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Multi-objective optimization model for planning metro-based underground logistics system network: Nanjing case study
1. | School of Management, Harbin Institute of Technology, Harbin 150001, China |
2. | College of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China |
3. | School of Management, Harbin Institute of Technology, Harbin 150001, China |
4. | College of Civil Engineering, Nanjing Tech University, Nanjing 211800, China |
Utilizing rail transit system for collaborative passenger-and-freight transport is a sustainable option to conquer urban congestion. This study proposes effective modeling and optimization techniques for planning a city-wide metro-based underground logistics system (M-ULS) network. Firstly, a novel metro prototype integrating retrofitted underground stations and newly-built capsule pipelines is designed to support automated inbound delivery from urban logistics gateways to in-city destinations. Based on four indicators (i.e. unity of freight flows, regional accessibility, environmental cost-saving, and order priority), an entropy-based fuzzy TOPSIS evaluation model is proposed to select appropriate origin-destination flows for underground freight transport. Then, a mixed integer programming model, with a well-matched solution framework combining multi-objective PSO algorithm and A* algorithm, are developed to optimize the location-allocation-routing (LAR) decisions of M-ULS network. Finally, real-world simulation based on Nanjing metro case is conducted for validation. The best facility configurations and flow assignments of the three-tier M-ULS network are reported in details. Results confirm that the proposed algorithm has good ability in providing high-quality Pareto-optimal LAR decisions. Moreover, the Nanjing M-ULS project shows strong economic feasibility while bringing millions of Yuan of annual external benefit to the society and environment.
References:
[1] |
A. B. Arabani, M. Zandieh and S. M. T. F. Ghomi,
Multi-objective genetic-based algorithms for a cross-docking scheduling problem, Applied Soft Computing, 11 (2011), 4954-4970.
doi: 10.1016/j.asoc.2011.06.004. |
[2] |
W. Behiri, S. Belmokhtar-Berraf and C. Chu,
Urban freight transport using passenger rail network: Scientific issues and quantitative analysis, Transportation Research Part E: Logistics and Transportation Review, 115 (2018), 227-245.
doi: 10.1016/j.tre.2018.05.002. |
[3] |
C. Cleophas, C. Cottrill, J. F. Ehmke and K. Tierney,
Collaborative urban transportation: Recent advances in theory and practice, European J. Oper. Res., 273 (2019), 801-816.
doi: 10.1016/j.ejor.2018.04.037. |
[4] |
P. Chen,
Effects of normalization on the entropy-based TOPSIS method, Expert Systems with Applications, 136 (2019), 33-41.
doi: 10.1016/j.eswa.2019.06.035. |
[5] |
J. Cui, J. Dodson and P. V. Hall, Planning for urban freight transport: An overview, Transport Reviews, 35, (2015), 583–598.
doi: 10.1080/01441647.2015.1038666. |
[6] |
N. Coulombel, L. Dablanc, M. Gardrat and M. Koning, The environmental social cost of urban road freight: Evidence from the Paris region, Transportation Research Part D: Transport and Environment, 63, (2018), 514–532.
doi: 10.1016/j.trd.2018.06.002. |
[7] |
Z. Chen, J. Dong and R. Ren,
Urban underground logistics system in China: Opportunities or challenges?, Underground Space, 2 (2017), 195-208.
doi: 10.1016/j.undsp.2017.08.002. |
[8] |
J. Dong, Y. Xu, B. Hwang, R. Ren and Z. Chen,
The impact of underground logistics system on urban sustainable development: A system dynamics approach, Sustainability, 11 (2019), 1223.
doi: 10.3390/su11051223. |
[9] |
J. Dong, W. Hu, S. Yan, R. Ren and X. Zhao, Network planning method for capacitated metro-based underground logistics system, Advances in Civil Engineering, 2018 (2018), Article ID 6958086.
doi: 10.1155/2018/6958086. |
[10] |
A. Dampier and M. Marinov,
A study of the feasibility and potential implementation of metro-based freight transportation in newcastle upon tyne, Urban Rail Transit, 1 (2015), 164-182.
doi: 10.1007/s40864-015-0024-7. |
[11] |
L, Dablanc, Freight Transport for Development Toolkit: Urban Freight, The World Bank, Washington, DC. 2009 |
[12] |
A. Devari, A. G. Nikolaev and Q, He, Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers, Transportation Research. Part E, Logistics and Transportation Review, 105, (2017), 105–122.
doi: 10.1016/j.tre.2017.06.011. |
[13] |
O. N. Egbunike and A. T. Potter,
Are freight pipelines a pipe dream? A critical review of the UK and European perspective, J. Transport Geography, 19 (2021), 499-508.
doi: 10.1016/j.jtrangeo.2010.05.004. |
[14] |
K. Govindan, M. Fattahi and E. Keyvanshokooh,
Supply chain network design under uncertainty: A comprehensive review and future research directions, European J. Oper. Res., 263 (2017), 108-141.
doi: 10.1016/j.ejor.2017.04.009. |
[15] |
W. Hu, J. Dong, B. Hwang, R. Ren and Z. Chen,
A preliminary prototyping approach for emerging metro-based underground logistics systems: Operation mechanism and facility layout, International J. Production Research, 19 (2020), 1-21.
doi: 10.1080/00207543.2020.1844333. |
[16] |
C. L. Hwang and K. Yoon, Methods for multiple attribute decision making, In Multiple Attribute Decision Making, Springer, Berlin, Heidelberg, (1981), 58–191. |
[17] |
W. Hu, J. Dong, B. Hwang, R. Ren, Y. Chen and Z. Chen,
Using system dynamics to analyze the development of urban freight transportation system based on rail transit: A case study of Beijing, Sustainable Cities and Society, 53 (2020), 101923.
doi: 10.1016/j.scs.2019.101923. |
[18] |
P. E. Hart, N. J. Nilsson and B. Raphael,
A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on Systems Science and Cybernetics, 4 (1968), 100-107.
doi: 10.1109/TSSC.1968.300136. |
[19] |
T. Howgego and M. Roe,
The use of pipelines for the urban distribution of goods, Transport Policy, 5 (1998), 61-72.
