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doi: 10.3934/jimo.2021151
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Lagrangian relaxation algorithm for the truck scheduling problem with products time window constraint in multi-door cross-dock

Institute of Industrial Engineering, School of Mechanical Engineering, Tongji University, Shanghai 201804, China

* Corresponding author: Binghai Zhou

Received  December 2020 Revised  June 2021 Early access September 2021

Cross-docking is a kind of process that products are unloaded in front of the inbound doors, consolidated based on the downstream demand, and then directly transferred to the outbound doors without a long storage process during the transportation. In this paper, a multi-door cross-dock truck scheduling problem is investigated in which the scheduling and sequencing assignment of trucks need to be considered, with the objectives of minimizing the inner transportation cost in the cross-dock and the total truck waiting cost. The major contribution of this paper is that a novel product-related time window constraint and the temporary storage area are firstly introduced to adapt to different physical conditions of goods considering real-world requirements. Then, a Lagrangian relaxation algorithm is proposed which aims to decompose the relaxed problem into several easy-to-be-solved sub-problems. Besides, a subgradient algorithm is used at each iteration to further deal with these sub-problems. Finally, theory analysis and simulation experiments of different problem scales are carried out during the comparison with a Greedy algorithm to evaluate the performance of the proposed algorithm. Results indicate that the Lagrangian relaxation algorithm is able to achieve more satisfactory near-optimal solutions within an acceptable time.

Citation: Binghai Zhou, Yuanrui Lei, Shi Zong. Lagrangian relaxation algorithm for the truck scheduling problem with products time window constraint in multi-door cross-dock. Journal of Industrial & Management Optimization, doi: 10.3934/jimo.2021151
References:
[1]

A. Amini and R. Tavakkoli-Moghaddam, A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks, Computers & Industrial Engineering, 96 (2016), 180-191.  doi: 10.1016/j.cie.2016.03.023.  Google Scholar

[2]

H. ArbabiM. M. Nasiri and A. Bozorgi-Amiri, A hub-and-spoke architecture for a parcel delivery system using the cross-docking distribution strategy, Eng. Optim., 53 (2021), 1593-1612.  doi: 10.1080/0305215X.2020.1808973.  Google Scholar

[3]

M. T. Assadi and M. Bagheri, Differential evolution and Population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems, Computers & Industrial Engineering, 96 (2016), 149-161.  doi: 10.1016/j.cie.2016.03.021.  Google Scholar

[4]

P. BodnarR. de Koster and K. Azadeh, Scheduling trucks in a cross-dock with mixed service mode dock doors, Transportation Science, 51 (2017), 112-131.  doi: 10.1287/trsc.2015.0612.  Google Scholar

[5]

N. Boysen and M. Fliedner, Cross dock scheduling: Classification, literature review and research agenda, Omega, 38 (2010), 413-422.  doi: 10.1016/j.omega.2009.10.008.  Google Scholar

[6]

A. ChiarelloM. Gaudioso and M. Sammarra, Truck synchronization at single door cross-docking terminals, OR Spectrum, 40 (2018), 395-447.  doi: 10.1007/s00291-018-0510-x.  Google Scholar

[7]

H. ChenC. Chu and J. M. Proth, An improvement of the Lagrangean relaxation approach for job shop scheduling: A dynamic programming method, IEEE Transactions on Robotics and Atomation, 14 (1998), 786-795.  doi: 10.1109/70.720354.  Google Scholar

[8]

J. Chen, K. Wang and Y. Huang, An integrated inbound logistics mode with intelligent scheduling of milk-run collection, drop and pull delivery and LNG vehicles, Journal of Intelligent Manufacturing, (2020), 1–9. doi: 10.1007/s10845-020-01637-3.  Google Scholar

[9]

L. ChenY. Liu and A. Langevin, A multi-compartment vehicle routing problem in cold-chain distribution, Comput. Oper. Res., 111 (2019), 58-66.  doi: 10.1016/j.cor.2019.06.001.  Google Scholar

[10]

U. ClausenD. DiekmannM. Pöting and C. Schumacher, Operating parcel transshipment terminals: A combined simulation and optimization approach, Journal of Simulation, 11 (2017), 2-10.  doi: 10.1057/s41273-016-0032-y.  Google Scholar

[11]

H. CorstenF. Becker and H. Salewski, Integrating truck and workforce scheduling in a cross-dock: Analysis of different workforce coordination policies, Journal of Business Economics, 90 (2020), 207-237.   Google Scholar

[12]

