, Hai ^d   Mahadevan, Sankaran ^e   Deng, Yong ^a,e  

^a SOUTHWEST UNIVERSITY   (China)

^b UNIVERSITY OF THE WEST OF ENGLAND   (United Kingdom)

^c MIDDLESEX UNIVERSITY   (United Kingdom)

^d HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (Hong Kong)

^e VANDERBILT UNIVERSITY   (United States)

Author keywords

adaptivity; capacity investments; network optimization; Physarum; supply chain design

Indexed keywords

BIOINFORMATICS; BIOLOGY; MOLDS; NUMERICAL METHODS; SUPPLY CHAINS;

ADAPTIVITY; CAPACITY INVESTMENT; NETWORK OPTIMIZATION; PHYSARUM; SUPPLY CHAIN DESIGN;

ITERATIVE METHODS;

EID: 84963739794     PISSN: 1674733X     EISSN: 18691919     Source Type: Journal    
DOI: 10.1007/s11432-015-5417-4     Document Type: Article

References (67)

1. Liu Z, Nagurney A. Supply chain networks with global outsourcing and quick-response production under demand and cost uncertainty. Annals Oper Res, 2013, 208: 251–289
2. Zhang W, Xu D. Integrating the logistics network design with order quantity determination under uncertain customer demands. Expert Syst Appl, 2014, 41: 168–175
3. Yu M, Nagurney A. Competitive food supply chain networks with application to fresh produce. Eur J Oper Res, 2013, 224: 273–282
4. Hu Z, Du X. Lifetime cost optimization with time-dependent reliability. Eng Optim, 2014, 46: 1389–1410
5. Ma H, Suo C. A model for designing multiple products logistics networks. Int J Phys Distrib & Log Manag, 2006, 36: 127–135
6. Zhu X, Yao Q. Logistics system design for biomass-to-bioenergy industry with multiple types of feedstocks. Bioresource Tech, 2011, 102: 10936–10945
7. Santoso T, Ahmed S, Goetschalckx M, et al. A stochastic programming approach for supply chain network design under uncertainty. Eur J Oper Res, 2005, 167: 96–115
8. Zhou G, Min H, Gen M. The balanced allocation of customers to multiple distribution centers in the supply chain network: a genetic algorithm approach. Comput Ind Eng, 2002, 43: 251–261
9. Trkman P, McCormack K. Supply chain risk in turbulent environments–a conceptual model for managing supply chain network risk. Int J Prod Econ, 2009, 119: 247–258
10. Altiparmak F, Gen M, Lin L, et al. A steady-state genetic algorithm for multi-product supply chain network design. Comput Ind Eng, 2009, 56: 521–537
11. Ahmadi J A, Azad N. Incorporating location, routing and inventory decisions in supply chain network design. Transport Res Part E: Log Transport Rev, 2010, 46: 582–597
12. Nagurney A. Supply chain network design under profit maximization and oligopolistic competition. Transport Res Part E: Log Transport Rev, 2010, 46: 281–294
13. Bilgen B. Application of fuzzy mathematical programming approach to the production allocation and distribution supply chain network problem. Expert Syst Appl, 2010, 37: 4488–4495
14. Beamon B M. Supply chain design and analysis: models and methods. Int J Prod Econ, 1998, 55: 281–294
15. Sabri E H, Beamon B M. A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 2000, 28: 581–598
16. Handfield R B, Nichols E L. Supply Chain Redesign: Transforming Supply Chains into Integrated Value Systems. Upper Saddle River: FT Press, 2002
17. Nagurney A. Optimal supply chain network design and redesign at minimal total cost and with demand satisfaction. Int J Prod Econ, 2010, 128: 200–208
18. Jiang W, Yang Y, Luo Y, et al. Determining basic probability assignment based on the improved similarity measures of generalized fuzzy numbers. Int J Comput Commun Control, 2015, 10: 333–347