doi: 10.1016/S0967-070X(98)00012-2. |
[20] |
J. Kennedy and R. Eberhart,
Particle swarm optimization, Proceedings of ICNN'95-International Conference on Neural Networks, 4 (1995), 1942-1948.
doi: 10.1109/ICNN.1995.488968. |
[21] |
M. Mokhtarzadeh, R. Tavakkoli-Moghaddam, C. Triki and Y. Rahimi,
A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location-allocation problem with the depreciation cost of hub facilities, Engineering Applications of Artificial Intelligence, 98 (2021), 104121.
doi: 10.1016/j.engappai.2020.104121. |
[22] |
A. Memari, A. R. A. Rahim, N. Absi, R. Ahmad and A. Hassan,
Carbon-capped distribution planning: A JIT perspective, Computers & Industrial Engineering, 97 (2016), 111-127.
doi: 10.1016/j.cie.2016.04.015. |
[23] |
M. Najafi, S. Ardekani and S. M. Shahandashti, Integrating Underground Freight Transportation into Existing Intermodal Systems, 2016. Available from: https://library.ctr.utexas.edu/hostedpdfs/uta/0-6870-1.pdf. |
[24] |
G. Nagy and S. Salhi,
Location-routing: Issues, models and methods, European J. Oper. Res., 177 (2007), 649-672.
doi: 10.1016/j.ejor.2006.04.004. |
[25] |
D. Paddeu and G. Parkhurst, The potential for automation to transform urban deliveries: Drivers, barriers and policy priorities, Advances in Transport Policy and Planning, 5, (2020), 291–314.
doi: 10.1016/bs.atpp.2020.01.003. |
[26] |
P. Potti and M. Marinov,
Evaluation of actual timetables and utilization levels of west midlands metro using event-based simulations, Urban Rail Transit, 6 (2020), 28-41.
doi: 10.1007/s40864-019-00120-4. |
[27] |
H. Quak, N. Nesterova and T. van Rooijen, Possibilities and barriers for using electric-powered vehicles in city logistics practice, Transportation Research Procedia, 12, (2016), 157–169.
doi: 10.1016/j.trpro.2016.02.055. |
[28] |
M. Strale, The Cargo Tram: Current Status and Perspectives, the Example of Brussels, Sustainable Logistics, Emerald Group Publishing Limited, 2014.
doi: 10.1108/S2044-994120140000006010. |
[29] |
E. Taniguchi and R. G. Thompson, City logistics 3: Towards Sustainable and Liveable Cities, John Wiley & Sons, 2018.
doi: 10.1002/9781119425472. |
[30] |
J. G. S. N. Visser,
The development of underground freight transport: An overview, Tunnelling and Underground Space Technology, 80 (2018), 123-127.
doi: 10.1016/j.tust.2018.06.006. |
[31] |
Y. Wang, S. Zhang, X. Guan, S. Peng, H. Wang, Y. Liu and M. Xu,
Collaborative multi-depot logistics network design with time window assignment, Expert Systems with Applications, 140 (2020), 112910.
doi: 10.1016/j.eswa.2019.112910. |
[32] |
C. Zheng, X. Zhao and J. Shen,
Research on Location Optimization of Metro-Based Underground Logistics System With Voronoi Diagram, IEEE Access, 8 (2020), 34407-34417.
doi: 10.1109/ACCESS.2020.2974497. |
[33] |
L. Zhao, H. Li, M. Li, Y. Sun, Q. Hu, S. Mao, J. Li and J. Xue,
Location selection of intra-city distribution hubs in the metro-integrated logistics system, Tunnelling and Underground Space Technology, 80 (2018), 246-256.
doi: 10.1016/j.tust.2018.06.024. |
[34] |
China Federation of Logistics and Purchasing, National Logistics Operation Status Report, 2019. Available from: http://www.chinawuliu.com.cn/lhhzq/202004/20/499790.shtml. |
[35] |
European Commission, EU transport in figures. Statistical pocketbook, 2015. Available from: https://ec.europa.eu/transport/sites/transport/files/pocketbook2015.pdf. |
[36] |
Cargo Sous Terrain, 2021. Available from: https://www.cst.ch. |
[37] |
DP World CargoSpeed with Virgin Hyperloop One, 2021. Available from: https://www.dpworld.com/smart-trade/cargospeed. |
[38] |
Nanjing Municipal Postal Administration, Annual Report of Nanjing Urban Traffic, 2019. Available from: http://jsnj.spb.gov.cn/xytj_3323/tjxx/202005/t20200527_2248042.html. |
show all references
References:
[1] |
A. B. Arabani, M. Zandieh and S. M. T. F. Ghomi,
Multi-objective genetic-based algorithms for a cross-docking scheduling problem, Applied Soft Computing, 11 (2011), 4954-4970.
doi: 10.1016/j.asoc.2011.06.004. |
[2] |
W. Behiri, S. Belmokhtar-Berraf and C. Chu,
Urban freight transport using passenger rail network: Scientific issues and quantitative analysis, Transportation Research Part E: Logistics and Transportation Review, 115 (2018), 227-245.
doi: 10.1016/j.tre.2018.05.002. |
[3] |
C. Cleophas, C. Cottrill, J. F. Ehmke and K. Tierney,
Collaborative urban transportation: Recent advances in theory and practice, European J. Oper. Res., 273 (2019), 801-816.
doi: 10.1016/j.ejor.2018.04.037. |
[4] |
P. Chen,
Effects of normalization on the entropy-based TOPSIS method, Expert Systems with Applications, 136 (2019), 33-41.
doi: 10.1016/j.eswa.2019.06.035. |
[5] |
J. Cui, J. Dodson and P. V. Hall, Planning for urban freight transport: An overview, Transport Reviews, 35, (2015), 583–598.
doi: 10.1080/01441647.2015.1038666. |
[6] |
N. Coulombel, L. Dablanc, M. Gardrat and M. Koning, The environmental social cost of urban road freight: Evidence from the Paris region, Transportation Research Part D: Transport and Environment, 63, (2018), 514–532.
doi: 10.1016/j.trd.2018.06.002. |
[7] |
Z. Chen, J. Dong and R. Ren,
Urban underground logistics system in China: Opportunities or challenges?, Underground Space, 2 (2017), 195-208.