C. DongQ. LiB. Shen and X. Tong, Sustainability in supply chains with behavioral concerns, Sustainability, 11 (2019), 4051.  doi: 10.3390/su11154051.  Google Scholar

[13]

G. B. FonsecaT. H. Nogueira and M. G. Ravetti, A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem, European J. Oper. Res., 275 (2019), 139-154.  doi: 10.1016/j.ejor.2018.11.033.  Google Scholar

[14]

T. Garai and H. Garg, Multi-objective linear fractional inventory model with possibility and necessity constraints under generalised intuitionistic fuzzy set environment, CAAI Transactions on Intelligence Technology, 4 (2019), 175-181.   Google Scholar

[15]

M. GaudiosoM. F. Monaco and M. Sammarra, A Lagrangian heuristics for the truck scheduling problem in multi-door, multi-product Cross-Docking with constant processing time, Omega, 101 (2021), 102255.  doi: 10.1016/j.omega.2020.102255.  Google Scholar

[16]

A. Golshahi-RoudbanehM. Hajiaghaei-Keshteli and M. M. Paydar, Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center, Knowledge-Based Systems, 129 (2017), 17-38.  doi: 10.1016/j.knosys.2017.05.006.  Google Scholar

[17]

O. Guemri, P. Nduwayo, R. Todosijević, S. Hanafi and F. Glover, Probabilistic tabu search for the cross-docking assignment problem, European J. Oper. Res., 277 (2019), 875–885., doi: 10.1016/j.ejor.2019.03.030.  Google Scholar

[18]

A. Gunawan, A. T. Widjaja, P. Vansteenwegen and V. F. Yu, A matheuristic algorithm for solving the vehicle routing problem with cross-docking, International Conference on Learning and Intelligent Optimization Springer, (2020), 9–15. doi: 10.1007/978-3-030-53552-0_2.  Google Scholar

[19]

A. GutierrezL. DieulleN. Labadie and N. Velasco, A hybrid metaheuristic algorithm for the vehicle routing problem with stochastic demands, Comput. Oper. Res., 99 (2018), 135-147.  doi: 10.1016/j.cor.2018.06.012.  Google Scholar

[20]

A. L. Ladier and G. Alpan, Robust cross-dock scheduling with time windows, Computers & Industrial Engineering, 99 (2016), 16-28.  doi: 10.1016/j.cie.2016.07.003.  Google Scholar

[21]

A. L. Ladier and G. Alpan, Crossdock truck scheduling with time windows: Earliness, tardiness and storage policies, J. Intell. Manufacturing, 29 (2018), 569-583.   Google Scholar

[22]

S. M. Mousavi and B. Vahdani, Cross-docking location selection in distribution systems: A new intuitionistic fuzzy hierarchical decision model, International J. Computational Intelligence Systems, 9 (2016), 91-109.  doi: 10.1080/18756891.2016.1144156.  Google Scholar

[23]

W. NassiefI. Contreras and R. As'ad, A mixed-integer programming formulation and Lagrangean relaxation for the cross-dock door assignment problem, International Journal of Production Research, 54 (2016), 494-508.  doi: 10.1080/00207543.2014.1003664.  Google Scholar

[24]

W. Nassief, I. Contreras and B. Jaumard, A comparison of formulations and relaxations for cross-dock door assignment problems, Comput. Oper. Res., 94 (2018), 76–88., doi: 10.1016/j.cor.2018.01.022.  Google Scholar

[25]

S. NiroomandH. Garg and A. Mahmoodirad, An intuitionistic fuzzy two stage supply chain network design problem with multi-mode demand and multi-mode transportation, ISA transactions, 107 (2020), 117-133.  doi: 10.1016/j.isatra.2020.07.033.  Google Scholar

[26]

C. SerranoX. Delorme and A. Dolgui, Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans, International Journal of Production Economics, 194 (2017), 102-112.  doi: 10.1016/j.ijpe.2017.09.008.  Google Scholar

[27]

I. SeyediM. Hamedi and R. Tavakkoli-Moghaddam, Truck Scheduling in a Cross-Docking Terminal by Using Novel Robust Heuristics, International Journal of Engineering, 32 (2019), 296-305.   Google Scholar

[28]

M. ShakeriM. Y. H. LowS. J. Turner and E. W. Lee, An efficient incremental evaluation function for optimizing truck scheduling in a resource-constrained crossdock using metaheuristics,, Expert Systems with Applications, 45 (2016), 172-184.  doi: 10.1016/j.eswa.2015.09.041.  Google Scholar