19. Deng Y. Generalized evidence theory. Appl Intell, 2015, 43: 530–543
20. Deng Y, Mahadevan S, Zhou D. Vulnerability assessment of physical protection systems: a bio-inspired approach. Int J Unconv Comput, 2015, 3–4: 227–243
21. Jiang W, Luo Y, Qin X, et al. An improved method to rank generalized fuzzy numbers with different left heights and right heights. J Intell Fuzzy Syst, 2015, 28: 2343–2355
22. Deng X, Hu Y, Deng Y, et al. Supplier selection using AHP methodology extended by D numbers. Expert Syst Appl, 2014, 41: 156–167
23. Deng Y, Chan F T. A new fuzzy dempster MCDM method and its application in supplier selection. Expert Syst Appl, 2011, 38: 9854–9861
24. Deng Y, Chan F T, Wu Y, et al. A new linguistic MCDM method based on multiple-criterion data fusion. Expert Syst Appl, 2011, 38: 6985–6993
25. Stephenson S L, Stempen H, Hall I. Myxomycetes: a Handbook of Slime Molds. Portland: Timber Press, 1994
26. Nakagaki T, Yamada H, Tóth Á. Intelligence: Maze-solving by an amoeboid organism. Nature, 2000, 407:470
27. Zhang X, Zhang Z, Zhang Y, et al. Route selection for emergency logistics management: a bio-inspired algorithm. Saf Sci, 2013, 54: 87–91
28. Zhang X, Zhang Y, Hu Y, et al. An adaptive amoeba algorithm for constrained shortest paths. Expert Syst Appl, 2013, 40: 7607–7616
29. Zhang X, Wang Q, Chan F T S, et al. A Physarum polycephalum optimization algorithm for the bi-objective shortest path problem. Int J Unconv Comput, 2014, 10: 143–162
30. Tero A, Kobayashi R, Nakagaki T. Physarum solver: a biologically inspired method of road-network navigation. Phys A, 2006, 363: 115–119
31. Zhang X, Huang S, Hu Y, et al. Solving 0-1 knapsack problems based on amoeboid organism algorithm. Appl Math Comput, 2013, 219: 9959–9970
32. Zhang X, Wang Q, Adamatzky A, et al. A biologically inspired optimization algorithm for solving fuzzy shortest path problems with mixed fuzzy arc lengths. J Optimiz Theory Appl, in press. doi: 10.1007/s10957-014-0542-6
33. Zhang Y, Zhang Z, Deng Y, et al. A biologically inspired solution for fuzzy shortest path problems. Appl Soft Comput, 2013, 13: 2356–2363
34. Zhang X, Liu Q, Hu Y, et al. An adaptive amoeba algorithm for shortest path tree computation in dynamic graphs. arXiv: 1311.0460. 2013
35. Gunji YP, Shirakawa T, Niizato T, et al. An adaptive and robust biological network based on the vacant-particle transportation model. J Theor Biol, 2011, 272: 187–200
36. Shirakawa T, Gunji, Y P. Computation of Voronoi diagram and collision-free path using the plasmodium of physarum polycephalum. Int J Unconv Comput, 2010, 6: 79–88
37. Shirakawa T, Gunji Y P. Emergence of morphological order in the network formation of Physarum polycephalum. Biophys Chem, 2007, 128: 253–260
38. Gao C, Lan X, Zhang X, et al. A bio-inspired methodology of identifying influential nodes in complex networks. PloS one, 2013, 8: e66732
39. Nakagaki T, Iima M, Ueda T, et al. Minimum-risk path finding by an adaptive amoebal network. Phys Rev Lett, 2007, 99: 068104
40. Adamatzky A. Route 20, autobahn 7, and slime mold: approximating the longest roads in USA and Germany with slime mold on 3-D terrains. IEEE Trans Cybernetics, 2014, 44: 126–136
41. Tero A, Yumiki K, Kobayashi R, et al. Flow-network adaptation in Physarum amoebae. Theory Biosci, 2008, 127: 89–94
42. Jones J, Adamatzky A. Computation of the travelling salesman problem by a shrinking blob. Natural Comput, 2014, 13: 1–16
43. Tero A, Takagi S, Saigusa T, et al. Rules for biologically inspired adaptive network design. Science, 2010, 327: 439–442