doi: 10.1016/j.undsp.2017.08.002. |
[8] |
J. Dong, Y. Xu, B. Hwang, R. Ren and Z. Chen,
The impact of underground logistics system on urban sustainable development: A system dynamics approach, Sustainability, 11 (2019), 1223.
doi: 10.3390/su11051223. |
[9] |
J. Dong, W. Hu, S. Yan, R. Ren and X. Zhao, Network planning method for capacitated metro-based underground logistics system, Advances in Civil Engineering, 2018 (2018), Article ID 6958086.
doi: 10.1155/2018/6958086. |
[10] |
A. Dampier and M. Marinov,
A study of the feasibility and potential implementation of metro-based freight transportation in newcastle upon tyne, Urban Rail Transit, 1 (2015), 164-182.
doi: 10.1007/s40864-015-0024-7. |
[11] |
L, Dablanc, Freight Transport for Development Toolkit: Urban Freight, The World Bank, Washington, DC. 2009 |
[12] |
A. Devari, A. G. Nikolaev and Q, He, Crowdsourcing the last mile delivery of online orders by exploiting the social networks of retail store customers, Transportation Research. Part E, Logistics and Transportation Review, 105, (2017), 105–122.
doi: 10.1016/j.tre.2017.06.011. |
[13] |
O. N. Egbunike and A. T. Potter,
Are freight pipelines a pipe dream? A critical review of the UK and European perspective, J. Transport Geography, 19 (2021), 499-508.
doi: 10.1016/j.jtrangeo.2010.05.004. |
[14] |
K. Govindan, M. Fattahi and E. Keyvanshokooh,
Supply chain network design under uncertainty: A comprehensive review and future research directions, European J. Oper. Res., 263 (2017), 108-141.
doi: 10.1016/j.ejor.2017.04.009. |
[15] |
W. Hu, J. Dong, B. Hwang, R. Ren and Z. Chen,
A preliminary prototyping approach for emerging metro-based underground logistics systems: Operation mechanism and facility layout, International J. Production Research, 19 (2020), 1-21.
doi: 10.1080/00207543.2020.1844333. |
[16] |
C. L. Hwang and K. Yoon, Methods for multiple attribute decision making, In Multiple Attribute Decision Making, Springer, Berlin, Heidelberg, (1981), 58–191. |
[17] |
W. Hu, J. Dong, B. Hwang, R. Ren, Y. Chen and Z. Chen,
Using system dynamics to analyze the development of urban freight transportation system based on rail transit: A case study of Beijing, Sustainable Cities and Society, 53 (2020), 101923.
doi: 10.1016/j.scs.2019.101923. |
[18] |
P. E. Hart, N. J. Nilsson and B. Raphael,
A formal basis for the heuristic determination of minimum cost paths, IEEE Transactions on Systems Science and Cybernetics, 4 (1968), 100-107.
doi: 10.1109/TSSC.1968.300136. |
[19] |
T. Howgego and M. Roe,
The use of pipelines for the urban distribution of goods, Transport Policy, 5 (1998), 61-72.
doi: 10.1016/S0967-070X(98)00012-2. |
[20] |
J. Kennedy and R. Eberhart,
Particle swarm optimization, Proceedings of ICNN'95-International Conference on Neural Networks, 4 (1995), 1942-1948.
doi: 10.1109/ICNN.1995.488968. |
[21] |
M. Mokhtarzadeh, R. Tavakkoli-Moghaddam, C. Triki and Y. Rahimi,
A hybrid of clustering and meta-heuristic algorithms to solve a p-mobile hub location-allocation problem with the depreciation cost of hub facilities, Engineering Applications of Artificial Intelligence, 98 (2021), 104121.
doi: 10.1016/j.engappai.2020.104121. |
[22] |
A. Memari, A. R. A. Rahim, N. Absi, R. Ahmad and A. Hassan,
Carbon-capped distribution planning: A JIT perspective, Computers & Industrial Engineering, 97 (2016), 111-127.
doi: 10.1016/j.cie.2016.04.015. |
[23] |
M. Najafi, S. Ardekani and S. M. Shahandashti, Integrating Underground Freight Transportation into Existing Intermodal Systems, 2016. Available from: https://library.ctr.utexas.edu/hostedpdfs/uta/0-6870-1.pdf. |
[24] |
G. Nagy and S. Salhi,
Location-routing: Issues, models and methods, European J. Oper. Res., 177 (2007), 649-672.
doi: 10.1016/j.ejor.2006.04.004. |
[25] |
D. Paddeu and G. Parkhurst, The potential for automation to transform urban deliveries: Drivers, barriers and policy priorities, Advances in Transport Policy and Planning, 5, (2020), 291–314.
doi: 10.1016/bs.atpp.2020.01.003. |
[26] |
P. Potti and M. Marinov,
Evaluation of actual timetables and utilization levels of west midlands metro using event-based simulations, Urban Rail Transit, 6 (2020), 28-41.
doi: 10.1007/s40864-019-00120-4. |
[27] |
H. Quak, N. Nesterova and T. van Rooijen, Possibilities and barriers for using electric-powered vehicles in city logistics practice, Transportation Research Procedia, 12, (2016), 157–169.
doi: 10.1016/j.trpro.2016.02.055. |
[28] |
M. Strale, The Cargo Tram: Current Status and Perspectives, the Example of Brussels, Sustainable Logistics, Emerald Group Publishing Limited, 2014.
doi: 10.1108/S2044-994120140000006010. |
[29] |
E. Taniguchi and R. G. Thompson, City logistics 3: Towards Sustainable and Liveable Cities, John Wiley & Sons, 2018.
doi: 10.1002/9781119425472. |
[30] |
J. G. S. N. Visser,
The development of underground freight transport: An overview, Tunnelling and Underground Space Technology, 80 (2018), 123-127.
doi: 10.1016/j.tust.2018.06.006. |
[31] |
Y. Wang, S. Zhang, X. Guan, S. Peng, H. Wang, Y. Liu and M. Xu,
Collaborative multi-depot logistics network design with time window assignment, Expert Systems with Applications, 140 (2020), 112910.