[29]

J. G. Urzúa-MoralesJ. P. Sepulveda-RojasM. AlfaroG. FuertesR. Ternero and M. Vargas, Logistic modeling of the last mile: Case study santiago, chile,, Sustainability, 12 (2020), 648.  doi: 10.3390/su12020648.  Google Scholar

[30]

B. Vahdani and S. Shahramfard, A truck scheduling problem at a cross-docking facility with mixed service mode dock doors, Engineering Computations, 12 (2019), 648.   Google Scholar

[31]

R. H. WalivU. MishraH. Garg and H. P. Umap, A nonlinear programming approach to solve the stochastic multi-objective inventory model using the uncertain information, Arabian Journal for Science and Engineering, 45 (2020), 6963-6973.  doi: 10.1007/s13369-020-04618-z.  Google Scholar

[32]

Z. Yong-wu, A Quantity discount model for two-echelon supply chain coordination under stochastic demand, Systems Engineering-Theory & Practice, 7 (2006), 25-32.   Google Scholar

[33]

B. H. Zhou and W. L. Liu, A modified column generation heuristic for hybrid flow shop multiple orders per job scheduling problem, International Journal of Manufacturing Technology and Management, 33 (2019), 88-113.  doi: 10.1504/IJMTM.2019.100166.  Google Scholar

[34]

B. H. ZhouM. Yin and Z. Q. Lu, An improved Lagrangian relaxation heuristic for the scheduling problem of operating theatres, Computers & Industrial Engineering, 101 (2016), 490-503.  doi: 10.1016/j.cie.2016.09.003.  Google Scholar

[35]

W. Zhou and Y. H. JIN, Fuzzy subgradient algorithm for solving Lagrangian relaxation dual problem, Control Decis., 11 (2004), 1213-1217.   Google Scholar

[36]

L. ZhuX. RenC. Lee and Y. Zhang, Coordination contracts in a dual-channel supply chain with a risk-averse retailer, Sustainability, 9 (2017), 2148.  doi: 10.3390/su9112148.  Google Scholar

show all references

References:
[1]

A. Amini and R. Tavakkoli-Moghaddam, A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks, Computers & Industrial Engineering, 96 (2016), 180-191.  doi: 10.1016/j.cie.2016.03.023.  Google Scholar

[2]

H. ArbabiM. M. Nasiri and A. Bozorgi-Amiri, A hub-and-spoke architecture for a parcel delivery system using the cross-docking distribution strategy, Eng. Optim., 53 (2021), 1593-1612.  doi: 10.1080/0305215X.2020.1808973.  Google Scholar

[3]

M. T. Assadi and M. Bagheri, Differential evolution and Population-based simulated annealing for truck scheduling problem in multiple door cross-docking systems, Computers & Industrial Engineering, 96 (2016), 149-161.  doi: 10.1016/j.cie.2016.03.021.  Google Scholar

[4]

P. BodnarR. de Koster and K. Azadeh, Scheduling trucks in a cross-dock with mixed service mode dock doors, Transportation Science, 51 (2017), 112-131.  doi: 10.1287/trsc.2015.0612.  Google Scholar

[5]

N. Boysen and M. Fliedner, Cross dock scheduling: Classification, literature review and research agenda, Omega, 38 (2010), 413-422.  doi: 10.1016/j.omega.2009.10.008.  Google Scholar

[6]

A. ChiarelloM. Gaudioso and M. Sammarra, Truck synchronization at single door cross-docking terminals, OR Spectrum, 40 (2018), 395-447.  doi: 10.1007/s00291-018-0510-x.  Google Scholar

[7]

H. ChenC. Chu and J. M. Proth, An improvement of the Lagrangean relaxation approach for job shop scheduling: A dynamic programming method, IEEE Transactions on Robotics and Atomation, 14 (1998), 786-795.  doi: 10.1109/70.720354.  Google Scholar

[8]

J. Chen, K. Wang and Y. Huang, An integrated inbound logistics mode with intelligent scheduling of milk-run collection, drop and pull delivery and LNG vehicles, Journal of Intelligent Manufacturing, (2020), 1–9. doi: 10.1007/s10845-020-01637-3.  Google Scholar

[9]

L. ChenY. Liu and A. Langevin, A multi-compartment vehicle routing problem in cold-chain distribution, Comput. Oper. Res., 111 (2019), 58-66.  doi: 10.1016/j.cor.2019.06.001.  Google Scholar

[10]

