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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 7/2020

08.08.2019 | Original Article

Analyzing multimodal transportation problem and its application to artificial intelligence

verfasst von: Gurupada Maity, Sankar Kumar Roy, José Luis Verdegay

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

In recent decades, there has been increased interest among both transportation researchers and practitioners in exploring the application of artificial intelligence (AI) paradigms to address the real-life problems in order to improve the efficiency, safety and environmental compatibility of transportation systems. In this paper, our main interest is to solve transportation problem by considering the multimodal transport systems and then utilize it to solve neural network (NN) problem in AI. The multimodal transportation problem (MMTP) is nothing but a linear programming problem, and so it is easy to solve by any simplex algorithm. To analyze the proposed method, a numerical example is included and solving it we reveal a better impact for analyzing the real-life decision-making problems. Thereafter, we revoke our approach for solving NN problems, which enhances a connection between MMTP and NN problems. Finally, conclusion and future research directions are presented regarding our study.
Vorheriger Artikel Classification of sonar echo signals in their reduced sparse forms using complex-valued wavelet neural network
Nächster Artikel Design of active noise control system using hybrid functional link artificial neural network and finite impulse response filters

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Literatur
1.
2.
Hitchcock FL (1941) The distribution of a product from several sources to numerous localities. J Math Phys 20:224–230MathSciNetCrossRef
3.
James JHL, Hsu CC, Chen YS (2014) Improving transportation service quality based on information fusion. Transp Res Part A: Policy Pract 67:225–239
4.
Ergun O, Kuyzu G, Savelsbergh M (2007) Reducing truckload transportation tosts through collaboration. Transp Sci 41(2):206–221CrossRef
5.
Luathep P, Sumalee A, Lam WHK, Li ZC, Lo HK (2011) Global optimization method for mixed transportation network design problem: a mixed-integer linear programming approach. Transp Res Part B: Methodol 45(5):808–827CrossRef
6.
Sheu JB, Chen YJ (2014) Transportation and economics of scale in recycling low-value materials. Transp Res Part B: Methodol 65:65–76CrossRef
7.
Roy SK, Maity G, Weber GW (2017) Multi-objective two-stage grey transportation problem using utility function with goals. Central Eur J Oper Res 25(2):417–439MathSciNetCrossRef
8.
Roy SK, Maity G, Weber GW, Alparslan Gök SZ (2017) Conic scalarization approach to solve multi-choice multi-objective transportation problem with interval goal. Ann Oper Res 253(1):599–620MathSciNetCrossRef
9.
Mahapatra DR, Roy SK (2011) Multi-objective interval-valued transportation probabilistic problem involving log-normal. Int J Math Sci Comput 1(2):14–21MathSciNet
10.
Roy SK (2014) Multi-choice stochastic transportation problem involving weibul distribution. Int J Oper Res 21(1):38–58MathSciNetCrossRef
11.
Maity G, Roy SK, Verdegay JL (2016) Multi-objective transportation problem with cost reliability under uncertain environment. Int J Comput Intell Syst 9(5):839–849CrossRef
12.
Maity G, Roy SK (2016) Solving a multi-objective transportation problem with nonlinear cost and multi-choice demand. Int J Manag Sci Eng Manag 11(1):62–70
13.
Nanry WP, Wesley JB (2000) Solving the pickup and delivery problem with time windows using reactive tabu search. Transp Res Part B: Methodol 34(2):107–121CrossRef
14.
Macharis C, Bontekoning YM (2004) Opportunities for OR in intermodal freight transport research: a review. Eur J Oper Res 153:400–416CrossRef
15.
Eibl P, Mackenzie R, Kidner D (1994) Vehicle routing and scheduling in the brewing industry. Int J Phys Distrib Logist Manag 24(6):27–37CrossRef
16.
Catalani M (2003) Transport competition on a multi-modal corridor by elasticity evaluation. University of Naples, Parthenope
17.
Qu L, Chen Y (2008) A hybrid MCDM method for route selection of multi-modal transportation network. Part I, LNCS 5263, Springer, Berlin, 374–383
18.
Haykin S (1994) Neural networks: a comprehensive foundation. Macmillan College Publishing Company Inc
19.
Ham FM, Kostanic I (2001) Principles of neurocomputing for science and engineering. McGraw Hill, New York
20.
Duda R, Hart P, Stork D (2001) Pattern classification, 2nd edn. Wiley, New YorkMATH
21.
Omkar SN, Khandelwal R, Yathindra S, Naik GN, Gopalakrishnan S (2008) Artificial immune system for multi-objective design optimization of composite structures. Eng Appl Artif Intell 21(8):1416–1429CrossRef
22.
Tan KC, Lee LH, Ou K (2001) Artificial intelligence heuristics in solving vehicle routing problems with time window constraints. Eng Appl Artif Intell 14(6):825–837CrossRef
23.
Yaghini M, Khoshraftar MM, Fallahi M (2013) A hybrid algorithm for artificial neural network training. Eng Appl Artif Intell 26(1):293–301CrossRef
24.
Effati S, Pakdaman M, Ranjbar M (2011) A new fuzzy neural network model for solving fuzzy linear programming problems and its applications. Neural Comput Appl 20:1285–1294CrossRef
25.
26.
27.
Veloso MM (1994) Learning by analogical reasoning in general problem-solving. Topics on case-based reasoning. Springer, Berlin, pp 33–50
28.
Kleinschmidt P, Schannath H (1995) A strongly polynomial algorithm for the transportation problem. Math Program 68:1–13MathSciNetMATH
29.
Mustafa K, Ali M, Kamoun F (1993) Neural networks for shortest path computation and routing in computer networks. IEEE Trans Neural Netw 4(6):941–954CrossRef
30.
31.
Zhang J, Zhao X, He X (2014) A minimum resource neural network framework for solving multiconstraint shortest path problems. IEEE Trans Neural Netw Learn Syst 25(8):1566–1582CrossRef
32.
33.
Araujo F, Ribeiro B, Rodrigues L (2001) A neural network for shortest path computation. IEEE Trans Neural Netw 12(5):1067–1073CrossRef
Metadaten
Titel
Analyzing multimodal transportation problem and its application to artificial intelligence
verfasst von
Gurupada Maity
Sankar Kumar Roy
José Luis Verdegay
Publikationsdatum
08.08.2019
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-019-04393-5

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