doi: 10.1016/j.eswa.2019.112910. |
[32] |
C. Zheng, X. Zhao and J. Shen,
Research on Location Optimization of Metro-Based Underground Logistics System With Voronoi Diagram, IEEE Access, 8 (2020), 34407-34417.
doi: 10.1109/ACCESS.2020.2974497. |
[33] |
L. Zhao, H. Li, M. Li, Y. Sun, Q. Hu, S. Mao, J. Li and J. Xue,
Location selection of intra-city distribution hubs in the metro-integrated logistics system, Tunnelling and Underground Space Technology, 80 (2018), 246-256.
doi: 10.1016/j.tust.2018.06.024. |
[34] |
China Federation of Logistics and Purchasing, National Logistics Operation Status Report, 2019. Available from: http://www.chinawuliu.com.cn/lhhzq/202004/20/499790.shtml. |
[35] |
European Commission, EU transport in figures. Statistical pocketbook, 2015. Available from: https://ec.europa.eu/transport/sites/transport/files/pocketbook2015.pdf. |
[36] |
Cargo Sous Terrain, 2021. Available from: https://www.cst.ch. |
[37] |
DP World CargoSpeed with Virgin Hyperloop One, 2021. Available from: https://www.dpworld.com/smart-trade/cargospeed. |
[38] |
Nanjing Municipal Postal Administration, Annual Report of Nanjing Urban Traffic, 2019. Available from: http://jsnj.spb.gov.cn/xytj_3323/tjxx/202005/t20200527_2248042.html. |






Note | Definition | Attribute |
Notation of indices | ||
set of LPWs, i.e., set of metro lines | indexed by |
|
set of SCs, i.e., set of candidate location of UDs | indexed by |
|
set of metro stations, i.e., set of candidate location of NFSs | indexed by |
|
set of metro interchanges, i.e., set of activated IFSs | indexed by |
|
set of metro line arcs between two adjacent metro stations | indexed by |
|
set of arcs between metro stations and SCs | indexed by |
|
set of arcs between two SCs | indexed by |
|
Exogenous parameters | ||
size of delivery orders from LPW |
[0, 15] parcel per-day | |
size of delivery orders from LPW |
– | |
freight travel cost by LGV | ||
ratio of the freight travel cost by metro to the freight travel cost by LGV | 10% | |
ratio of the freight travel cost by CPs to the freight travel cost by LGV | 25% | |
underground transfer cost at IFS | ||
fixed cost for CP construction | ||
fixed cost for NFS retrofit | ||
fixed cost for UD construction | ||
allowable level for low load operations at UD | 60% | |
allowable level for low load operations at CP | 50% | |
penalty cost due to low load operations of UD | ||
penalty cost due to low load operations of CP | ||
maximal road travel distance from UD to SC | 2km | |
order handling capacity of NFS | 1 |
|
transport capacity of CP | 4 |
|
order handling capacity of UD | 3.5 |
|
order transfer capacity of IFS | 1.5 |
|
Euclidean distance of arc |
– | |
depreciation coefficient of M-ULS network facilities | 1/25550 | |
Binary variables | ||
1, if metro station |
||
1, if |
||
1, if SC |
||
1, if arc |
||
1, if the trip of |
0-1 variable | |
1, if the trip of |
||
1, if |
||
1, if |
||
1, if |
Note | Definition | Attribute |
Notation of indices | ||
set of LPWs, i.e., set of metro lines | indexed by |
|
set of SCs, i.e., set of candidate location of UDs | indexed by |
|
set of metro stations, i.e., set of candidate location of NFSs | indexed by |
|
set of metro interchanges, i.e., set of activated IFSs | indexed by |
|
set of metro line arcs between two adjacent metro stations | indexed by |
|
set of arcs between metro stations and SCs | indexed by |
|
set of arcs between two SCs | indexed by |
|
Exogenous parameters | ||
size of delivery orders from LPW |
[0, 15] parcel per-day | |
size of delivery orders from LPW |
– | |
freight travel cost by LGV | ||
