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Risk minimization inventory model with a profit target and option contracts under spot price uncertainty
A time-division distribution strategy for the two-echelon vehicle routing problem with demand blowout
1. | School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China |
2. | Technology and Equipment of Rail Transit Operation and, Maintenance Key Laboratory of Sichuan Province, Chengdu 610031, China |
3. | Avic Chengdu Aircraft Industrial (Group)Co., Ltd, Chengdu 610031, China |
4. | School of Marketing, University of Southern Mississippi, Hattiesburg, MS 39406, USA |
Based on the rapid development of e-commerce, major promotional events and holidays can lead to explosive growth in market demand and place significant pressure on distribution systems. In this study, we considered a distribution system in which products are first transported to transfer satellites from a central depot and then delivered to customers from the transfer satellites. We modeled this distribution problem as a two-echelon vehicle routing problem with demand blowout (2E-VRPDB). We adopt a time-division distribution strategy to address massive delivery demand in two phases by offering incentives to customers who accept flexible delivery dates. We propose a hybrid fireworks algorithm (HFWA) to solve the 2E-VRPDB model. This model fuses an optimal cutting algorithm with an improved fireworks algorithm. To demonstrate the effectiveness and efficiency of the proposed HFWA, we conducted comparative analysis on a genetic algorithm and ant colony algorithm using a VRP example set. Finally, we applied the proposed model and HFWA to solve a distribution problem for the Jingdong Mall in Chengdu, China. The computational results demonstrate that the proposed approach can effectively reduce logistical costs and maintain a high service level.
References:
[1] |
R. Baldacci, A. Mingozzi, R. Roberti and R. W. Clavo,
An exact algorithm for the two-echelon capacitated vehicle routing problem, Operations Research, 61 (2013), 298-314.
doi: 10.1287/opre.1120.1153. |
[2] |
A. Bevilaqua, D. Bevilaqua and K. Yamanaka,
Parallel island based Memetic Algorithm with Lin-Kernighan local search for a real-life Two-Echelon Heterogeneous Vehicle Routing Problem based on Brazilian wholesale companies, Applied Soft Computing, 76 (2019), 697-711.
doi: 10.1016/j.asoc.2018.12.036. |
[3] |
U. Breunig, R. Baldacci, R. F. Hartl and T. Vidal,
The electric two-echelon vehicle routing problem, Computers and Operations Research, 103 (2019), 198-210.
doi: 10.1016/j.cor.2018.11.005. |
[4] |
U. Breunig, V. Schmid, R. F. Hartl and T. Vidal,
A large neighbourhood based heuristic for two-echelon routing problems, Computers and Operations Research, 76 (2016), 208-225.
doi: 10.1016/j.cor.2016.06.014. |
[5] |
M.-C. Chen, P.-J. W and Y.-H. Hsu,
An effective pricing model for the congestion alleviation of e-commerce logistics, Computers and Industrial Engineering, 129 (2019), 368-376.
doi: 10.1016/j.cie.2019.01.060. |
[6] |
Double 11 constantly refreshes the imagination of Chinese market, Global times, November 12, 2019 (015). |
[7] |
P. Grangier, M. Gendreau, F. Lehuédé and L.-M. Rousseau,
An adaptive large neighborhood search for the two-echelon multiple-trip vehicle routing problem with satellite synchronization, European Journal of Operational Research, 254 (2016), 80-91.
doi: 10.1016/j.ejor.2016.03.040. |
[8] |
M. Guan, M. Cha, Y. Li, Y. Wang and J. Yu, Predicting time-bounded purchases during a mega shopping festival, 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), (2019), 1–8.
doi: 10.1109/BIGCOMP.2019.8679217. |
[9] |
X. Guo, Y. J. L. Jaramillo, J. Bloemhof-Ruwaard and G. D. H. Claassen, On integrating crowdsourced delivery in last-mile logistics: A simulation study to quantify its feasibility, Journal of Cleaner Production, 241 (2019), 118365.
doi: 10.1016/j.jclepro.2019.118365. |
[10] |
P. He and J. Li,
The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation, Applied Soft Computing, 77 (2019), 387-398.
doi: 10.1016/j.asoc.2019.01.040. |
[11] |
W. Jie, J. Yang, M. Zhang and Y. Huang,
The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology, European Journal of Operational Research, 272 (2019), 879-904.
doi: 10.1016/j.ejor.2018.07.002. |
[12] |
H. Li, L. Zhang, T. Lv and X. Chang,
The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems, Transportation Research Part B: Methodological, 94 (2016), 169-188.
doi: 10.1016/j.trb.2016.09.012. |
[13] |
H. Li, H. Wang, J. Chen and M. Bai,
Two-echelon vehicle routing problem with time windows and mobile satellites, Transportation Research Part B: Methodological, 138 (2020), 179-201.
doi: 10.1016/j.trb.2020.05.010. |
[14] |
H. Li, Y. Liu, X. Jian and Y. Lu,
The two-echelon distribution system considering the real-time transshipment capacity varying, Transportation Research Part B: Methodological, 110 (2018), 239-260.
doi: 10.1016/j.trb.2018.02.015. |
[15] |
R. Liu, L. Tao, Q. Hu and X. Xie,
Simulation-based optimisation approach for the stochastic two-echelon logistics problem, International Journal of Production Research, 55 (2017), 187-201.
doi: 10.1080/00207543.2016.1201221. |
[16] |
T. Liu, Z. Luo, H. Qin and A. Lim,
A branch-and-cut algorithm for the two-echelon capacitated vehicle routing problem with grouping constraints, European Journal of Operational Research, 266 (2018), 487-497.
