交通运输工程

轴辐式中国国内煤炭运输网络构建

  • 王文雅 ,
  • 李振福
展开
  • (大连海事大学 交通运输工程学院,辽宁 大连 116026)
王文雅(1990 — ),女,博士生,E-mail:wwydlmu@163.com.

收稿日期: 2019-07-06

  修回日期: 2019-09-12

  网络出版日期: 2019-09-12

基金资助

国家高技术研究发展计划项目(2016YFC142706);大连海事大学重点科研培育项目(3132019307).

Hub-and-spoke network construction of China’s domestic coal transportation

  • WANG Wen-ya ,
  • LI Zhen-fu
Expand
  • (Transportation Engineering College,Dalian Maritime University,Dalian 116026,China)

Received date: 2019-07-06

  Revised date: 2019-09-12

  Online published: 2019-09-12

摘要

提出轴辐式中国国内煤炭运输网络结构,构造一个同时考虑经济因素和环境因素的双目标轴辐式煤炭运输网络优化模型.应用精英策略非支配排序的遗传算法(NSGA-II),设定10个算例,并对比多目标粒子群优化算法(MOPSO)和多目标和声搜索算法(MOHS)对模型进行求解.结果表明,轴辐式煤炭运输网络优化模型有效,相比于MOPSO、MOHS算法,NSGA-II算法能够在更短的时间内生成较高质量的最优解,并且所得最优解二氧化碳的总排放量更少.

本文引用格式

王文雅 , 李振福 . 轴辐式中国国内煤炭运输网络构建[J]. 大连海事大学学报, 2020 , 46(1) : 75 -88 . DOI: 10.16411/j.cnki.issn1006-7736.2020.01.009

Abstract

The hub-and-spoke network structure of China’s domestic coal transportation was proposed to construct a multi-objective hub-and-spoke coal transportation network optimization model by considering both economic and environmental factors. The non-dominated sorting genetic algorithm-II (NSGA-II) was used to set 10 examples, and in comparison with the multi-objective particle swarm optimization (MOPSO) algorithm and the multi-objective harmony search (MOHS) algorithm to solve the proposed model. The results show that the effectivity of hub-and-spoke coal transportation network is effective. Compared with MOPSO and MOHS algorithms, NSGA-II algorithm can generate high-quality optimal solution in shorter time, and the total carbon dioxide emission of the optimal solution is less.