ratio of the freight travel cost by metro to the freight travel cost by LGV | 10% | |
ratio of the freight travel cost by CPs to the freight travel cost by LGV | 25% | |
underground transfer cost at IFS | ||
fixed cost for CP construction | ||
fixed cost for NFS retrofit | ||
fixed cost for UD construction | ||
allowable level for low load operations at UD | 60% | |
allowable level for low load operations at CP | 50% | |
penalty cost due to low load operations of UD | ||
penalty cost due to low load operations of CP | ||
maximal road travel distance from UD to SC | 2km | |
order handling capacity of NFS | 1 |
|
transport capacity of CP | 4 |
|
order handling capacity of UD | 3.5 |
|
order transfer capacity of IFS | 1.5 |
|
Euclidean distance of arc |
– | |
depreciation coefficient of M-ULS network facilities | 1/25550 | |
Binary variables | ||
1, if metro station |
||
1, if |
||
1, if SC |
||
1, if arc |
||
1, if the trip of |
0-1 variable | |
1, if the trip of |
||
1, if |
||
1, if |
||
1, if |
Number at most | Nanjing metro case | |
Variables |
1,402 | |
Variable |
35,200 | |
Variables |
132,660 | |
Variables |
1,833,920 | |
Variables |
170,850,460 | |
Variable |
142,649 | |
Variable |
127,037,680 | |
Sum of constraints and variables | 300,033,971 |
Number at most | Nanjing metro case | |
Variables |
1,402 | |
Variable |
35,200 | |
Variables |
132,660 | |
Variables |
1,833,920 | |
Variables |
170,850,460 | |
Variable |
142,649 | |
Variable |
127,037,680 | |
Sum of constraints and variables | 300,033,971 |
LPW 1 | LPW 2 | LPW 3 | LPW 4 | |
Accessed metro line | Line 1 | Line 2 | Line 3 | Line 4 |
Total demand orders ( |
1,237 | 1,028 | 1,141 | 654 |
Average value of |
0.3025 | 0.269 | 0.3207 | 0.3436 |
Average value of |
0.3025 | 0.269 | 0.3207 | 0.3436 |
Maximum value of |
0.9471 | 0.9226 | 0.8901 | 0.9284 |
Average value of |
58.47 | 60.2 | 58.01 | 21.83 |
Average value of |
36.59 | 37.98 | 41.31 | 22.84 |
Average value of |
1,332 | 1,997 | 2,189 | 462 |
Average value of |
13,921 | 10,380 | 8,990 | 3,897 |
Size of orders inputted into metro | 704 | 647 | 621 | 446 |
Served SC number | 255 | 265 | 264 | 271 |
Utilization rate of metro line | 93.9% | 86.3% | 82.8% | 59.5% |
Fulfillment rate of underground logistics | 57.9% | 60.2% | 60% | 61.6% |
LPW 1 | LPW 2 | LPW 3 | LPW 4 | |
Accessed metro line | Line 1 | Line 2 | Line 3 | Line 4 |
Total demand orders ( |
1,237 | 1,028 | 1,141 | 654 |
Average value of |
0.3025 | 0.269 | 0.3207 | 0.3436 |
Average value of |
0.3025 | 0.269 | 0.3207 | 0.3436 |
Maximum value of |
0.9471 | 0.9226 | 0.8901 | 0.9284 |
Average value of |
58.47 | 60.2 | 58.01 | 21.83 |
Average value of |
36.59 | 37.98 | 41.31 | 22.84 |
Average value of |
1,332 | 1,997 | 2,189 | 462 |
Average value of |
13,921 | 10,380 | 8,990 | 3,897 |
Size of orders inputted into metro | 704 | 647 | 621 | 446 |
Served SC number | 255 | 265 | 264 | 271 |
Utilization rate of metro line | 93.9% | 86.3% | 82.8% | 59.5% |
Fulfillment rate of underground logistics | 57.9% | 60.2% | 60% | 61.6% |
ID | Station full name | |||||||
Line 1 | NFS-1 | Er-Qiao-Gong-Yuan | 11 | 6 | 40.5 | 6.8 | 5.5 | 4.63 |
NFS-2 | Ba-Dou-Shan | 17 | 5 | 44.9 | 9 | 8.87 | 11.23 | |
NFS-3 | Yan-Zi-Ji | 17 | 9 | 80.5 | 8.9 | 13.14 | 6.6 | |
NFS-4 | Xin-Mo-Fan-Ma-Lu | 11 | 4 | 98.5 | 24.6 | 7.44 | 8.18 | |
NFS-5 | Xuan-Wu-Men | 9 | 3 | 77.8 | 25.9 | 3.36 | 3.87 | |
NFS-6 | Zhang-Fu-Yuan | 8 | 6 | 67.6 | 11.3 | 4.29 | 1.68 | |
NFS-7 | San-Shan-Jie | 4 | 3 | 37 | 12.3 | 3.66 | 0.84 | |
NFS-8 | Zhong-Hua-Men | 6 | 2 | 35 | 17.5 | 3.95 | 4.87 | |
NFS-9 | Ruan-Jian-Da-Dao | 9 | 4 | 44.9 | 11.2 | 6.01 | 4.51 | |
NFS-10 | Hua-Shen-Miao | 8 | 5 | 39.4 | 7.9 | 4.66 | 2.26 | |