doi: 10.1016/j.ejor.2017.10.017. |
[17] |
Z. Y. Ma, Y. B. Ling and J. Li,
2E-VRP Optimization Algorithm with Optimal Cutting and Full Path Matching Cross, Computer Engineering, 41 (2015), 279-285.
|
[18] |
M. Marinelli, A. Colovic and M. Dell'Orco,
A novel Dynamic programming approach for Two-Echelon Capacitated Vehicle Routing Problem in City Logistics with Environmental considerations, Transportation Research Procedia, 30 (2018), 147-156.
doi: 10.1016/j.trpro.2018.09.017. |
[19] |
E. Morganti, L. Dablancg and F. Fortin,
Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas, Research in Transportation Business and Management, 11 (2014), 23-31.
doi: 10.1016/j.rtbm.2014.03.002. |
[20] |
G. Perboli, R. Tadei and D. Vigo,
The two-echelon capacitated vehicle routing problem: Models and math-based heuristics, Transportation Science, 45 (2011), 364-380.
doi: 10.1287/trsc.1110.0368. |
[21] |
F. A. Santos, G. R. Mateus and A. S. D. Cunha,
A branch-and-cut-and-price algorithm for the two-echelon capacitated vehicle routing problem, Transportation Science, 49 (2015), 355-368.
doi: 10.1287/trsc.2013.0500. |
[22] |
M. Soysal, J. M. Bloemhof-Ruwaard and T. Bektas,
The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations, International Journal of Production Economics, 164 (2015), 366-378.
doi: 10.1016/j.ijpe.2014.11.016. |
[23] |
E. Swilley and R. E. Goldsmith,
Black Friday and Cyber Monday: Understanding consumer intentions on two major shopping days, Journal of Retailing and Consumer Services, 20 (2013), 43-50.
doi: 10.1016/j.jretconser.2012.10.003. |
[24] |
Y. Tan and Y. Zhu, Fireworks algorithm for optimization, International Conference in Swarm Intelligence, Berlin: Springer, 355–364. |
[25] |
E. B. Tirkolaee, A. Goli, A. Faridnia, M. Soltani and G.-W. Weber, Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms, Journal of Cleaner Production, 276 (2020), 122927.
doi: 10.1016/j.jclepro.2020.122927. |
[26] |
E. B. Tirkolaee, A. Goli, M. Pahlevan and R. M. Kordestanizadeh,
A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization, Waste Management and Research, 37 (2019), 1089-1101.
doi: 10.1177/0734242X19865340. |
[27] |
E. B. Tirkolaee, S. Hadian and H. Golpra,
A novel multi-objective model for two-echelon green routing problem of perishable products with intermediate depots, Journal of Industrial Engineering and Management Studies, 6 (2019), 196-213.
|
[28] |
E. B. Tirkolaee, S. Hadian, G.-W. Weber and I. Mahdavi,
A robust green traffic-based routing problem for perishable products distribution, Computational Intelligence, 36 (2020), 80-101.
doi: 10.1111/coin.12240. |
[29] |
K. Wang, S. Lan and Y. Zhao,
A genetic-algorithm-based approach to the two-echelon capacitated vehicle routing problem with stochastic demands in logistics service, Journal of the Operational Research Society, 68 (2017), 1409-1421.
doi: 10.1057/s41274-016-0170-7. |
[30] |
X. M. Yan, Z. F. Hao, H. Huang, B Li and S. Jiang,
Assignment-preference ant colony optimization for the two-echelon vehicle routing problem, Indian Pulp and Paper Technical Association, 30 (2018), 484-494.
|
[31] |
T. T. Zhang and Z. F. Liu,
Fireworks algorithm for mean-VaR/CVaR models, Physica A: Statistical Mechanics and its Applications, 483 (2017), 1-8.
doi: 10.1016/j.physa.2017.04.036. |
show all references
References:
[1] |
R. Baldacci, A. Mingozzi, R. Roberti and R. W. Clavo,
An exact algorithm for the two-echelon capacitated vehicle routing problem, Operations Research, 61 (2013), 298-314.
doi: 10.1287/opre.1120.1153. |
[2] |
A. Bevilaqua, D. Bevilaqua and K. Yamanaka,
Parallel island based Memetic Algorithm with Lin-Kernighan local search for a real-life Two-Echelon Heterogeneous Vehicle Routing Problem based on Brazilian wholesale companies, Applied Soft Computing, 76 (2019), 697-711.
doi: 10.1016/j.asoc.2018.12.036. |
[3] |
U. Breunig, R. Baldacci, R. F. Hartl and T. Vidal,
The electric two-echelon vehicle routing problem, Computers and Operations Research, 103 (2019), 198-210.
doi: 10.1016/j.cor.2018.11.005. |
[4] |
U. Breunig, V. Schmid, R. F. Hartl and T. Vidal,
A large neighbourhood based heuristic for two-echelon routing problems, Computers and Operations Research, 76 (2016), 208-225.
doi: 10.1016/j.cor.2016.06.014. |
[5] |
M.-C. Chen, P.-J. W and Y.-H. Hsu,
An effective pricing model for the congestion alleviation of e-commerce logistics, Computers and Industrial Engineering, 129 (2019), 368-376.
doi: 10.1016/j.cie.2019.01.060. |
[6] |
Double 11 constantly refreshes the imagination of Chinese market, Global times, November 12, 2019 (015). |
[7] |
P. Grangier, M. Gendreau, F. Lehuédé and L.-M. Rousseau,
An adaptive large neighborhood search for the two-echelon multiple-trip vehicle routing problem with satellite synchronization, European Journal of Operational Research, 254 (2016), 80-91.
