Methodology for the Multi-Objective Analysis of Data Sets in Freight Delivery
Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.
[1] European Commission, European Transport Policy for 2011: Roadmap to a single European Transport Area-Towards a Competitive and Resource Efficient Transport System, Brussels: European Commission, 2011.
[2] G. Carrion, C. Nitzl, & J. Roldan, "Mediation analyses in partial least squares structural equation modeling: Guidelines and empirical examples," in Partial least squares path modeling, Cham: Springer, pp. 173–195, 2017.
[3] C. Lin, P. Tsai, K. Lai, & J. Chen, "Cloud removal from multitemporal satellite images using information cloning," IEEE Trans. Geosci. Remot. Sens., vol. 51, no. 1, pp. 232-241, January 2013.
[4] A. Y. Jackson, & L. A. Mazzei, Thinking with theory in qualitative research: Viewing data across multiple perspectives, London: Routledge, 2012.
[5] S. P. Greaves, & M. A. Figliozzi, "Collecting commercial vehicle tour data with passive global positioning system technology: Issues and potential applications," Transpor. Res. Recor., vol. 2049, no. 1, pp. 158-166, 2008.
[6] J. Broach, J. Dill, & J. Gliebe, "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transport. Res. Part A: Pol. and Prac., vol. 46, no. 10, pp. 1730-1740, 2012.
[7] A. Kuppam, J. Lemp, D. Beagan, V. Livshits, L. Vallabhaneni, & S. Nippani, "Development of a tour-based truck travel demand model using truck GPS data," in Ann. Meet. 93rd on Transport. Res. Board, pp. 1-10, January 2014.
[8] G. Dievulis, "An approach of solving the problem of flow distribution in transport network," Transp. Engin., vol. 1, no. 10, 1995.
[9] A. Andziulis, S. Jakovlev D. Adomaitis, D. Dzemydienė. "Integration of Mobile Control Systems into Intermodal Container Transportation Management," Transport, 27(1), 40-48, 2012.
[10] P. Sung-Young, & L. Chul-Young, "A Study of the Forecasting of Container Volume Using Neural Network," Journ. of Kor. Navig. and Port Resear., vol. 26, no. 2, pp. 182-188, 2002.
[11] W.-Y. Peng, & C.-W. Chu, "A comparison of univariate methods for forecasting container throughput volumes," Math. and Comp. Model. vol. 50 pp. 1045-1057, 2009.
[12] B. L. Bowerman, & R. T. O'Connel, Forecasting and Time Series: An Applied Approach, 3rd ed., Duxbury Press, Belmont, CA, 1993.
[13] S. Bagdonas, Container flow forecasting studies, Klaipeda: Klaipeda University, pp. 2-37, 2013.
[14] S. Razminas, Research of real-time transport route optimization algorithms, Kaunas: Kaunas university of technology, pp. 5-39, 2006.
[15] I. D. Psychas, M. Marinaki, & Y. Marinakis, "A parallel multi-start NSGA II algorithm for multi-objective energy reduction vehicle routing problem," in Int. Conf. on Evol. Multi-Criter. Opt., pp. 336–350, 2015.
[16] D. M. Pierre, & N. Zakaria, "Partially optimized cyclic shift crossover for multi-objective genetic algorithms for the multi-objective vehicle routing problem with time windows," in Conf. 2014 IEEE on Comput. Intellig. in Multi-Criter. Dec.-Mak., pp. 106–115, 2014.
[17] J. Zhang, & J. Li, "A hybrid genetic algorithm to the vehicle routing problem with fuzzy cost coefficients," in Int. Conf. 2014 IEEE on Fuzzy Syst. & Knowl. Disc., pp. 147–152, 2014.
[18] V. S. Kumar, M. R. Thansekhar, R. Saravanan, & S. M. J. Amali, "Solving multi-objective vehicle routing problem with time windows by FAGA," in Conf. Engin., pp. 2176–2185, 2014.
[19] R. Liu, Z. Jiang, & N. Geng, "A hybrid genetic algorithm for the multi-depot open vehicle routing problem," OR Spectr., vol. 36, no. 2, pp. 401–421, 2014.
[20] C. M. Mohr, Optimization of warehouse order-picking routes using vehicle routing model & genetic algorithm: doctoral dissertation. New York: State University of New York at Binghamton, 2014.