NFS-11 | Sheng-Tai-Lu | 18 | 4 | 93.1 | 23.3 | 9.58 | 12.85 | |
NFS-12 | Zhu-Shan-Lu | 32 | 12 | 95.4 | 8 | 20.2 | 17.29 | |
NFS-13 | Nan-Jing-Jiao-Yuan | 9 | 4 | 35.1 | 8.8 | 4.34 | 5.23 | |
Line 2 | NFS-14 | Qing-Lian-Jie | 8 | 1 | 32.1 | 32.1 | 0.29 | 10.04 |
NFS-15 | You-Fang-Qiao | 15 | 4 | 71.8 | 18 | 3.09 | 11.65 | |
NFS-16 | Yuan-Tong | 6 | 3 | 44.8 | 14.9 | 3.39 | 2.07 | |
NFS-17 | Xiong-Long-Da-Jie | 13 | 7 | 84.6 | 12.1 | 8.6 | 6.29 | |
NFS-18 | Yun-Jing-Lu | 14 | 7 | 90.5 | 12.9 | 10.99 | 6.45 | |
NFS-19 | Virtual station | 8 | 4 | 49.6 | 12.4 | 3.48 | 3.19 | |
NFS-20 | Ming-Gu-Gong | 19 | 9 | 79.3 | 8.8 | 13.83 | 9.47 | |
NFS-21 | Xia-Ma-Fang | 5 | 1 | 34 | 34 | 2.17 | 3.27 | |
NFS-22 | Ma-Qun | 13 | 3 | 55.6 | 18.5 | 2.45 | 10.65 | |
NFS-23 | Xian-Lin-Zhong-Xin | 14 | 8 | 44.8 | 5.6 | 12.54 | 4.49 | |
Line 3 | NFS-24 | Virtual station | 7 | 2 | 38.4 | 19.2 | 2.53 | 5.55 |
NFS-25 | Fu-Qiao | 9 | 6 | 59.4 | 9.9 | 5.65 | 2.46 | |
NFS-26 | Virtual station | 2 | 1 | 34.8 | 34.8 | 0.29 | 0.36 | |
NFS-27 | Ka-Zi-Men | 11 | 7 | 74.1 | 10.6 | 9.41 | 2.82 | |
NFS-28 | Hong-Yun-Da-Dao | 5 | 2 | 41.6 | 20.8 | 1.65 | 1.72 | |
NFS-29 | Tian-Yuan-Xi-Lu | 26 | 9 | 98.4 | 10.9 | 13.02 | 12.26 | |
NFS-30 | Cheng-Xin-Da-Dao | 24 | 9 | 97.8 | 10.9 | 11.06 | 16.12 | |
Line 4 | NFS-31 | Hui-Tong-Lu | 16 | 6 | 85.1 | 14.2 | 11.63 | 9.38 |
NFS-32 | Wang-Jia-Wan | 4 | 1 | 33.4 | 33.4 | 1.48 | 2.7 | |
NFS-33 | Gang-Zi-Cun | 7 | 3 | 31.5 | 10.5 | 3.23 | 3.9 | |
NFS-34 | Yun-Nan-Lu | 11 | 6 | 96 | 16 | 9.76 | 2.61 | |
IFS-1 & NFS-35 | Nan-Jing-Zhan | 13 | 9 | 83.8 | 9.3 | 11 | 3.09 | |
IFS-2 & NFS-36 | Gu-Lou | 7 | 5 | 91 | 18.2 | 6.93 | 0.99 | |
IFS-3 & NFS-37 | Xin-Jie-Kou | 8 | 4 | 89.3 | 22.3 | 4.91 | 2.55 | |
IFS-4 & NFS-38 | Nan-Jing-Nan-Zhan | 8 | 3 | 67.4 | 22.5 | 3.01 | 4.54 | |
IFS-5 & NFS-39 | Da-Xing-Gong | 8 | 4 | 46 | 11.5 | 2.38 | 1.92 | |
IFS-6 | Jin-Ma-Lu | – | – | – | – | – | – | |
IFS-7 | Ji-Ming-Si | – | – | – | – | – | – | |
ID | Station full name | |||||||
Line 1 | NFS-1 | Er-Qiao-Gong-Yuan | 21.6 | 1.39 | 2.08 | 39 | 13.2 | – |
NFS-2 | Ba-Dou-Shan | 20 | 3.78 | 7.64 | 30.2 | 11 | – | |
NFS-3 | Yan-Zi-Ji | 37.8 | 4.41 | 6.28 | 0 | 11.1 | – | |
NFS-4 | Xin-Mo-Fan-Ma-Lu | 56.3 | 6.49 | 19.69 | 0 | 0 | – | |
NFS-5 | Xuan-Wu-Men | 27.7 | 3.4 | 7.1 | 0 | 0 | – | |
NFS-6 | Zhang-Fu-Yuan8 | 42.9 | 2.29 | 1.57 | 0 | 8.7 | – | |
NFS-7 | San-Shan-Jie | 13.2 | 2.33 | 0.64 | 46 | 7.7 | – | |
NFS-8 | Zhong-Hua-Men | 16.4 | 3.8 | 9.11 | 50 | 2.5 | – | |
NFS-9 | Ruan-Jian-Da-Dao | 19.4 | 4.44 | 2.09 | 30.2 | 8.8 | – | |
NFS-10 | Hua-Shen-Miao | 22.5 | 2.48 | 1.1 | 41.2 | 12.1 | – | |
NFS-11 | Sheng-Tai-Lu | 53.2 | 12.18 | 27.86 | 0 | 0 | – | |
NFS-12 | Zhu-Shan-Lu | 59.4 | 9.99 | 14.86 | 0 | 12 | – | |
NFS-13 | Nan-Jing-Jiao-Yuan | 12.5 | 1.89 | 3.04 | 49.8 | 11.2 | – | |
Line 2 | NFS-14 | Qing-Lian-Jie | 17.9 | 0.52 | 13.54 | 55.8 | 0 | – |
NFS-15 | You-Fang-Qiao | 40.1 | 3.29 | 5.16 | 0 | 2 | – | |
NFS-16 | Yuan-Tong | 17.1 | 2.34 | 1.30 | 30.4 | 5.1 | – | |
NFS-17 | Xiong-Long-Da-Jie | 33.3 | 4.87 | 5.53 | 0 | 7.9 | – | |
NFS-18 | Yun-Jing-Lu | 56.3 | 6.45 | 8.99 | 0 | 7.1 | – | |
NFS-19 | Virtual station | 20.2 | 2.92 | 2.97 | 20.8 | 7.6 | – | |
NFS-20 | Ming-Gu-Gong | 38.3 | 7.19 | 8.4 | 0 | 11.2 | – | |
NFS-21 | Xia-Ma-Fang | 3.69 | 5.67 | 52 | 0 | – | ||
NFS-22 | Ma-Qun | 35.3 | 2.68 | 8.16 | 8.8 | 1.5 | – | |
NFS-23 | Xian-Lin-Zhong-Xin | 27.9 | 4.41 | 2.41 | 30.4 | 14.4 | – | |
Line 3 | NFS-24 Virtual station | 13.7 | 2.41 | 5.69 | 43.2 | 0.8 | – | |
NFS-25 | Fu-Qiao | 25.6 | 3.48 | 2.24 | 1.2 | 10.1 | – | |
NFS-26 | Virtual station | 11 | 0.61 | 0.86 | 50.4 | 0 | – | |