doi: 10.1016/j.ejor.2016.03.040. |
[8] |
M. Guan, M. Cha, Y. Li, Y. Wang and J. Yu, Predicting time-bounded purchases during a mega shopping festival, 2019 IEEE International Conference on Big Data and Smart Computing (BigComp), (2019), 1–8.
doi: 10.1109/BIGCOMP.2019.8679217. |
[9] |
X. Guo, Y. J. L. Jaramillo, J. Bloemhof-Ruwaard and G. D. H. Claassen, On integrating crowdsourced delivery in last-mile logistics: A simulation study to quantify its feasibility, Journal of Cleaner Production, 241 (2019), 118365.
doi: 10.1016/j.jclepro.2019.118365. |
[10] |
P. He and J. Li,
The two-echelon multi-trip vehicle routing problem with dynamic satellites for crop harvesting and transportation, Applied Soft Computing, 77 (2019), 387-398.
doi: 10.1016/j.asoc.2019.01.040. |
[11] |
W. Jie, J. Yang, M. Zhang and Y. Huang,
The two-echelon capacitated electric vehicle routing problem with battery swapping stations: Formulation and efficient methodology, European Journal of Operational Research, 272 (2019), 879-904.
doi: 10.1016/j.ejor.2018.07.002. |
[12] |
H. Li, L. Zhang, T. Lv and X. Chang,
The two-echelon time-constrained vehicle routing problem in linehaul-delivery systems, Transportation Research Part B: Methodological, 94 (2016), 169-188.
doi: 10.1016/j.trb.2016.09.012. |
[13] |
H. Li, H. Wang, J. Chen and M. Bai,
Two-echelon vehicle routing problem with time windows and mobile satellites, Transportation Research Part B: Methodological, 138 (2020), 179-201.
doi: 10.1016/j.trb.2020.05.010. |
[14] |
H. Li, Y. Liu, X. Jian and Y. Lu,
The two-echelon distribution system considering the real-time transshipment capacity varying, Transportation Research Part B: Methodological, 110 (2018), 239-260.
doi: 10.1016/j.trb.2018.02.015. |
[15] |
R. Liu, L. Tao, Q. Hu and X. Xie,
Simulation-based optimisation approach for the stochastic two-echelon logistics problem, International Journal of Production Research, 55 (2017), 187-201.
doi: 10.1080/00207543.2016.1201221. |
[16] |
T. Liu, Z. Luo, H. Qin and A. Lim,
A branch-and-cut algorithm for the two-echelon capacitated vehicle routing problem with grouping constraints, European Journal of Operational Research, 266 (2018), 487-497.
doi: 10.1016/j.ejor.2017.10.017. |
[17] |
Z. Y. Ma, Y. B. Ling and J. Li,
2E-VRP Optimization Algorithm with Optimal Cutting and Full Path Matching Cross, Computer Engineering, 41 (2015), 279-285.
|
[18] |
M. Marinelli, A. Colovic and M. Dell'Orco,
A novel Dynamic programming approach for Two-Echelon Capacitated Vehicle Routing Problem in City Logistics with Environmental considerations, Transportation Research Procedia, 30 (2018), 147-156.
doi: 10.1016/j.trpro.2018.09.017. |
[19] |
E. Morganti, L. Dablancg and F. Fortin,
Final deliveries for online shopping: The deployment of pickup point networks in urban and suburban areas, Research in Transportation Business and Management, 11 (2014), 23-31.
doi: 10.1016/j.rtbm.2014.03.002. |
[20] |
G. Perboli, R. Tadei and D. Vigo,
The two-echelon capacitated vehicle routing problem: Models and math-based heuristics, Transportation Science, 45 (2011), 364-380.
doi: 10.1287/trsc.1110.0368. |
[21] |
F. A. Santos, G. R. Mateus and A. S. D. Cunha,
A branch-and-cut-and-price algorithm for the two-echelon capacitated vehicle routing problem, Transportation Science, 49 (2015), 355-368.
doi: 10.1287/trsc.2013.0500. |
[22] |
M. Soysal, J. M. Bloemhof-Ruwaard and T. Bektas,
The time-dependent two-echelon capacitated vehicle routing problem with environmental considerations, International Journal of Production Economics, 164 (2015), 366-378.
doi: 10.1016/j.ijpe.2014.11.016. |
[23] |
E. Swilley and R. E. Goldsmith,
Black Friday and Cyber Monday: Understanding consumer intentions on two major shopping days, Journal of Retailing and Consumer Services, 20 (2013), 43-50.
doi: 10.1016/j.jretconser.2012.10.003. |
[24] |
Y. Tan and Y. Zhu, Fireworks algorithm for optimization, International Conference in Swarm Intelligence, Berlin: Springer, 355–364. |
[25] |
E. B. Tirkolaee, A. Goli, A. Faridnia, M. Soltani and G.-W. Weber, Multi-objective optimization for the reliable pollution-routing problem with cross-dock selection using Pareto-based algorithms, Journal of Cleaner Production, 276 (2020), 122927.
doi: 10.1016/j.jclepro.2020.122927. |
[26] |
E. B. Tirkolaee, A. Goli, M. Pahlevan and R. M. Kordestanizadeh,
A robust bi-objective multi-trip periodic capacitated arc routing problem for urban waste collection using a multi-objective invasive weed optimization, Waste Management and Research, 37 (2019), 1089-1101.
doi: 10.1177/0734242X19865340. |
[27] |
E. B. Tirkolaee, S. Hadian and H. Golpra,
A novel multi-objective model for two-echelon green routing problem of perishable products with intermediate depots, Journal of Industrial Engineering and Management Studies, 6 (2019), 196-213.
|
[28] |
E. B. Tirkolaee, S. Hadian, G.-W. Weber and I. Mahdavi,
A robust green traffic-based routing problem for perishable products distribution, Computational Intelligence, 36 (2020), 80-101.