U. ClausenD. DiekmannM. Pöting and C. Schumacher, Operating parcel transshipment terminals: A combined simulation and optimization approach, Journal of Simulation, 11 (2017), 2-10.  doi: 10.1057/s41273-016-0032-y.  Google Scholar

[11]

H. CorstenF. Becker and H. Salewski, Integrating truck and workforce scheduling in a cross-dock: Analysis of different workforce coordination policies, Journal of Business Economics, 90 (2020), 207-237.   Google Scholar

[12]

C. DongQ. LiB. Shen and X. Tong, Sustainability in supply chains with behavioral concerns, Sustainability, 11 (2019), 4051.  doi: 10.3390/su11154051.  Google Scholar

[13]

G. B. FonsecaT. H. Nogueira and M. G. Ravetti, A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem, European J. Oper. Res., 275 (2019), 139-154.  doi: 10.1016/j.ejor.2018.11.033.  Google Scholar

[14]

T. Garai and H. Garg, Multi-objective linear fractional inventory model with possibility and necessity constraints under generalised intuitionistic fuzzy set environment, CAAI Transactions on Intelligence Technology, 4 (2019), 175-181.   Google Scholar

[15]

M. GaudiosoM. F. Monaco and M. Sammarra, A Lagrangian heuristics for the truck scheduling problem in multi-door, multi-product Cross-Docking with constant processing time, Omega, 101 (2021), 102255.  doi: 10.1016/j.omega.2020.102255.  Google Scholar

[16]

A. Golshahi-RoudbanehM. Hajiaghaei-Keshteli and M. M. Paydar, Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center, Knowledge-Based Systems, 129 (2017), 17-38.  doi: 10.1016/j.knosys.2017.05.006.  Google Scholar

[17]

O. Guemri, P. Nduwayo, R. Todosijević, S. Hanafi and F. Glover, Probabilistic tabu search for the cross-docking assignment problem, European J. Oper. Res., 277 (2019), 875–885., doi: 10.1016/j.ejor.2019.03.030.  Google Scholar

[18]

A. Gunawan, A. T. Widjaja, P. Vansteenwegen and V. F. Yu, A matheuristic algorithm for solving the vehicle routing problem with cross-docking, International Conference on Learning and Intelligent Optimization Springer, (2020), 9–15. doi: 10.1007/978-3-030-53552-0_2.  Google Scholar

[19]

A. GutierrezL. DieulleN. Labadie and N. Velasco, A hybrid metaheuristic algorithm for the vehicle routing problem with stochastic demands, Comput. Oper. Res., 99 (2018), 135-147.  doi: 10.1016/j.cor.2018.06.012.  Google Scholar

[20]

A. L. Ladier and G. Alpan, Robust cross-dock scheduling with time windows, Computers & Industrial Engineering, 99 (2016), 16-28.  doi: 10.1016/j.cie.2016.07.003.  Google Scholar

[21]

A. L. Ladier and G. Alpan, Crossdock truck scheduling with time windows: Earliness, tardiness and storage policies, J. Intell. Manufacturing, 29 (2018), 569-583.   Google Scholar

[22]

S. M. Mousavi and B. Vahdani, Cross-docking location selection in distribution systems: A new intuitionistic fuzzy hierarchical decision model, International J. Computational Intelligence Systems, 9 (2016), 91-109.  doi: 10.1080/18756891.2016.1144156.  Google Scholar

[23]

W. NassiefI. Contreras and R. As'ad, A mixed-integer programming formulation and Lagrangean relaxation for the cross-dock door assignment problem, International Journal of Production Research, 54 (2016), 494-508.  doi: 10.1080/00207543.2014.1003664.  Google Scholar

[24]

W. Nassief, I. Contreras and B. Jaumard, A comparison of formulations and relaxations for cross-dock door assignment problems, Comput. Oper. Res., 94 (2018), 76–88., doi: 10.1016/j.cor.2018.01.022.  Google Scholar

[25]

S. NiroomandH. Garg and A. Mahmoodirad, An intuitionistic fuzzy two stage supply chain network design problem with multi-mode demand and multi-mode transportation, ISA transactions, 107 (2020), 117-133.  doi: 10.1016/j.isatra.2020.07.033.  Google Scholar

[26]

C. SerranoX. Delorme and A. Dolgui, Scheduling of truck arrivals, truck departures and shop-floor operation in a cross-dock platform, based on trucks loading plans, International Journal of Production Economics, 194 (2017), 102-112.  doi: 10.1016/j.ijpe.2017.09.008.  Google Scholar