NFS-27 | Ka-Zi-Men | 40.5 | 3.67 | 1.15 | 0 | 9.4 | – | |
NFS-28 | Hong-Yun-Da-Dao | 18 | 2.06 | 2.71 | 36.8 | 0 | – | |
NFS-29 | Tian-Yuan-Xi-Lu | 51.2 | 6 | 4.89 | 0 | 9.1 | – | |
NFS-30 | Cheng-Xin-Da-Dao | 53.4 | 6.58 | 7.59 | 0 | 9.1 | – | |
Line 4 | NFS-31 Hui-Tong-Lu | 33.5 | 11.11 | 5.42 | 0 | 5.8 | – | |
NFS-32 | Wang-Jia-Wan | 15.3 | 2.65 | 4.76 | 53.2 | 0 | – | |
NFS-33 | Gang-Zi-Cun | 15.2 | 1.17 | 4.33 | 57 | 9.5 | – | |
NFS-34 | Yun-Nan-Lu | 53.6 | 9.16 | 4.06 | 0 | 4 | – | |
IFS-1 & NFS-35 | Nan-Jing-Zhan | 36.2 | 5.02 | 1.13 | 0 | 10.7 | 871 | |
IFS-2 & NFS-36 | Gu-Lou 40.4 | 7.34 | 1.58 | 0 | 1.8 | 759 | ||
IFS-3 & NFS-37 | Xin-Jie-Kou | 47.6 | 4.93 | 4.01 | 0 | 0 | 1,259 | |
IFS-4 & NFS-38 | Nan-Jing-Nan-Zhan | 24 | 2.96 | 9.24 | 0 | 0 | 804 | |
IFS-5 & NFS-39 | Da-Xing-Gong | 16.4 | 1.87 | 1.95 | 28 | 8.5 | 1,090 | |
IFS-6 | Jin-Ma-Lu | – | – | – | – | – | 565 | |
IFS-7 | Ji-Ming-Si | – | – | – | – | – | 622 | |
1 number of SCs allocated to NFS; 2 number of UDs covered by NFS; 3 total size of orders handled by NFS ( 4 average size of orders handled by UD ( 5 length of CP segments connected to NFS (km); 6 average service radius of UD (km). 7 transport cost of NFS orders on first-tier M-ULS network ( 8 transport cost of NFS orders on second-tier M-ULS network ( 9 transport cost of NFS orders on third-tier M-ULS network ( 10 penalty cost of NFS ( 11 penalty cost of CP segments connected to NFS ( 12 size of orders transferred at IFS ( |
ID | Station full name | |||||||
Line 1 | NFS-1 | Er-Qiao-Gong-Yuan | 11 | 6 | 40.5 | 6.8 | 5.5 | 4.63 |
NFS-2 | Ba-Dou-Shan | 17 | 5 | 44.9 | 9 | 8.87 | 11.23 | |
NFS-3 | Yan-Zi-Ji | 17 | 9 | 80.5 | 8.9 | 13.14 | 6.6 | |
NFS-4 | Xin-Mo-Fan-Ma-Lu | 11 | 4 | 98.5 | 24.6 | 7.44 | 8.18 | |
NFS-5 | Xuan-Wu-Men | 9 | 3 | 77.8 | 25.9 | 3.36 | 3.87 | |
NFS-6 | Zhang-Fu-Yuan | 8 | 6 | 67.6 | 11.3 | 4.29 | 1.68 | |
NFS-7 | San-Shan-Jie | 4 | 3 | 37 | 12.3 | 3.66 | 0.84 | |
NFS-8 | Zhong-Hua-Men | 6 | 2 | 35 | 17.5 | 3.95 | 4.87 | |
NFS-9 | Ruan-Jian-Da-Dao | 9 | 4 | 44.9 | 11.2 | 6.01 | 4.51 | |
NFS-10 | Hua-Shen-Miao | 8 | 5 | 39.4 | 7.9 | 4.66 | 2.26 | |
NFS-11 | Sheng-Tai-Lu | 18 | 4 | 93.1 | 23.3 | 9.58 | 12.85 | |
NFS-12 | Zhu-Shan-Lu | 32 | 12 | 95.4 | 8 | 20.2 | 17.29 | |
NFS-13 | Nan-Jing-Jiao-Yuan | 9 | 4 | 35.1 | 8.8 | 4.34 | 5.23 | |
Line 2 | NFS-14 | Qing-Lian-Jie | 8 | 1 | 32.1 | 32.1 | 0.29 | 10.04 |
NFS-15 | You-Fang-Qiao | 15 | 4 | 71.8 | 18 | 3.09 | 11.65 | |
NFS-16 | Yuan-Tong | 6 | 3 | 44.8 | 14.9 | 3.39 | 2.07 | |
NFS-17 | Xiong-Long-Da-Jie | 13 | 7 | 84.6 | 12.1 | 8.6 | 6.29 | |
NFS-18 | Yun-Jing-Lu | 14 | 7 | 90.5 | 12.9 | 10.99 | 6.45 | |
NFS-19 | Virtual station | 8 | 4 | 49.6 | 12.4 | 3.48 | 3.19 | |
NFS-20 | Ming-Gu-Gong | 19 | 9 | 79.3 | 8.8 | 13.83 | 9.47 | |
NFS-21 | Xia-Ma-Fang | 5 | 1 | 34 | 34 | 2.17 | 3.27 | |
NFS-22 | Ma-Qun | 13 | 3 | 55.6 | 18.5 | 2.45 | 10.65 | |
NFS-23 | Xian-Lin-Zhong-Xin | 14 | 8 | 44.8 | 5.6 | 12.54 | 4.49 | |
Line 3 | NFS-24 | Virtual station | 7 | 2 | 38.4 | 19.2 | 2.53 | 5.55 |
NFS-25 | Fu-Qiao | 9 | 6 | 59.4 | 9.9 | 5.65 | 2.46 | |
NFS-26 | Virtual station | 2 | 1 | 34.8 | 34.8 | 0.29 | 0.36 | |
NFS-27 | Ka-Zi-Men | 11 | 7 | 74.1 | 10.6 | 9.41 | 2.82 | |
NFS-28 | Hong-Yun-Da-Dao | 5 | 2 | 41.6 | 20.8 | 1.65 | 1.72 | |
NFS-29 | Tian-Yuan-Xi-Lu | 26 | 9 | 98.4 | 10.9 | 13.02 | 12.26 | |
NFS-30 | Cheng-Xin-Da-Dao | 24 | 9 | 97.8 | 10.9 | 11.06 | 16.12 | |
Line 4 | NFS-31 | Hui-Tong-Lu | 16 | 6 | 85.1 | 14.2 | 11.63 | 9.38 |
NFS-32 | Wang-Jia-Wan | 4 | 1 | 33.4 | 33.4 | 1.48 | 2.7 | |
NFS-33 | Gang-Zi-Cun | 7 | 3 | 31.5 | 10.5 | 3.23 | 3.9 | |
NFS-34 | Yun-Nan-Lu | 11 | 6 | 96 | 16 | 9.76 | 2.61 | |
