|Table of Contents|

Safety scheduling of hazardous materials transportation vehicle considering spatio-temporal dissimilarity(PDF)

《交通运输工程学报》[ISSN:1671-1637/CN:61-1369/U]

Issue:
2019年03期
Page:
145-156
Research Field:
交通运输规划与管理
Publishing date:

Info

Title:
Safety scheduling of hazardous materials transportation vehicle considering spatio-temporal dissimilarity
Author(s):
CHAI Huo HE Rui-chun DAI Cun-jie MA Chang-xi
(School of Traffic and Transportation, Lanzhou Jiaotong University, Lanzhou 730070, Gansu, China)
Keywords:
transportation planning vehicle scheduling spatio-temporal dissimilarity hazardous material transportation dissimilar route transport safety
PACS:
U492.3
DOI:
-
Abstract:
To ensure a safety distance between the hazardous materials transportation vehicles, the travel routes and departure time intervals of hazardous materials transportation vehicles were optimized in term of space-time. The impact of hazardous material transportation vehicle accident on other vehicles and the relationship between the hazardous material transportation vehicle accident and the spatio-temporal distance were analyzed, an evaluation method of spatio-temporal safety distance between vehicles was proposed, and taking the spatio-temporal safty distance as a constraint, the calculation method of vehicle safety departure time interval was proposed. A scheduling model of hazardous material transportation vehicle satisfying the spatio-temporal dissimilarity constraint was established. A two-stage solution method was designed to generate the vehicle scheduling timetable. The NSGA-Ⅱ optimization algorithm was used to optimize the travel route of vehicle at the first stage. The genetic algorithm and approximation algorithm based on the inserting thought were designed to optimize the departure time interval at the second stage. To verify the effectiveness of vehicle scheduling model and algorithm, the advantages and disadvantages of different methods at each stage were compared, and the influences of hazardous material accident impact factor and accident impact acceptance on the scheduling results were analyzed. Research result shows that the proposed method can obtain hazardous material transportation vehicle scheduling timetables with different hazardous material accident impact factors, and always ensure the vehicles a safe distance during driving. The average total transportation times obtained by the genetic algorithm and approximation algorithm are 2.45 and 2.49 h, respectively, indicating that the optimal solution of approximation algorithm is inferior to that of genetic algorithm, but the run time is only 1/10 000-1/5 000 of that of genetic algorithm. The smaller the hazardous material accident impact factor or the accident impact acceptance, the larger the vehicle safety departure time interval is, which leads to a longer total transportation time. The vehicle scheduling considering the spatio-temporal dissimilarity can compensate for the deficiency of dissimilar routing method only considering the spatial dissimilarity. At the same time, using the dissimilar routing method can prevent the problem of missing the optimal transportation route. 4 tabs, 15 figs, 34 refs.

References:

[1] 赵 岩.诊治危险品运输事故急症[J].中国公路,2016(3):69-71.
ZHAO Yan. Diagnosis and treatment of emergency for dangerous goods transportation accident[J]. China Highway, 2016(3): 69-71.(in Chinese)
[2] KEENEY R L. Equity and public risk[J]. Operations Research, 1980, 28(3): 527-534.
[3] AKGÜN V, ERKUT E, BATTA R. On finding dissimilar paths[J]. European Journal of Operational Research, 2000,121(2): 232-246.
[4] LOMBARD K, CHURCH R L. The gateway shortest path problem: generating alternative routes for a corridor location problem[J]. Geographical Systems, 1993, 1(1): 25-45.
[5] KUBY M, XU Z Y, XIE X D. A minimax method for finding the k best “differentiated” paths[J]. Geographical Analysis, 1997, 29(4): 298-313.
[6] 何瑞春,李引珍.最佳相异度相异最短路径的遗传算法[J].兰州交通大学学报(自然科学版),2005,24(3):116-119.
HE Rui-chun, LI Yin-zhen. Genetic algorithms for finding dissimilar shortest paths based on best dissimilar measure[J]. Journal of Lanzhou Jiaotong University(Natural Science), 2005, 24(3): 116-119.(in Chinese)
[7] DELL'OLMO P, GENTILI M, SCOZZARI A. On finding dissimilar Pareto-optimal paths[J]. European Journal of Operational Research, 2005, 162(1): 70-82.
[8] LI Yin-zhen, HE Rui-chun, LIU Lin-zhong, et al. Genetic algorithms for dissimilar shortest paths based on optimal fuzzy dissimilar measure and applications[C]∥Springer. International Conference on Fuzzy Systems and Knowledge Discovery. Berlin: Springer, 2005: 312-320.
[9] 李引珍,何瑞春,郭耀煌,等.多目标网络相异路径的Pareto解及其遗传算法[J].系统工程学报,2008,23(3):264-268.
LI Yin-zhen, HE Rui-chun, GUO Yao-huang, et al. Pareto solution set and its genetic algorithm for multi-objective network dissimilar paths[J]. Journal of Systems Engineering, 2008, 23(3): 264-268.(in Chinese)
[10] 马昌喜,何瑞春,熊瑞琦.基于双层规划的危险货物配送路径鲁棒优化[J].交通运输工程学报,2018,18(5):165-175.
MA Chang-xi, HE Rui-chun, XIONG Rui-qi. Robust optimization on distributing routes of hazardous materials based on bi-level programming[J]. Journal of Traffic and Transportation Engineering, 2018, 18(5): 165-175.(in Chinese)
[11] 代存杰,李引珍,马昌喜,等.考虑风险分布特征的危险品运输路径优化[J].中国公路学报,2018,31(4):330-342.
DAI Cun-jie, LI Yin-zhen, MA Chang-xi, et al. Transportation path optimization for hazardous materials considering characteristics of risk distribution[J]. China Journal of Highway and Transport, 2018, 31(4): 330-342.(in Chinese)
[12] LIM Y, RHEE S. An efficient dissimilar path searching
method for evacuation routing[J]. KSCE Journal of Civil Engineering, 2010, 14(1): 61-67.
[13] KANG Y Y, BATTA R, KWON C. Generalized route
planning model for hazardous material transportation with VaR and equity considerations[J]. Computers and Operations Research, 2014, 43(1): 237-247.
[14] TALARICO L, SÖRENSEN K, SPRINGAEL J. The k-dissimilar vehicle routing problem[J]. European Journal of Operational Research, 2015, 244(1): 129-140.
[15] PUSHAK Y, HARE W, LUCET Y. Multiple-path selection for new highway alignments using discrete algorithms[J]. European Journal of Operational Research, 2016, 248(2): 415-427.
[16] YEN J Y. Finding the k-shortest loopless paths in a network[J]. Management Science, 1971, 17(11): 712-716.
[17] XU W, HE S, RUI S, et al. Finding the k shortest paths in a schedule-based transit network[J]. Computers and Operations Research, 2012, 39(8): 1812-1826.
[18] LAWLER E L. A procedure for computing the k best solutions to discrete optimization problems and its application to the shortest path problem[J]. Management Science, 1972, 18(7): 401-405.
[19] LIU Lin-zhong, MU Hai-bo, YANG Ju-hua. Simulated
annealing based GRASP for Pareto-optimal dissimilar paths problem[J]. Soft Computing, 2017, 21(18): 5457-5473.
[20] MARTÍ R, VELARDE J L G, DUARTE A. Heuristics for the bi-objective path dissimilarity problem[J]. Computers and Operations Research, 2009, 36(11): 2905-2912.
[21] CONSTANTINO M, MOURÃO M C, PINTO L S. Dissimilar arc routing problems[J]. Networks, 2017, 70(3): 233-245.
[22] THYAGARAJAN K, BATTA R, KARWAN M H, et al. Planning dissimilar paths for military units[J]. Military Operations Research, 2005, 10(1): 25-42.
[23] YAN S Y, WANG S S, WU M W. A model with a solution algorithm for the cash transportation vehicle routing and scheduling problem[J]. Computers and Industrial Engineering, 2012, 63(2): 464-473.
[24] BATTA R, CHIU S S. Optimal obnoxious paths on a network: transportation of hazardous materials[J]. Operations Research, 1988, 36(1): 84-92.
[25] 陆 键,刘禹杰,马晓丽.基于博弈论的危险品运输网络选线[J].中国公路学报,2018,31(4):322-329.
LU Jian, LIU Yu-jie, MA Xiao-li. Game-theory-based hazardous materials transport network routing[J]. China Journal of Highway and Transport, 2018, 31(4): 322-329.(in Chinese)
[26] 王 磊,华 珺,杨云峰,等.道路危险货物运输的系统动力学仿真研究[J].中国公路学报,2018,31(8):181-188,196.
WANG Lei, HUA Jun, YANG Yun-feng, et al. Study on system dynamics simulation of road dangerous cargo transportation[J]. China Journal of Highway and Transport, 2018, 31(8): 181-188, 196.(in Chinese)
[27] CAROTENUTO P, GIORDANI S, RICCIARDELLI S. Finding minimum and equitable risk routes for hazmat shipments[J]. Computers and Operations Research, 2007, 34(5): 1304-1327.
[28] DEB K, PRATAP A, AGARWAL S, et al. A fast and elitist multiobjective genetic algorithm: NSGA-Ⅱ[J]. IEEE Transactions on Evolutionary Computation, 2002, 6(2): 182-197.
[29] PULIDO F J, MANDOW L, DE LA CRUZ J L P.
Multiobjective shortest path problems with lexicographic goal-based preferences[J]. European Journal of Operational Research, 2014, 239(1): 89-101.
[30] ALP E. Risk-based transportation planning practice: overall methodology and a case example[J]. INFOR: Information Systems and Operational Research, 1995, 33(1): 4-19.
[31] GARRIDO R A, BRONFMAN A C. Equity and social
acceptability in multiple hazardous materials routing through urban areas[J]. Transportation Research Part A: Policy and Practice, 2017, 102: 244-260.
[32] MACHUCA E, MANDOW L, DE LA CRUZ J L P, et al. Heuristic multiobjective search for hazmat transportation problems[C]∥Springer. Conference of the Spanish Association for Artificial Intelligence. Berlin: Springer, 2011: 243-252.
[33] DUQUE D, LOZANO L, MEDAGLIA A L. An exact method for the biobjective shortest path problem for large-scale road networks[J]. European Journal of Operational Research, 2015, 242(3): 788-797.
[34] GUO X L, VERMA M. Choosing vehicle capacity to minimize risk for transporting flammable materials[J]. Journal of Loss Prevention in the Process Industries, 2010, 23(2): 220-225.

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Last Update: 2019-06-27