[27]

I. SeyediM. Hamedi and R. Tavakkoli-Moghaddam, Truck Scheduling in a Cross-Docking Terminal by Using Novel Robust Heuristics, International Journal of Engineering, 32 (2019), 296-305.   Google Scholar

[28]

M. ShakeriM. Y. H. LowS. J. Turner and E. W. Lee, An efficient incremental evaluation function for optimizing truck scheduling in a resource-constrained crossdock using metaheuristics,, Expert Systems with Applications, 45 (2016), 172-184.  doi: 10.1016/j.eswa.2015.09.041.  Google Scholar

[29]

J. G. Urzúa-MoralesJ. P. Sepulveda-RojasM. AlfaroG. FuertesR. Ternero and M. Vargas, Logistic modeling of the last mile: Case study santiago, chile,, Sustainability, 12 (2020), 648.  doi: 10.3390/su12020648.  Google Scholar

[30]

B. Vahdani and S. Shahramfard, A truck scheduling problem at a cross-docking facility with mixed service mode dock doors, Engineering Computations, 12 (2019), 648.   Google Scholar

[31]

R. H. WalivU. MishraH. Garg and H. P. Umap, A nonlinear programming approach to solve the stochastic multi-objective inventory model using the uncertain information, Arabian Journal for Science and Engineering, 45 (2020), 6963-6973.  doi: 10.1007/s13369-020-04618-z.  Google Scholar

[32]

Z. Yong-wu, A Quantity discount model for two-echelon supply chain coordination under stochastic demand, Systems Engineering-Theory & Practice, 7 (2006), 25-32.   Google Scholar

[33]

B. H. Zhou and W. L. Liu, A modified column generation heuristic for hybrid flow shop multiple orders per job scheduling problem, International Journal of Manufacturing Technology and Management, 33 (2019), 88-113.  doi: 10.1504/IJMTM.2019.100166.  Google Scholar

[34]

B. H. ZhouM. Yin and Z. Q. Lu, An improved Lagrangian relaxation heuristic for the scheduling problem of operating theatres, Computers & Industrial Engineering, 101 (2016), 490-503.  doi: 10.1016/j.cie.2016.09.003.  Google Scholar

[35]

W. Zhou and Y. H. JIN, Fuzzy subgradient algorithm for solving Lagrangian relaxation dual problem, Control Decis., 11 (2004), 1213-1217.   Google Scholar

[36]

L. ZhuX. RenC. Lee and Y. Zhang, Coordination contracts in a dual-channel supply chain with a risk-averse retailer, Sustainability, 9 (2017), 2148.  doi: 10.3390/su9112148.  Google Scholar