[21] Y. G. Cai, Y. L. Tang, & Q. J. Yang, "An improved genetic algorithm for multi-depot heterogeneous vehicle routing problem with simultaneous pickup & delivery time windows," App. Mech. Mat., 738, pp. 361–365, 2015.
[22] S. Karakatic, & V. Podgorelec, "A survey of genetic algorithms for solving multi depot vehicle routing problem," App. Soft Comp., vol. 27, pp. 519–532, 2015.
[23] F. Ahmadizar, M. Zeynivand, & J. Arkat, "Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach," App. Math. Model., vol. 39, no. 22, pp. 7065–7081, 2015.
[24] M. Wen, J. F. Cordeau, G. Laporte, & J. Larsen, "The dynamic multi-period vehicle routing problem," Comp. & Oper. Res., vol. 37, no. 9, pp. 1615-1623, 2010.
[25] M. Shen, D.-R. Liu, & S.-H. Shann, "Outlier detection from vehicle trajectories to discover roaming events," Inf. Scien., vol. 2, pp. 294, 2015.
[26] A. K. Minga, "Genetic algorithms in aerospace design," in Conf of AIAA Southeastern Regional Student, pp. 3-87, 1986.
[27] D. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Massachusetts: Addison-Wesley Publishing Company, 1989.
[28] S. Hartmann, "A competitive genetic algorithm for resource-constrained project scheduling," Nav. Res. Logist., vol. 45, pp. 733–750, 1998.
[29] M. Khouja, Z. Michalewicz, M. Wilmot, "The use of genetic algorithms to solve the economic lot size scheduling problem," Europ. Jour. of oper. resear., vol. 110, pp. 509-524, 1998.
[30] S. Hartmann, "A self-adapting genetic algorithm for project scheduling under resource constraints," Nav. Res. Logist., vol. 49, pp. 433-448, 2002.
[31] R. Chelouah, & P. Siarry, "Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions," Europ. Jour. of oper. resear., vol. 148, pp. 335-348, 2003.
[32] Y. Z.Wang, "Using genetic algorithm methods to solve course scheduling problems," Exp. syst. with applic., vol. 25, pp. 39-50, 2003.
[33] S. K. Iyer, & B. Saxena, "Improved genetic algorithm for the permutation flowshop scheduling problem," Comp. and oper. resear., vol. 31, pp. 593-606, 2004.
[34] H. Yu, & F. Lu. "A multi-modal route planning approach with an improved genetic algorithm." Advances in geo-spatial information science 193, 2012
[35] G. Felinskas, Euristinių metodų tyrimas ir taikymas ribotų išteklių tvarkaraščiams optimizuoti: doctoral dissertation, Kaunas: Vytautas Magnum University, 2007.
[36] T. Murata, & H. Ishibuchi, "MOGA: Multi-objective genetic algorithms," Proc. of 1995 IEEE International Conference on Evolutionary Computation, pp. 289-294, Perth, Australia, November 1995.
[37] S. Fazayeli, A. Eydi, K. Alireza; & N. Isa, "Location-routing problem in multi-modal transportation network with time windows and fuzzy demands: presenting a two-part genetic algorithm," Comp. & Indust. Engin., vol. 119, pp. 233-246, 2018.
[1] European Commission, European Transport Policy for 2011: Roadmap to a single European Transport Area-Towards a Competitive and Resource Efficient Transport System, Brussels: European Commission, 2011.
[2] G. Carrion, C. Nitzl, & J. Roldan, "Mediation analyses in partial least squares structural equation modeling: Guidelines and empirical examples," in Partial least squares path modeling, Cham: Springer, pp. 173–195, 2017.
[3] C. Lin, P. Tsai, K. Lai, & J. Chen, "Cloud removal from multitemporal satellite images using information cloning," IEEE Trans. Geosci. Remot. Sens., vol. 51, no. 1, pp. 232-241, January 2013.
[4] A. Y. Jackson, & L. A. Mazzei, Thinking with theory in qualitative research: Viewing data across multiple perspectives, London: Routledge, 2012.
[5] S. P. Greaves, & M. A. Figliozzi, "Collecting commercial vehicle tour data with passive global positioning system technology: Issues and potential applications," Transpor. Res. Recor., vol. 2049, no. 1, pp. 158-166, 2008.