IFS-1 & NFS-35 | Nan-Jing-Zhan | 13 | 9 | 83.8 | 9.3 | 11 | 3.09 | |
IFS-2 & NFS-36 | Gu-Lou | 7 | 5 | 91 | 18.2 | 6.93 | 0.99 | |
IFS-3 & NFS-37 | Xin-Jie-Kou | 8 | 4 | 89.3 | 22.3 | 4.91 | 2.55 | |
IFS-4 & NFS-38 | Nan-Jing-Nan-Zhan | 8 | 3 | 67.4 | 22.5 | 3.01 | 4.54 | |
IFS-5 & NFS-39 | Da-Xing-Gong | 8 | 4 | 46 | 11.5 | 2.38 | 1.92 | |
IFS-6 | Jin-Ma-Lu | – | – | – | – | – | – | |
IFS-7 | Ji-Ming-Si | – | – | – | – | – | – | |
ID | Station full name | |||||||
Line 1 | NFS-1 | Er-Qiao-Gong-Yuan | 21.6 | 1.39 | 2.08 | 39 | 13.2 | – |
NFS-2 | Ba-Dou-Shan | 20 | 3.78 | 7.64 | 30.2 | 11 | – | |
NFS-3 | Yan-Zi-Ji | 37.8 | 4.41 | 6.28 | 0 | 11.1 | – | |
NFS-4 | Xin-Mo-Fan-Ma-Lu | 56.3 | 6.49 | 19.69 | 0 | 0 | – | |
NFS-5 | Xuan-Wu-Men | 27.7 | 3.4 | 7.1 | 0 | 0 | – | |
NFS-6 | Zhang-Fu-Yuan8 | 42.9 | 2.29 | 1.57 | 0 | 8.7 | – | |
NFS-7 | San-Shan-Jie | 13.2 | 2.33 | 0.64 | 46 | 7.7 | – | |
NFS-8 | Zhong-Hua-Men | 16.4 | 3.8 | 9.11 | 50 | 2.5 | – | |
NFS-9 | Ruan-Jian-Da-Dao | 19.4 | 4.44 | 2.09 | 30.2 | 8.8 | – | |
NFS-10 | Hua-Shen-Miao | 22.5 | 2.48 | 1.1 | 41.2 | 12.1 | – | |
NFS-11 | Sheng-Tai-Lu | 53.2 | 12.18 | 27.86 | 0 | 0 | – | |
NFS-12 | Zhu-Shan-Lu | 59.4 | 9.99 | 14.86 | 0 | 12 | – | |
NFS-13 | Nan-Jing-Jiao-Yuan | 12.5 | 1.89 | 3.04 | 49.8 | 11.2 | – | |
Line 2 | NFS-14 | Qing-Lian-Jie | 17.9 | 0.52 | 13.54 | 55.8 | 0 | – |
NFS-15 | You-Fang-Qiao | 40.1 | 3.29 | 5.16 | 0 | 2 | – | |
NFS-16 | Yuan-Tong | 17.1 | 2.34 | 1.30 | 30.4 | 5.1 | – | |
NFS-17 | Xiong-Long-Da-Jie | 33.3 | 4.87 | 5.53 | 0 | 7.9 | – | |
NFS-18 | Yun-Jing-Lu | 56.3 | 6.45 | 8.99 | 0 | 7.1 | – | |
NFS-19 | Virtual station | 20.2 | 2.92 | 2.97 | 20.8 | 7.6 | – | |
NFS-20 | Ming-Gu-Gong | 38.3 | 7.19 | 8.4 | 0 | 11.2 | – | |
NFS-21 | Xia-Ma-Fang | 3.69 | 5.67 | 52 | 0 | – | ||
NFS-22 | Ma-Qun | 35.3 | 2.68 | 8.16 | 8.8 | 1.5 | – | |
NFS-23 | Xian-Lin-Zhong-Xin | 27.9 | 4.41 | 2.41 | 30.4 | 14.4 | – | |
Line 3 | NFS-24 Virtual station | 13.7 | 2.41 | 5.69 | 43.2 | 0.8 | – | |
NFS-25 | Fu-Qiao | 25.6 | 3.48 | 2.24 | 1.2 | 10.1 | – | |
NFS-26 | Virtual station | 11 | 0.61 | 0.86 | 50.4 | 0 | – | |
NFS-27 | Ka-Zi-Men | 40.5 | 3.67 | 1.15 | 0 | 9.4 | – | |
NFS-28 | Hong-Yun-Da-Dao | 18 | 2.06 | 2.71 | 36.8 | 0 | – | |
NFS-29 | Tian-Yuan-Xi-Lu | 51.2 | 6 | 4.89 | 0 | 9.1 | – | |
NFS-30 | Cheng-Xin-Da-Dao | 53.4 | 6.58 | 7.59 | 0 | 9.1 | – | |
Line 4 | NFS-31 Hui-Tong-Lu | 33.5 | 11.11 | 5.42 | 0 | 5.8 | – | |
NFS-32 | Wang-Jia-Wan | 15.3 | 2.65 | 4.76 | 53.2 | 0 | – | |
NFS-33 | Gang-Zi-Cun | 15.2 | 1.17 | 4.33 | 57 | 9.5 | – | |
NFS-34 | Yun-Nan-Lu | 53.6 | 9.16 | 4.06 | 0 | 4 | – | |
IFS-1 & NFS-35 | Nan-Jing-Zhan | 36.2 | 5.02 | 1.13 | 0 | 10.7 | 871 | |
IFS-2 & NFS-36 | Gu-Lou 40.4 | 7.34 | 1.58 | 0 | 1.8 | 759 | ||
IFS-3 & NFS-37 | Xin-Jie-Kou | 47.6 | 4.93 | 4.01 | 0 | 0 | 1,259 | |
IFS-4 & NFS-38 | Nan-Jing-Nan-Zhan | 24 | 2.96 | 9.24 | 0 | 0 | 804 | |
IFS-5 & NFS-39 | Da-Xing-Gong | 16.4 | 1.87 | 1.95 | 28 | 8.5 | 1,090 | |
IFS-6 | Jin-Ma-Lu | – | – | – | – | – | 565 | |
IFS-7 | Ji-Ming-Si | – | – | – | – | – | 622 | |
1 number of SCs allocated to NFS; 2 number of UDs covered by NFS; 3 total size of orders handled by NFS ( 4 average size of orders handled by UD ( 5 length of CP segments connected to NFS (km); 6 average service radius of UD (km). 7 transport cost of NFS orders on first-tier M-ULS network ( 8 transport cost of NFS orders on second-tier M-ULS network ( 9 transport cost of NFS orders on third-tier M-ULS network ( 10 penalty cost of NFS ( 11 penalty cost of CP segments connected to NFS ( 12 size of orders transferred at IFS ( |
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