Figure 1.  The scheduling process at cross-dock
Figure 2.  Several values of %gap (group2) based on the different value of slackness
Figure 3.  Values of %gap and %dev in group 2
Table 1.  Computer results for problems in group 1, b = 0.98, 0.95, and 0.90
p=0.98 p=0.95 p=0.90
L G L G L G
I #t×#d s IP LR %gap CPU UB %dev IP LR %gap CPU UB %dev IP LR %gap CPU UB %dev
1 15 1.683 1.683 5.32 0.55 1.815 10.43 1.98 1.98 4.97 0.56 2.187 10.43 1.643 1.643 5.27 0.46 1.99 11.99
2 20 1.701 1.701 4.82 0.5 1.89 8.37 2.1 2.1 4.38 0.33 2.278 8.45 1.785 1.785 5.39 0.42 1.754 8.03
3 4×2 30 1.729 1.729 3.44 0.53 2.004 15.27 2.059 2.059 3.55 0.42 2.358 14.54 1.997 1.997 3.59 0.51 1.769 14.69
4 15 2.56 2.56 9.55 0.66 2.45 14.81 2.56 2.56 9.27 0.68 2.816 14.52 2.765 2.765 9.83 0.84 3.013 14.96
5 20 2.26 2.26 8.15 0.8 3.064 13.02 2.659 2.659 7.03 0.8 3.225 12.76 2.872 2.872 6.89 0.8 2.838 13.4
6 5×3 30 2.498 2.498 10.55 0.65 2.648 12.76 2.602 2.602 9.02 0.8 3.152 12.76 2.862 2.862 11 0.96 3.089 12.89
7 15 2.709 2.709 6.58 1.59 3.954 26.35 3.078 3.078 6.33 1.61 4.163 26.62 2.924 2.924 6.46 1.07 3.83 27.95
8 20 3.108 3.108 4.32 1.64 3.533 25 3.205 3.205 4.32 1.06 4.257 24.27 2.66 2.66 4.62 1.33 4.087 23.06
9 6×3 30 2.495 2.495 7.55 1.6 3.735 26.32 3.08 3.08 6.29 1.02 4.016 26.06 2.957 2.957 6.54 1.27 4.257 27.1
10 15 3.31 3.31 4.76 1.92 4.153 13.37 3.755 3.988 4.21 2.35 4.72 13.37 4.068 4.068 4.04 1.88 4.153 12.84
11 20 3.425 3.624 6.35 1.93 4.337 14.81 3.89 3.897 4.96 1.91 4.518 15.11 4.155 4.17 6.05 2.38 3.433 15.87
12 7×3 30 3.257 3.471 3.69 3.13 3.747 21.32 3.572 3.732 3.55 2.66 4.625 21.98 3.375 3.471 3.41 3.19 4.163 22.2
13 15 3.18 3.381 7.79 5.86 4.848 29.61 3.58 3.636 6.04 7.1 5.051 30.53 3.09 3.09 7.61 7.1 3.788 30.84
14 20 3.155 3.301 6.23 4.89 4.847 29.92 3.42 3.628 6.23 7.41 5.049 30.53 3.171 3.301 6.17 4.94 3.786 29
15 7×4 30 3.178 3.204 6.83 5.38 4.262 32.8 3.595 3.641 5.38 8.41 5.014 31.24 3.285 3.386 6.62 8.41 4.061 32.49
16 15 3.65 3.826 5.76 4.4 5.089 24.08 4.058 4.159 5.7 4.27 5.472 24.08 3.588 3.701 5.81 5.34 5.472 25.28
17 20 3.425 3.425 5.58 5.79 5.329 21.76 4.117 4.228 5.03 4.45 5.438 22.91 4.489 4.608 5.53 5.57 4.894 23.14
18 8×3 30 3.785 3.85 6.41 8.54 4.745 22.79 4.095 4.184 5.57 10.05 5.214 23.49 4.258 4.435 7.19 8.37 4.432 24.19
19 15 3.455 3.485 4.57 8.09 5.375 35.21 3.986 4.1 4.76 7.03 5.973 36.3 3.698 3.772 4.57 7.03 5.973 35.94
20 20 3.576 3.678 5.81 6.36 4.802 34.09 4.168 4.18 4.76 7.64 5.856 33.42 3.259 3.344 5.09 9.17 6.09 33.09
21 8×4 30 3.385 3.428 6.14 9.16 5.446 32.42 4.15 4.18 4.76 11.33 5.856 33.42 3.857 3.929 4.52 11.33 5.739 33.09
Instance is short for $i$, $t$ for trucks, $d$ for doors, $s$ for the slackness of temporary storage capacity (%), $L$ for the Lagrangian relaxation algorithm, $G$ for the Greedy algorithm. The value of $IP, LP$ and $UB$ is in $10^5$.
p=0.98 p=0.95 p=0.90
L G L G L G
I #t×#d s IP LR %gap CPU UB %dev IP LR %gap CPU UB %dev IP LR %gap CPU UB %dev
1 15 1.683 1.683 5.32 0.55 1.815 10.43 1.98 1.98 4.97 0.56 2.187 10.43 1.643 1.643 5.27 0.46 1.99 11.99