doi: 10.1111/coin.12240. |
[29] |
K. Wang, S. Lan and Y. Zhao,
A genetic-algorithm-based approach to the two-echelon capacitated vehicle routing problem with stochastic demands in logistics service, Journal of the Operational Research Society, 68 (2017), 1409-1421.
doi: 10.1057/s41274-016-0170-7. |
[30] |
X. M. Yan, Z. F. Hao, H. Huang, B Li and S. Jiang,
Assignment-preference ant colony optimization for the two-echelon vehicle routing problem, Indian Pulp and Paper Technical Association, 30 (2018), 484-494.
|
[31] |
T. T. Zhang and Z. F. Liu,
Fireworks algorithm for mean-VaR/CVaR models, Physica A: Statistical Mechanics and its Applications, 483 (2017), 1-8.
doi: 10.1016/j.physa.2017.04.036. |








Sets and Parameters | Description |
D | Set of depots, |
S | Set of satellites, |
C | Set of customers, |
G | Set of first-level delivery vehicles, |
H | Set of second-level delivery vehicles, |
M | A large enough number |
T |
Working hours per day |
d |
The distance of the |
q |
Demand of customer c |
cap |
The capacity of the first-level vehicle |
cap |
The capacity of the second-level vehicle |
t |
The deadline of customer c |
b |
Compensation per unit cargo for accepting flexible delivery |
b |
Delay cost per delivery |
a |
Fixed cost of the first-level delivery vehicle per delivery |
a |
Fixed cost of the second-level delivery vehicle per delivery |
c |
Unit distance cost of the first-level delivery vehicle per delivery |
c |
Unit distance cost of the second-level delivery vehicle per delivery |
c |
The labor cost of the first-level delivery vehicle per delivery |
c |
The labor cost of the second -level delivery per delivery |
f |
If customer c chooses flexible delivery, fc =1; otherwise, fc =0 |
T |
Time required to complete standard delivery |
Sets and Parameters | Description |
D | Set of depots, |
S | Set of satellites, |
C | Set of customers, |
G | Set of first-level delivery vehicles, |
H | Set of second-level delivery vehicles, |
M | A large enough number |
T |
Working hours per day |
d |
The distance of the |
q |
Demand of customer c |
cap |
The capacity of the first-level vehicle |
cap |
The capacity of the second-level vehicle |
t |
The deadline of customer c |
b |
Compensation per unit cargo for accepting flexible delivery |
b |
Delay cost per delivery |
a |
Fixed cost of the first-level delivery vehicle per delivery |
a |
Fixed cost of the second-level delivery vehicle per delivery |
c |
Unit distance cost of the first-level delivery vehicle per delivery |
c |
Unit distance cost of the second-level delivery vehicle per delivery |
c |
The labor cost of the first-level delivery vehicle per delivery |
c |
The labor cost of the second -level delivery per delivery |
f |
If customer c chooses flexible delivery, fc =1; otherwise, fc =0 |
T |
Time required to complete standard delivery |
Variables | Description |
x |
First-level distribution vehicle g travels the |
y |
Second -level distribution vehicle h travels the |
z |
Customer c cargo comes from satellite s, z |
w |
The actual load of first level vehicle g to satellite s; decision variable |
l |
The total demand of satellite s |
t |
Time of first-level vehicle g arriving at satellite s |
t |
Time of arrival of second-level vehicle h to satellite s |
t |
The longest time for the first-level vehicle to complete the distribution task |
time |
The actual delivery time of the customer c |
dtime |
The delay time for customer c |
U1 |
Restrict the occurrence of sub-tour in the first-level vehicles |
U2 |
Restrict the occurrence of sub-tour in the second-level vehicles |
u |
Intermediate variable, no actual meaning |
u |
Intermediate variable, no actual meaning |
Variables | Description |
x |
First-level distribution vehicle g travels the |
y |
Second -level distribution vehicle h travels the |
z |
Customer c cargo comes from satellite s, z |
w |
The actual load of first level vehicle g to satellite s; decision variable |
l |
The total demand of satellite s |
t |
Time of first-level vehicle g arriving at satellite s |
t |
Time of arrival of second-level vehicle h to satellite s |
t |
The longest time for the first-level vehicle to complete the distribution task |
time |
The actual delivery time of the customer c |
dtime |
The delay time for customer c |
U1 |
Restrict the occurrence of sub-tour in the first-level vehicles |
U2 |
Restrict the occurrence of sub-tour in the second-level vehicles |
u |
Intermediate variable, no actual meaning |
u |
Intermediate variable, no actual meaning |
Algorithm | Parameter | Value |
HFWA | Fireworks population size | 5 |