44. Adamatzky A, Alonso-Sanz R. Rebuilding Iberian motorways with slime mould. Biosyst, 2011, 105: 89–100
45. Adamatzky A. Bioevaluation of World Transport Networks. Singapore: World Scientific, 2012
46. Adamatzky A, Martínez G J, Chapa-Vergara S V, et al. Approximating Mexican highways with slime mould. Natural Comput, 2011, 10: 1195–1214
47. Gao C, Yan C, Zhang Z, et al. An amoeboid algorithm for solving linear transportation problem. Phys A, 2014, 398: 179–186
48. Adamatzky A, Martinez G J. Bio-imitation of Mexican migration routes to the USA with slime mould on 3D terrains. J Bionic Eng, 2013, 10: 242–250
49. Adamatzky A. Physarum Machines: Computers from Slime Mould. Singapore: World Scientific, 2010
50. Adamatzky A, Schubert T. Slime mold microfluidic logic gates. Mater Today, 2014, 17: 86–91
51. Nagurney A. A system-optimization perspective for supply chain network integration: the horizontal merger case. Transport Res Part E: Log Transport Rev, 2009, 45: 1–15
52. Nagurney A, Woolley T, Qiang Q. Multi-product supply chain horizontal network integration: models, theory, and computational results. Int Trans Oper Res, 2010, 17: 333–349
53. Nagurney A. Supply Chain Network Economics: Dynamics of Prices, Flows and Profits. Cheltenham: Edward Elgar Publishing, 2006
54. Nagurney A, Dong J, Zhang D, et al. A supply chain network equilibrium model. Transport Res Part E: Log Transport Rev, 2002, 38: 281–303
55. Nagurney A, Woolley T. Environmental and cost synergy in supply chain network integration in mergers and acquisitions. In: Multiple Criteria Decision Making for Sustainable Energy and Transportation Systems. Berlin: Springer, 2010. 57–78
56. Tero A, Kobayashi R, Nakagaki T. A mathematical model for adaptive transport network in path finding by true slime mold. J Theor Biol, 2007, 244: 553–564
57. Bell M G, Lida Y. Transportation Network Analysis. Hoboken: John Wiley & Sons, 1997
58. Si BF, Gao ZY. Modeling Network Flow and System Optimization for Traffic and Transportation System (in Chinese). Beijing: China Communications Press, 2013
59. Adamatzky A. If BZ medium did spanning trees these would be the same trees as Physarum built. Phys Lett A, 2009, 373: 952–956
60. Gunji Y P, Shirakawa T, Niizato T, et al. Minimal model of a cell connecting amoebic motion and adaptive transport networks. J Theor Biol, 2008, 253: 659–667
61. Gunji Y P, Shirakawa T, Niizato T, et al. An adaptive and robust biological network based on the vacant-particle transportation model. J Theor Biol, 2011, 272: 187–200
62. Tsompanas M A I, Sirakoulis G C. Modeling and hardware implementation of an amoeba-like cellular automaton. Bioinspir Biomim, 2012, 7: 036013
63. Tsompanas M A I, Sirakoulis G C, Adamatzky A. Evolving transport networks with cellular automata models inspired by slime mould. IEEE Trans Cybern, in press. doi: 10.1109/TCYB.2014.2361731
64. Kalogeiton V S, Papadopoulos D P, Sirakoulis G C. Hey Physarum! Can you perform SLAM? Int J Unconv Comput, 2014, 10: 271–293
65. Adamatzky A, Jones J. Road planning with slime mould: if Physarum built motorways it would route M6/M74 through Newcastle. Int J of Bifurcat Chaos, 2010, 20: 3065–3084
66. Chakravarthy H, Proch P B, Rajan R, et al. Bio inspired approach as a problem solving technique. Netw Complex Syst, 2012, 2: 14–22
67. Liu Y, Zhang Z, Gao C, et al. A physarum network evolution model based on IBTM. In: Advances in Swarm Intelligence. Berlin: Springer, 2013. 19–26