2 20 1.701 1.701 4.82 0.5 1.89 8.37 2.1 2.1 4.38 0.33 2.278 8.45 1.785 1.785 5.39 0.42 1.754 8.03
3 4×2 30 1.729 1.729 3.44 0.53 2.004 15.27 2.059 2.059 3.55 0.42 2.358 14.54 1.997 1.997 3.59 0.51 1.769 14.69
4 15 2.56 2.56 9.55 0.66 2.45 14.81 2.56 2.56 9.27 0.68 2.816 14.52 2.765 2.765 9.83 0.84 3.013 14.96
5 20 2.26 2.26 8.15 0.8 3.064 13.02 2.659 2.659 7.03 0.8 3.225 12.76 2.872 2.872 6.89 0.8 2.838 13.4
6 5×3 30 2.498 2.498 10.55 0.65 2.648 12.76 2.602 2.602 9.02 0.8 3.152 12.76 2.862 2.862 11 0.96 3.089 12.89
7 15 2.709 2.709 6.58 1.59 3.954 26.35 3.078 3.078 6.33 1.61 4.163 26.62 2.924 2.924 6.46 1.07 3.83 27.95
8 20 3.108 3.108 4.32 1.64 3.533 25 3.205 3.205 4.32 1.06 4.257 24.27 2.66 2.66 4.62 1.33 4.087 23.06
9 6×3 30 2.495 2.495 7.55 1.6 3.735 26.32 3.08 3.08 6.29 1.02 4.016 26.06 2.957 2.957 6.54 1.27 4.257 27.1
10 15 3.31 3.31 4.76 1.92 4.153 13.37 3.755 3.988 4.21 2.35 4.72 13.37 4.068 4.068 4.04 1.88 4.153 12.84
11 20 3.425 3.624 6.35 1.93 4.337 14.81 3.89 3.897 4.96 1.91 4.518 15.11 4.155 4.17 6.05 2.38 3.433 15.87
12 7×3 30 3.257 3.471 3.69 3.13 3.747 21.32 3.572 3.732 3.55 2.66 4.625 21.98 3.375 3.471 3.41 3.19 4.163 22.2
13 15 3.18 3.381 7.79 5.86 4.848 29.61 3.58 3.636 6.04 7.1 5.051 30.53 3.09 3.09 7.61 7.1 3.788 30.84
14 20 3.155 3.301 6.23 4.89 4.847 29.92 3.42 3.628 6.23 7.41 5.049 30.53 3.171 3.301 6.17 4.94 3.786 29
15 7×4 30 3.178 3.204 6.83 5.38 4.262 32.8 3.595 3.641 5.38 8.41 5.014 31.24 3.285 3.386 6.62 8.41 4.061 32.49
16 15 3.65 3.826 5.76 4.4 5.089 24.08 4.058 4.159 5.7 4.27 5.472 24.08 3.588 3.701 5.81 5.34 5.472 25.28
17 20 3.425 3.425 5.58 5.79 5.329 21.76 4.117 4.228 5.03 4.45 5.438 22.91 4.489 4.608 5.53 5.57 4.894 23.14
18 8×3 30 3.785 3.85 6.41 8.54 4.745 22.79 4.095 4.184 5.57 10.05 5.214 23.49 4.258 4.435 7.19 8.37 4.432 24.19
19 15 3.455 3.485 4.57 8.09 5.375 35.21 3.986 4.1 4.76 7.03 5.973 36.3 3.698 3.772 4.57 7.03 5.973 35.94
20 20 3.576 3.678 5.81 6.36 4.802 34.09 4.168 4.18 4.76 7.64 5.856 33.42 3.259 3.344 5.09 9.17 6.09 33.09
21 8×4 30 3.385 3.428 6.14 9.16 5.446 32.42 4.15 4.18 4.76 11.33 5.856 33.42 3.857 3.929 4.52 11.33 5.739 33.09
Instance is short for $i$, $t$ for trucks, $d$ for doors, $s$ for the slackness of temporary storage capacity (%), $L$ for the Lagrangian relaxation algorithm, $G$ for the Greedy algorithm. The value of $IP, LP$ and $UB$ is in $10^5$.
Table 2.  Computer results for problems in group 2 (slackness = 10)
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 4.759 5.21 18 5.965 18.8
2 9×4 4.856 5.28 19.76 6.382 24.5
3 9×5 4.661 4.38 30.13 6.8 39.5
4 10×3 5.527 5.25 42.36 6.876 14.43
5 10×4 5.595 4.76 15.57 7.014 21.56
6 10×5 5.055 5.75 44.57 7.609 43.35
7 11×5 6.017 7.86 77.65 8.618 31.41
8 11×6 5.997 9.09 80.49 9.149 38.69
9 15×6 8.223 9.09 177.84 12.43 37.39
10 15×7 8.21 7.42 124.02 13.09 47.64
11 20×10 10.89 9.91 265.06 20.73 71.41
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 4.759 5.21 18 5.965 18.8
2 9×4 4.856 5.28 19.76 6.382 24.5
3 9×5 4.661 4.38 30.13 6.8 39.5
4 10×3 5.527 5.25 42.36 6.876 14.43