The number of explosion sparks | 2 | |
Upper limit of the number of explosion sparks | 50 | |
Variation spark number | 2 | |
Number of iterations | 1000 | |
ACO | Number of ants | 50 |
Pheromone heuristic factor | 1 | |
Fitness heuristic factor | 9 | |
Pheromone volatile factor | 0.1 | |
Constant coefficient | 1 | |
Number of iterations | 1000 | |
GA | Population size | 50 |
Cross factor | 0.8 | |
Mutation factor | 0.2 | |
Number of iterations | 1000 |
Algorithm | Parameter | Value |
HFWA | Fireworks population size | 5 |
The number of explosion sparks | 2 | |
Upper limit of the number of explosion sparks | 50 | |
Variation spark number | 2 | |
Number of iterations | 1000 | |
ACO | Number of ants | 50 |
Pheromone heuristic factor | 1 | |
Fitness heuristic factor | 9 | |
Pheromone volatile factor | 0.1 | |
Constant coefficient | 1 | |
Number of iterations | 1000 | |
GA | Population size | 50 |
Cross factor | 0.8 | |
Mutation factor | 0.2 | |
Number of iterations | 1000 |
No | Standard test |
n | ACO | Optimal solution (km) |
|||
Optimal (km) | Average (km) | Time (s) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 422.93 | 422.93 | 50.7 | 1.40% | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 387.84 | 387.84 | 50.5 | 0.75% | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 479.05 | 484.18 | 49.1 | 1.80% | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 377.56 | 377.56 | 49.1 | 1.63% | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 753.75 | 768.28 | 74.9 | 3.23% | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 761.76 | 776.57 | 75 | 6.60% | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | 745.38 | 759.23 | 74.9 | 5.36% | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 790.55 | 804.92 | 75 | 4.45% | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | 797.87 | 802.72 | 75.7 | 2.42% | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 618.69 | 637.24 | 124.9 | 16.57% | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | 665.23 | 684.06 | 120.8 | 11.34% | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | 613.78 | 627.29 | 120.2 | 15.64% | 530.76 |
No | Standard test |
n | ACO | Optimal solution (km) |
|||
Optimal (km) | Average (km) | Time (s) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 422.93 | 422.93 | 50.7 | 1.40% | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 387.84 | 387.84 | 50.5 | 0.75% | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 479.05 | 484.18 | 49.1 | 1.80% | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 377.56 | 377.56 | 49.1 | 1.63% | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 753.75 | 768.28 | 74.9 | 3.23% | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 761.76 | 776.57 | 75 | 6.60% | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | 745.38 | 759.23 | 74.9 | 5.36% | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 790.55 | 804.92 | 75 | 4.45% | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | 797.87 | 802.72 | 75.7 | 2.42% | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 618.69 | 637.24 | 124.9 | 16.57% | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | 665.23 | 684.06 | 120.8 | 11.34% | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | 613.78 | 627.29 | 120.2 | 15.64% | 530.76 |
No. | Standard test |
n | GA | Optimal solution (km) |
|||
Optimal (km) | Average (km) | Time (s) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 417.07 | 438.04 | 472.4 | 0.00% | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 387.84 | 399.37 | 468.9 | 0.75 % | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 475.62 | 492.41 | 474.9 | 1.07 % | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 377.56 | 383.11 | 472.1 | 1.63 % | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 730.16 | 764.25 | 502.2 | 0.00 % | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 725.04 | 747.5 | 484.6 | 1.46 % | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | 732.37 | 760.82 | 488.5 | 3.52 % | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 763.58 | 790.26 | 491.8 | 0.89 % | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | 782.04 | 792.21 | 510.7 | 0.38 % | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 599.66 | 631.44 | 860.2 | 12.98 % | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | 641.66 | 671.12 | 695.5 | 7.39 % | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | 604.92 | 620.14 | 656.7 | 13.97 % | 530.76 |
No. | Standard test |
n | GA | Optimal solution (km) |
|||