参考文献

[1] 管小俊.煤炭物流运输网络绩效评价研究[J].物流技术,2011,30(8):36- 38.
Guan X J. Performance Evaluation for Coal Logistics Transportation Network [J] Logistics Technology,2011,30(8):36- 38.(in Chinese)
[2] Mou D, Li Z. A spatial analysis of China's coal flow [J]. Energy Policy, 2012,(48): 358–368.
[3] Venka K, Eirini K V, Dimitra P, et al. An optimization model of energy and transportation systems: Assessing the high-speed rail impacts in the United States [J]. Transportation Research Part C: Emerging Technologies, 2015, (54): 131-156.
[4] Wang C, Ducruet C. Transport corridors and regional balance in China: the case of coal trade and logistics [J]. Journal of Transport Geography, 2014, (40): 3-16.
[5] Bertrand R, Philipp G, Frederic M. Economic impacts of debottlenecking congestion in the Chinese coal supply chain [J]. Energy Economics, 2016, (60): 387-399.
[6] 吕涛,王飞,刘风.中国跨区域铁路煤炭运输CO2排放及运输格局优化研究[J].中国煤炭,2017(05): 16-21+33.
Lv T, Wang F, Liu F. Study on CO2 emission from interprovincial railway coal transportation and transport pattern optimization [J]. China Coal,2017(05): 16-21+33 .(in Chinese)
[7] 武云亮,黄少鹏.我国煤炭物流网络体系优化及其政策建议[J].经济管理,2008,34(10).
Wu Y L, Huang S P. China's coal logistics network system: optimization and policy side suggestions [J]. China Coal,2008,34(10). (in Chinese)
[8] 王文雅, 李振福. 中国煤炭运输网络空间演化[J].交通运输工程学报,2019,03(19):166-177.
Wang W Y, Li Z F. Spatial evolution of coal transportation network of China [J]. Journal of Traffic and Transportation Engineering, 2019,03(19):166-177. (in Chinese)
[9] 杨光华.区域物流网络结构的演化机理与优化研究[D].南京:中南大学,2010.
Yang G H. Study on the evolution mechanism and optimization of regional logistics network structure [D]. Central South University,2010. (in Chinese)
[10] 李梦瑶,王红春.轴辐式物流网络研究综述[J].物流技术, 2018, 37(09): 1-5.
Li M Y, Wang H C. Review of Research on Hub-and-spoke Logistics Network [J]. Logistics Technology,2018, 37(09): 1-5. (in Chinese)
[11] 白雪,邓国斌.一种改进的多目标遗传算法的研究[J].轻工科技,2017,33,(09):67-68+70.
Bai X, Deng G B. Study on an improved multi-objective genetic algorithm [J]. Light Industry Science and Technology,2017,33,(09):67-68+70. (in Chinese)
[12] Murugan P, Kannan S, Baskar S. NSGA-II algorithm for multi-objective generation expansion planning problem [J]. Electr. Power Syst. Res, 2009,79 (4), 622–628.
[13] Ramesh S, Kannan S, Baskar S. Application of modified NSGA-II algorithm to multi-objective reactive power planning [J]. Applied Soft Computing, 2012,12 (2),741–753.
[14] Xu Z, Ming X G, Zheng M, et al. Cross-trained workers scheduling for field service using improved NSGA-II [J]. International journal of production research, 2015,53(4),1255–1272.
[15] Alikar N, Seyed M M, Raja A G, et al. Application of the NSGA-II algorithm to a multi-period inventory-redundancy allocation problem in a series-parallel system [J].Reliability Engineering & System Safety, 2017,160: 1-10.
[16] Wang S M, Ma S, Duan W Y. Seakeeping optimization of trimaran outrigger layout based on NSGA-II [J]. Applied Ocean Research, 2018,78: 110-122.
[17] Xu Z Z, Wang Y S, Teng Z R, et al. Low-carbon product multi-objective optimization design for meeting requirements of enterprise, user and government [J]. Journal of Cleaner Production, 2015,103: 747-758.
[18] Camara M V O, Ribeiro G M, Tosta M D C R. A pareto optimal study for the multi-objective oil platform location problem with NSGA-II [J]. Journal of Petroleum Science and Engineering, 2018,169:258-268.
[19] Jeong K, MinHyeok K, Hyunbin J, et al. Search of optimal locations for species- or group-specific primer design in DNA sequences: Non-dominated Sorting Genetic Algorithm II (NSGA-II) [J]. Ecological Informatics, 2015,29:214-220..
[20] Abouei A M, Rezvan M T. Multi-objective optimization of reliability-redundancy allocation problem with cold-standby strategy using NSGA-II [J]. Reliability Engineering & System Safety, 2018,172:225-238.
[21] Han Z, Mei Z, Li P. Multi-objective optimization and sensitivity analysis of an organic Rankine cycle coupled with a one-dimensional radial-inflow turbine efficiency prediction model[J]. Energy Conversion and Management, 2018,166: 37-47.
[22] Zangooei M H., Habibi J., Alizadehsani R. Disease Diagnosis with a hybrid method SVR using NSGA-II [J]. Neurocomputing, 2014,136:14-29.
[23] Lv J F, Jiang X B, He G H, et al. Economic and system reliability optimization of heat exchanger networks using NSGA-II algorithm [J]. Applied Thermal Engineering, 2017,124: 716-72.
[24] 张翔宇,董增川,马红亮.基于改进多目标遗传算法的小浪底水库优化调度研究[J].水电能源科学, 2017,(01):65-68.
Zhang X Y, Dong Z C, Ma H L. Study on Optimization Operation of Xiaolangdi Reservoir Based on Improved Multi-objective Genetic Algorithm [J]. Water Resources and Power. 2017,(01):65-68. (in Chinese)
[25] 李清,胡志华.基于多目标遗传算法的灾后可靠路径选择[J].浙江大学学报(工学版), 2016,(01): 33-40+47.
Li Q, Hu Z H. Reliable path selection after disaster based on multi-objective genetic algorithm [J]. Journal of Zhejiang University(Engineering Science). 2016,(01): 33-40+47. (in Chinese)
[26] 刘玉,王海起,侯金亮,等.基于多目标遗传算法的空间优化选址方法研究[J].地理空间信息, 2018,(03): 26-29+8.
Li Y, Wang H Q, Hou J L, et al. Spatial Optimal Location Method Based on Multi-objective Genetic Algorithm [J]. Geospatial Information. 2018,(03): 26-29+8. (in Chinese)
[27] 宋健,杨蕴,吴剑锋,等.混合多目标遗传算法求解地下水污染修复管理模型[J]. 环境科学学报, 2016,(09):3428-3435.
Song J, Yang Y, Wu J F,et al. A new hybrid multi-objective genetic algorithm for optimal design of groundwater remediation systems [J]. Acta Scientiae Circumstantiae, 2016,(09):3428-3435. (in Chinese)
[28] 李婷,桂行东.孙飞,等.基于Pareto多目标遗传算法的单列车定时节能研究[J].广西大学学报(自然科学版), 2017,(05):1715-1722.
Li T,Gui X D,Sun F,et al. Study on timing energy saving of single train based on Paretomulti-objective genetic algorithm [J]. Journal of Guangxi University(Natural Science Edition), 2017,(05):1715-1722. (in Chinese)
[29] 郭珊珊,郭萍,李茉.基于多目标遗传算法的渠系配水优化模型[J].中国农业大学学报,2017,22(07):71-77.
Guo S S,Guo P,Li M. Multi-objective genetic algorithm optimization model for canal scheduling [J]. Journal of China Agricultural University,2017,22(07):71-77.(in Chinese)
[30] 刘辉,钟俊.基于粒子群算法的共享单车站间调度优化方法[J].西昌学院学报(自然科学版),2019(02):67-69+102.
Liu H, Zhong J. On solutions to the problem of scheduling shared bicycles between sites based on particle swarm optimization algorithm [J]. Journal of Xichang University(Natural Science Edition),2019(02):67-69+102. (in Chinese)
[31] 吴昊,杨佳,王会颖,尹道明.求解人力资源分配问题的多目标和声搜索算法[J].计算机技术与发展,2013,23(02):65-68+72.
Wu H, Yang J, Wang H Y, et al. Multi-objective Harmony Search Algorithm for Solving Human Resource Allocation Problem [J]. Computer Technology and Development, 2013,23(02):65-68+72. (in Chinese)
[32] Mousavi S M, Sadeghi J, Niaki S T A, et al. Two parameter-tuned meta-heuristics for a discounted inventory control problem in a fuzzy environment[C]. Metselaar HSC, 2014.
[33] Tsiakis P, Shah N, Pantelides C C.Design of multi-echelon supply chain Networks under demand uncertainty [J]. Industrial & Engineering Chemistry Research, 2001,40(16), 3585–3604.
[34] Nurjanni K P, Carvalho M S, Costa L. Green supply chain design: A mathematical modeling approach based on a multi-objective optimization model [J]. International Journal of Production Economics, 2017, 183: 421-432.
文章导航

/