[6] J. Broach, J. Dill, & J. Gliebe, "Where do cyclists ride? A route choice model developed with revealed preference GPS data," Transport. Res. Part A: Pol. and Prac., vol. 46, no. 10, pp. 1730-1740, 2012.
[7] A. Kuppam, J. Lemp, D. Beagan, V. Livshits, L. Vallabhaneni, & S. Nippani, "Development of a tour-based truck travel demand model using truck GPS data," in Ann. Meet. 93rd on Transport. Res. Board, pp. 1-10, January 2014.
[8] G. Dievulis, "An approach of solving the problem of flow distribution in transport network," Transp. Engin., vol. 1, no. 10, 1995.
[9] A. Andziulis, S. Jakovlev D. Adomaitis, D. Dzemydienė. "Integration of Mobile Control Systems into Intermodal Container Transportation Management," Transport, 27(1), 40-48, 2012.
[10] P. Sung-Young, & L. Chul-Young, "A Study of the Forecasting of Container Volume Using Neural Network," Journ. of Kor. Navig. and Port Resear., vol. 26, no. 2, pp. 182-188, 2002.
[11] W.-Y. Peng, & C.-W. Chu, "A comparison of univariate methods for forecasting container throughput volumes," Math. and Comp. Model. vol. 50 pp. 1045-1057, 2009.
[12] B. L. Bowerman, & R. T. O'Connel, Forecasting and Time Series: An Applied Approach, 3rd ed., Duxbury Press, Belmont, CA, 1993.
[13] S. Bagdonas, Container flow forecasting studies, Klaipeda: Klaipeda University, pp. 2-37, 2013.
[14] S. Razminas, Research of real-time transport route optimization algorithms, Kaunas: Kaunas university of technology, pp. 5-39, 2006.
[15] I. D. Psychas, M. Marinaki, & Y. Marinakis, "A parallel multi-start NSGA II algorithm for multi-objective energy reduction vehicle routing problem," in Int. Conf. on Evol. Multi-Criter. Opt., pp. 336–350, 2015.
[16] D. M. Pierre, & N. Zakaria, "Partially optimized cyclic shift crossover for multi-objective genetic algorithms for the multi-objective vehicle routing problem with time windows," in Conf. 2014 IEEE on Comput. Intellig. in Multi-Criter. Dec.-Mak., pp. 106–115, 2014.
[17] J. Zhang, & J. Li, "A hybrid genetic algorithm to the vehicle routing problem with fuzzy cost coefficients," in Int. Conf. 2014 IEEE on Fuzzy Syst. & Knowl. Disc., pp. 147–152, 2014.
[18] V. S. Kumar, M. R. Thansekhar, R. Saravanan, & S. M. J. Amali, "Solving multi-objective vehicle routing problem with time windows by FAGA," in Conf. Engin., pp. 2176–2185, 2014.
[19] R. Liu, Z. Jiang, & N. Geng, "A hybrid genetic algorithm for the multi-depot open vehicle routing problem," OR Spectr., vol. 36, no. 2, pp. 401–421, 2014.
[20] C. M. Mohr, Optimization of warehouse order-picking routes using vehicle routing model & genetic algorithm: doctoral dissertation. New York: State University of New York at Binghamton, 2014.
[21] Y. G. Cai, Y. L. Tang, & Q. J. Yang, "An improved genetic algorithm for multi-depot heterogeneous vehicle routing problem with simultaneous pickup & delivery time windows," App. Mech. Mat., 738, pp. 361–365, 2015.
[22] S. Karakatic, & V. Podgorelec, "A survey of genetic algorithms for solving multi depot vehicle routing problem," App. Soft Comp., vol. 27, pp. 519–532, 2015.
[23] F. Ahmadizar, M. Zeynivand, & J. Arkat, "Two-level vehicle routing with cross-docking in a three-echelon supply chain: A genetic algorithm approach," App. Math. Model., vol. 39, no. 22, pp. 7065–7081, 2015.
[24] M. Wen, J. F. Cordeau, G. Laporte, & J. Larsen, "The dynamic multi-period vehicle routing problem," Comp. & Oper. Res., vol. 37, no. 9, pp. 1615-1623, 2010.
[25] M. Shen, D.-R. Liu, & S.-H. Shann, "Outlier detection from vehicle trajectories to discover roaming events," Inf. Scien., vol. 2, pp. 294, 2015.