Optimal (km) | Average (km) | Time (s) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 417.07 | 438.04 | 472.4 | 0.00% | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 387.84 | 399.37 | 468.9 | 0.75 % | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 475.62 | 492.41 | 474.9 | 1.07 % | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 377.56 | 383.11 | 472.1 | 1.63 % | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 730.16 | 764.25 | 502.2 | 0.00 % | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 725.04 | 747.5 | 484.6 | 1.46 % | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | 732.37 | 760.82 | 488.5 | 3.52 % | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 763.58 | 790.26 | 491.8 | 0.89 % | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | 782.04 | 792.21 | 510.7 | 0.38 % | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 599.66 | 631.44 | 860.2 | 12.98 % | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | 641.66 | 671.12 | 695.5 | 7.39 % | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | 604.92 | 620.14 | 656.7 | 13.97 % | 530.76 |
No. | Standard test |
n | HFWA | Optimal solution (km) |
|||
Optimal (km) | Average (km) | Time (s) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 417.07 | 417.07 | 103.7 | 0.00 % | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 384.96 | 386.69 | 106.2 | 0.00 % | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 470.6 | 472.84 | 107.1 | 0.00 % | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 371.5 | 376.35 | 104.2 | 0.00 % | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 730.16 | 734.76 | 188.9 | 0.00 % | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 714.63 | 724.6 | 191.1 | 0.00 % | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | 707.48 | 712.08 | 192.1 | 0.00 % | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 756.85 | 765.18 | 192.1 | 0.00 % | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | 779.05 | 781.95 | 194.7 | 0.00 % | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 530.76 | 557.82 | 593.6 | 0.00 % | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | 597.49 | 622.8 | 490 | 0.00 % | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | 530.76 | 549.47 | 499.2 | 0.00 % | 530.76 |
No. | Standard test |
n | HFWA | Optimal solution (km) |
|||
Optimal (km) | Average (km) | Time (s) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 417.07 | 417.07 | 103.7 | 0.00 % | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 384.96 | 386.69 | 106.2 | 0.00 % | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 470.6 | 472.84 | 107.1 | 0.00 % | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 371.5 | 376.35 | 104.2 | 0.00 % | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 730.16 | 734.76 | 188.9 | 0.00 % | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 714.63 | 724.6 | 191.1 | 0.00 % | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | 707.48 | 712.08 | 192.1 | 0.00 % | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 756.85 | 765.18 | 192.1 | 0.00 % | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | 779.05 | 781.95 | 194.7 | 0.00 % | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 530.76 | 557.82 | 593.6 | 0.00 % | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | 597.49 | 622.8 | 490 | 0.00 % | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | 530.76 | 549.47 | 499.2 | 0.00 % | 530.76 |
No. | Standard test |
n | [18] and [11] | Optimal solution (km) |
|||
Optimal (km) | GAP (%) | Optimal (km) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 417.07 | 0.00 % | 417.07 | 0.00 % | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 384.96 | 0.00 % | 384.96 | 0.00 % | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 470.6 | 0.00 % | 470.6 | 0.00 % | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 371.5 | 0.00 % | 371.5 | 0.00 % | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 743.22 | 1.79 % | 730.16 | 0.00 % | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 710.48 | -0.58 % | 714.63 | 0.00 % | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | - | - | 707.48 | 0.00 % | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 756.85 | 0.00 % | 756.85 | 0.00 % | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | - | - | 779.05 | 0.00 % | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 577.16 | 8.74 % | 530.76 | 0.00 % | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | - | - | 597.49 | 0.00 % | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | - | - | 530.76 | 0.00 % | 530.76 |
No. | Standard test |
n | [18] and [11] | Optimal solution (km) |
|||
Optimal (km) | GAP (%) | Optimal (km) | GAP (%) | ||||
1 | Set2a_E-n22-k4-s6-17 | 22 | 417.07 | 0.00 % | 417.07 | 0.00 % | 417.07 |
2 | Set2a_E-n22-k4-s8-14 | 22 | 384.96 | 0.00 % | 384.96 | 0.00 % | 384.96 |
3 | Set2a_E-n22-k4-s9-19 | 22 | 470.6 | 0.00 % | 470.6 | 0.00 % | 470.6 |
4 | Set2a_E-n22-k4-s10-14 | 22 | 371.5 | 0.00 % | 371.5 | 0.00 % | 371.5 |
5 | Set2a_E-n33-k4-s1-9 | 33 | 743.22 | 1.79 % | 730.16 | 0.00 % | 730.16 |
6 | Set2a_E-n33-k4-s2-13 | 33 | 710.48 | -0.58 % | 714.63 | 0.00 % | 714.63 |
7 | Set2a_E-n33-k4-s3-17 | 33 | - | - | 707.48 | 0.00 % | 707.48 |
8 | Set2a_E-n33-k4-s7-25 | 33 | 756.85 | 0.00 % | 756.85 | 0.00 % | 756.85 |
9 | Set2a_E-n33-k4-s14-22 | 33 | - | - | 779.05 | 0.00 % | 779.05 |
10 | Set2b_E-n51-k5-s2-4-17-46 | 51 | 577.16 | 8.74 % | 530.76 | 0.00 % | 530.76 |
11 | Set2b_E-n51-k5-s2-17 | 51 | - | - | 597.49 | 0.00 % | 597.49 |
12 | Set2b_E-n51-k5-s4-46 | 51 | - | - | 530.76 | 0.00 % | 530.76 |
Node | X | Y | Node | X | Y | Node | X | Y |
D | 20639 | 18019 | 16 | 14533 | 5098 | 34 | 13728 | 8485 |
S1 | 807 | 16768 | 17 | 13623 | 8242 | 35 | 12730 | 4622 |
S2 | 33084 | 19137 | 18 | 13573 | 3064 | 36 | 12968 | 4261 |
1 | 11066 | 5223 | 19 | 15874 | 7803 | 37 | 12611 | 3804 |
2 | 10481 | 7350 | 20 | 11212 | 6558 | 38 | 15604 | 5195 |
3 | 16356 | 5406 | 21 | 13396 | 3457 | 39 | 16340 | 4248 |
4 | 14197 | 6453 | 22 | 11813 | 9258 | 40 | 15342 | 6701 |
5 | 9591 | 5209 | 23 | 15606 | 5339 | 41 | 12902 | 3362 |
6 | 12664 | 5236 | 24 | 17577 | 5196 | 42 | 12149 | 5210 |
7 | 10772 | 5910 | 25 | 10496 | 5801 | 43 | 13221 | 5054 |
8 | 15321 | 5178 | 26 | 17472 | 3701 | 44 | 13451 | 7415 |
9 | 15239 | 5209 | 27 | 13839 | 7873 | 45 | 15543 | 7984 |
10 | 13556 | 7147 | 28 | 16555 | 8635 | 46 | 13025 | 4248 |
11 | 16660 | 4104 | 29 | 9347 | 6362 | 47 | 17460 | 4241 |
12 | 12438 | 3987 | 30 | 9547 | 8857 | 48 | 10895 | 4818 |
13 | 13850 | 6882 | 31 | 17942 | 3752 | 49 | 13704 | 10362 |
14 | 15196 | 8050 | 32 | 11042 | 5921 | 50 | 12929 | 4844 |
15 | 12864 | 4804 | 33 | 11387 | 5026 |
Node | X | Y | Node | X | Y | Node | X | Y |
D | 20639 | 18019 | 16 | 14533 | 5098 | 34 | 13728 | 8485 |
S1 | 807 | 16768 | 17 | 13623 | 8242 | 35 | 12730 | 4622 |
S2 | 33084 | 19137 | 18 | 13573 | 3064 | 36 | 12968 | 4261 |
1 | 11066 | 5223 | 19 | 15874 | 7803 | 37 | 12611 | 3804 |
2 | 10481 | 7350 | 20 | 11212 | 6558 | 38 | 15604 | 5195 |
3 | 16356 | 5406 | 21 | 13396 | 3457 | 39 | 16340 | 4248 |
4 | 14197 | 6453 | 22 | 11813 | 9258 | 40 | 15342 | 6701 |
5 | 9591 | 5209 | 23 | 15606 | 5339 | 41 | 12902 | 3362 |
6 | 12664 | 5236 | 24 | 17577 | 5196 | 42 | 12149 | 5210 |
7 | 10772 | 5910 | 25 | 10496 | 5801 | 43 | 13221 | 5054 |
8 | 15321 | 5178 | 26 | 17472 | 3701 | 44 | 13451 | 7415 |
9 | 15239 | 5209 | 27 | 13839 | 7873 | 45 | 15543 | 7984 |
10 | 13556 | 7147 | 28 | 16555 | 8635 | 46 | 13025 | 4248 |
11 | 16660 | 4104 | 29 | 9347 | 6362 | 47 | 17460 | 4241 |
12 | 12438 | 3987 | 30 | 9547 | 8857 | 48 | 10895 | 4818 |
13 | 13850 | 6882 | 31 | 17942 | 3752 | 49 | 13704 | 10362 |
14 | 15196 | 8050 | 32 | 11042 | 5921 | 50 | 12929 | 4844 |
15 | 12864 | 4804 | 33 | 11387 | 5026 |
Node | Demand (packages) | Time (days) | Node | Demand (packages) | Time (days) | Node | Demand (packages) | Time (days) |
1 | 91 | 3 | 18 | 46 | 3 | 35 | 302 | 6 |
2 | 224 | 3 | 19 | 49 | 3 | 36 | 94 | 6 |
3 | 215 | 3 | 20 | 85 | 3 | 37 | 249 | 6 |
4 | 53 | 6 | 21 | 277 | 6 | 38 | 248 | 3 |
5 | 39 | 6 | 22 | 84 | 6 | 39 | 125 | 3 |
6 | 164 | 6 | 23 | 268 | 6 | 40 | 130 | 3 |
7 | 316 | 6 | 24 | 80 | 3 | 41 | 25 | 3 |
8 | 112 | 3 | 25 | 306 | 3 | 42 | 75 | 6 |
9 | 192 | 3 | 26 | 115 | 3 | 43 | 175 | 6 |
10 | 74 | 3 | 27 | 65 | 3 | 44 | 256 | 6 |
11 | 247 | 6 | 28 | 83 | 6 | 45 | 307 | 6 |
12 | 84 | 6 | 29 | 203 | 6 | 46 | 42 | 3 |
13 | 166 | 6 | 30 | 156 | 6 | 47 | 186 | 3 |
14 | 230 | 3 | 31 | 116 | 6 | 48 | 100 | 6 |
15 | 293 | 3 | 32 | 273 | 3 | 49 | 57 | 6 |
16 | 316 | 6 | 33 | 192 | 3 | 50 | 110 | 6 |
17 | 180 | 6 | 34 | 181 | 3 |
Node | Demand (packages) | Time (days) | Node | Demand (packages) | Time (days) | Node | Demand (packages) | Time (days) |
1 | 91 | 3 | 18 | 46 | 3 | 35 | 302 | 6 |
2 | 224 | 3 | 19 | 49 | 3 | 36 | 94 | 6 |
3 | 215 | 3 | 20 | 85 | 3 | 37 | 249 | 6 |
4 | 53 | 6 | 21 | 277 | 6 | 38 | 248 | 3 |
5 | 39 | 6 | 22 | 84 | 6 | 39 | 125 | 3 |
6 | 164 | 6 | 23 | 268 | 6 | 40 | 130 | 3 |
7 | 316 | 6 | 24 | 80 | 3 | 41 | 25 | 3 |
8 | 112 | 3 | 25 | 306 | 3 | 42 | 75 | 6 |
9 | 192 | 3 | 26 | 115 | 3 | 43 | 175 | 6 |
10 | 74 | 3 | 27 | 65 | 3 | 44 | 256 | 6 |
11 | 247 | 6 | 28 | 83 | 6 | 45 | 307 | 6 |
12 | 84 | 6 | 29 | 203 | 6 | 46 | 42 | 3 |
13 | 166 | 6 | 30 | 156 | 6 | 47 | 186 | 3 |