[26] A. K. Minga, "Genetic algorithms in aerospace design," in Conf of AIAA Southeastern Regional Student, pp. 3-87, 1986.
[27] D. E. Goldberg, Genetic Algorithm in Search, Optimization and Machine Learning, Massachusetts: Addison-Wesley Publishing Company, 1989.
[28] S. Hartmann, "A competitive genetic algorithm for resource-constrained project scheduling," Nav. Res. Logist., vol. 45, pp. 733–750, 1998.
[29] M. Khouja, Z. Michalewicz, M. Wilmot, "The use of genetic algorithms to solve the economic lot size scheduling problem," Europ. Jour. of oper. resear., vol. 110, pp. 509-524, 1998.
[30] S. Hartmann, "A self-adapting genetic algorithm for project scheduling under resource constraints," Nav. Res. Logist., vol. 49, pp. 433-448, 2002.
[31] R. Chelouah, & P. Siarry, "Genetic and Nelder-Mead algorithms hybridized for a more accurate global optimization of continuous multiminima functions," Europ. Jour. of oper. resear., vol. 148, pp. 335-348, 2003.
[32] Y. Z.Wang, "Using genetic algorithm methods to solve course scheduling problems," Exp. syst. with applic., vol. 25, pp. 39-50, 2003.
[33] S. K. Iyer, & B. Saxena, "Improved genetic algorithm for the permutation flowshop scheduling problem," Comp. and oper. resear., vol. 31, pp. 593-606, 2004.
[34] H. Yu, & F. Lu. "A multi-modal route planning approach with an improved genetic algorithm." Advances in geo-spatial information science 193, 2012
[35] G. Felinskas, Euristinių metodų tyrimas ir taikymas ribotų išteklių tvarkaraščiams optimizuoti: doctoral dissertation, Kaunas: Vytautas Magnum University, 2007.
[36] T. Murata, & H. Ishibuchi, "MOGA: Multi-objective genetic algorithms," Proc. of 1995 IEEE International Conference on Evolutionary Computation, pp. 289-294, Perth, Australia, November 1995.
[37] S. Fazayeli, A. Eydi, K. Alireza; & N. Isa, "Location-routing problem in multi-modal transportation network with time windows and fuzzy demands: presenting a two-part genetic algorithm," Comp. & Indust. Engin., vol. 119, pp. 233-246, 2018.
@article{"International Journal of Information, Control and Computer Sciences:80349", author = "Dale Dzemydiene and Aurelija Burinskiene and Arunas Miliauskas and Kristina Ciziuniene", title = "Methodology for the Multi-Objective Analysis of Data Sets in Freight Delivery", abstract = "Data flow and the purpose of reporting the data are different and dependent on business needs. Different parameters are reported and transferred regularly during freight delivery. This business practices form the dataset constructed for each time point and contain all required information for freight moving decisions. As a significant amount of these data is used for various purposes, an integrating methodological approach must be developed to respond to the indicated problem. The proposed methodology contains several steps: (1) collecting context data sets and data validation; (2) multi-objective analysis for optimizing freight transfer services. For data validation, the study involves Grubbs outliers analysis, particularly for data cleaning and the identification of statistical significance of data reporting event cases. The Grubbs test is often used as it measures one external value at a time exceeding the boundaries of standard normal distribution. In the study area, the test was not widely applied by authors, except when the Grubbs test for outlier detection was used to identify outsiders in fuel consumption data. In the study, the authors applied the method with a confidence level of 99%. For the multi-objective analysis, the authors would like to select the forms of construction of the genetic algorithms, which have more possibilities to extract the best solution. For freight delivery management, the schemas of genetic algorithms' structure are used as a more effective technique. Due to that, the adaptable genetic algorithm is applied for the description of choosing process of the effective transportation corridor. In this study, the multi-objective genetic algorithm methods are used to optimize the data evaluation and select the appropriate transport corridor. The authors suggest a methodology for the multi-objective analysis, which evaluates collected context data sets and uses this evaluation to determine a delivery corridor for freight transfer service in the multi-modal transportation network. In the multi-objective analysis, authors include safety components, the number of accidents a year, and freight delivery time in the multi-modal transportation network. The proposed methodology has practical value in the management of multi-modal transportation processes.
", keywords = "Multi-objective decision support, analysis, data validation, freight delivery, multi-modal transportation, genetic programming methods. ", volume = "15", number = "6", pages = "224-8", }