14 | 230 | 3 | 31 | 116 | 6 | 48 | 100 | 6 |
15 | 293 | 3 | 32 | 273 | 3 | 49 | 57 | 6 |
16 | 316 | 6 | 33 | 192 | 3 | 50 | 110 | 6 |
17 | 180 | 6 | 34 | 181 | 3 |
Level | Vehicle NO. | Standard delivery vehicle route |
First-level vehicle delivery | 1S | D-S1-D |
2S | D-S1-D | |
3S | D-S1-D | |
4S | D-S1-D | |
Second-level vehicle delivery | 1 | S1-21-18-41-S1 |
2 | S1-37-12-S1 | |
3 | S1-35-6-S1 | |
4 | S1-42-33-48-1-S1 | |
5 | S1-32-20-S1 | |
6 | S1-25-5-S1 | |
7 | S1-29-2-S1 | |
8 | S1-30-22-49-34-S1 | |
9 | S1-17-27-S1 | |
10 | S1-44-10-13-S1 | |
11 | S1-4-40-14-S1 | |
12 | S1-45-19-28-S1 | |
13 | S1-24-47-31-26-S1 | |
14 | S1-11-39-S1 | |
15 | S1-3-23-S1 | |
16 | S1-38-8-S1 | |
17 | S1-7-S1 | |
18 | S1-9-S1 | |
19 | S1-16-43-S1 | |
20 | S1-50-15-36-S1 | |
21 | S1-46-S1 | |
Delivery time (days) | 7 | |
Delay cost (yuan) | 32240 | |
Compensation cost (yuan) | 0 | |
Total cost (yuan) | 2159128 |
Level | Vehicle NO. | Standard delivery vehicle route |
First-level vehicle delivery | 1S | D-S1-D |
2S | D-S1-D | |
3S | D-S1-D | |
4S | D-S1-D | |
Second-level vehicle delivery | 1 | S1-21-18-41-S1 |
2 | S1-37-12-S1 | |
3 | S1-35-6-S1 | |
4 | S1-42-33-48-1-S1 | |
5 | S1-32-20-S1 | |
6 | S1-25-5-S1 | |
7 | S1-29-2-S1 | |
8 | S1-30-22-49-34-S1 | |
9 | S1-17-27-S1 | |
10 | S1-44-10-13-S1 | |
11 | S1-4-40-14-S1 | |
12 | S1-45-19-28-S1 | |
13 | S1-24-47-31-26-S1 | |
14 | S1-11-39-S1 | |
15 | S1-3-23-S1 | |
16 | S1-38-8-S1 | |
17 | S1-7-S1 | |
18 | S1-9-S1 | |
19 | S1-16-43-S1 | |
20 | S1-50-15-36-S1 | |
21 | S1-46-S1 | |
Delivery time (days) | 7 | |
Delay cost (yuan) | 32240 | |
Compensation cost (yuan) | 0 | |
Total cost (yuan) | 2159128 |
Delivery method | Level | Vehicle NO. | TDD vehicle route |
Standard delivery | First-level vehicle delivery | 1S | D-S1-D |
2S | D-S1-D | ||
Second-level vehicle delivery | 1 | S1-26-47-24-8-9-S1 | |
2 | S1-38-19-20-S1 | ||
3 | S1-32-1-S1 | ||
4 | S1-2-25-S1 | ||
5 | S1-33-41-18-S1 | ||
6 | S1-46-15-S1 | ||
7 | S1-10-27-S1 | ||
8 | S1-14-34-S1 | ||
9 | S1-40-3-S1 | ||
10 | S1-39-S1 | ||
Flexible delivery | First-level vehicle delivery | 1S* | D-S1-D |
2S* | D-S1-D | ||
3S* | D-S1-D | ||
Second-level vehicle delivery | 11 | S1-23-13-S1 | |
12 | S1-17-44-S1 | ||
13 | S1-45-28-49-S1 | ||
14 | S1-22-31-11-S1 | ||
15 | S1-16-43-S1 | ||
16 | S1-6-37-12-S1 | ||
17 | S1-21-36-50-S1 | ||
18 | S1-42-35-48-S1 | ||
19 | S1-7-5-S1 | ||
20 | S1-29-30-S1 | ||
Delivery time (days) | 6 | ||
Delay cost (yuan) | 0 | ||
Compensation cost (yuan) | 2239.5 | ||
Total cost (yuan) | 1937381 |
Delivery method | Level | Vehicle NO. | TDD vehicle route |
Standard delivery | First-level vehicle delivery | 1S | D-S1-D |
2S | D-S1-D | ||
Second-level vehicle delivery | 1 | S1-26-47-24-8-9-S1 | |
2 | S1-38-19-20-S1 | ||
3 | S1-32-1-S1 | ||
4 | S1-2-25-S1 | ||
5 | S1-33-41-18-S1 | ||
6 | S1-46-15-S1 | ||
7 | S1-10-27-S1 | ||
8 | S1-14-34-S1 | ||
9 | S1-40-3-S1 | ||
10 | S1-39-S1 | ||
Flexible delivery | First-level vehicle delivery | 1S* | D-S1-D |
2S* | D-S1-D | ||
3S* | D-S1-D | ||
Second-level vehicle delivery | 11 | S1-23-13-S1 | |
12 | S1-17-44-S1 | ||
13 | S1-45-28-49-S1 | ||
14 | S1-22-31-11-S1 | ||
15 | S1-16-43-S1 | ||
16 | S1-6-37-12-S1 | ||
17 | S1-21-36-50-S1 | ||
18 | S1-42-35-48-S1 | ||
19 | S1-7-5-S1 | ||
20 | S1-29-30-S1 | ||
Delivery time (days) | 6 | ||
Delay cost (yuan) | 0 | ||
Compensation cost (yuan) | 2239.5 | ||
Total cost (yuan) | 1937381 |
Standard | Penalty | Standard | TDD second- | Penalty | Compensation | Total cost |
delivery | (yuan) | delivery | level arrival | (yuan) | cost | of delivery |
time (day) | cost (yuan) | time (day) | (yuan) | (yuan) | ||
2 | 40300 | 1881455 | 4 | 6250 | 2239.5 | 1783100 |
5 | 3750 | 2239.5 | 1781518 | |||
6 | 1250 | 2239.5 | 1779718 | |||
3 | 32240 | 1868263 | 4 | 5000 | 2239.5 | 1782724 |
5 | 2500 | 2239.5 | 1779725 | |||
6 | 0 | 2239.5 | 1775722 | |||
4 | 24180 | 1862604 | 5 | 2500 | 2239.5 | 1779704 |
6 | 0 | 2239.5 | 1777541 | |||
7 | 0 | 2239.5 | 1777541 |
Standard | Penalty | Standard | TDD second- | Penalty | Compensation | Total cost |
delivery | (yuan) | delivery | level arrival | (yuan) | cost | of delivery |
time (day) | cost (yuan) | time (day) | (yuan) | (yuan) | ||
2 | 40300 | 1881455 | 4 | 6250 | 2239.5 | 1783100 |
5 | 3750 | 2239.5 | 1781518 | |||
6 | 1250 | 2239.5 | 1779718 | |||
3 | 32240 | 1868263 | 4 | 5000 | 2239.5 | 1782724 |
5 | 2500 | 2239.5 | 1779725 | |||
6 | 0 | 2239.5 | 1775722 | |||
4 | 24180 | 1862604 | 5 | 2500 | 2239.5 | 1779704 |
6 | 0 | 2239.5 | 1777541 | |||
7 | 0 | 2239.5 | 1777541 |
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