5 10×4 5.595 4.76 15.57 7.014 21.56
6 10×5 5.055 5.75 44.57 7.609 43.35
7 11×5 6.017 7.86 77.65 8.618 31.41
8 11×6 5.997 9.09 80.49 9.149 38.69
9 15×6 8.223 9.09 177.84 12.43 37.39
10 15×7 8.21 7.42 124.02 13.09 47.64
11 20×10 10.89 9.91 265.06 20.73 71.41
Table 3.  Computer results for problems in group 2 (slackness = 15)
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 4.742 5.17 24.5 5.892 19.3
2 9×4 4.813 4.97 22.69 6.382 24.5
3 9×5 4.559 5.65 36.25 6.66 40.72
4 10×3 5.456 5.09 40.71 6.674 16.1
5 10×4 5.195 4.76 31.97 7.142 30.92
6 10×5 5.355 5.3 42.97 7.609 35.32
7 11×5 6.017 6.54 55.32 8.618 33.86
8 11×6 5.6 7.45 91.46 9.149 41.4
9 15×6 8.403 8.26 125.77 12.43 35.68
10 15×7 8.237 6.65 81.64 13.18 49.35
11 20×10 10.64 8.26 164.22 20.69 68.47
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 4.742 5.17 24.5 5.892 19.3
2 9×4 4.813 4.97 22.69 6.382 24.5
3 9×5 4.559 5.65 36.25 6.66 40.72
4 10×3 5.456 5.09 40.71 6.674 16.1
5 10×4 5.195 4.76 31.97 7.142 30.92
6 10×5 5.355 5.3 42.97 7.609 35.32
7 11×5 6.017 6.54 55.32 8.618 33.86
8 11×6 5.6 7.45 91.46 9.149 41.4
9 15×6 8.403 8.26 125.77 12.43 35.68
10 15×7 8.237 6.65 81.64 13.18 49.35
11 20×10 10.64 8.26 164.22 20.69 68.47
Table 4.  Computer results for problems in group 2 (slackness = 20)
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 5.035 4.53 18.16 6.003 13.12
2 9×4 4.764 5.32 27.23 6.173 29.83
3 9×5 4.758 6.02 26.69 6.899 34.3
4 10×3 5.466 4.92 29.03 6.674 16.1
5 10×4 5.087 5.56 27.07 7.142 32.45
6 10×5 5.075 4.9 40.41 7.609 42.79
7 11×5 6.037 7.41 62.67 8.618 32.18
8 11×6 5.777 7.41 62.09 9.139 46.64
9 15×6 8.012 7.41 133.29 12.4 36.94
10 15×7 8.045 5.82 159.65 13.22 45.71
11 20×10 10.85 7.41 226.27 20.73 66.9
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 5.035 4.53 18.16 6.003 13.12
2 9×4 4.764 5.32 27.23 6.173 29.83
3 9×5 4.758 6.02 26.69 6.899 34.3
4 10×3 5.466 4.92 29.03 6.674 16.1
5 10×4 5.087 5.56 27.07 7.142 32.45
6 10×5 5.075 4.9 40.41 7.609 42.79
7 11×5 6.037 7.41 62.67 8.618 32.18
8 11×6 5.777 7.41 62.09 9.139 46.64
9 15×6 8.012 7.41 133.29 12.4 36.94
10 15×7 8.045 5.82 159.65 13.22 45.71
11 20×10 10.85 7.41 226.27 20.73 66.9
Table 5.  Computer results for problems in group 2 (slackness = 30)
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 4.76 4.8 17.01 5.965 19.3
2 9×4 4.647 5.08 22.76 6.108 30.38
3 9×5 4.684 3.9 22.59 6.8 39.5
4 10×3 5.943 4.9 35.42 6.344 6.74
5 10×4 5.375 4.76 42.1 7.082 26.54
6 10×5 5.315 4.76 47.71 7.609 36.34
7 11×5 6.057 4.76 50.29 8.618 35.51
8 11×6 5.817 6.54 77.44 9.149 46.99
9 15×6 8.221 6.54 114.18 12.37 40.67
10 15×7 8.527 4.29 148.43 13.3 47.33
11 20×10 10.79 5.66 302.54 23.68 70.76
Instances #trucks×#doors Lagrangian relaxation algorithm Greedy algorithm
LR %gap CPU time UB %dev
1 9×3 4.76 4.8 17.01 5.965 19.3
2 9×4 4.647 5.08 22.76 6.108 30.38
3 9×5 4.684 3.9 22.59 6.8 39.5
4 10×3 5.943 4.9 35.42 6.344 6.74
5 10×4 5.375 4.76 42.1 7.082 26.54
6 10×5 5.315 4.76 47.71 7.609 36.34
7 11×5 6.057 4.76 50.29 8.618 35.51
8 11×6 5.817 6.54 77.44 9.149 46.99
9 15×6 8.221 6.54 114.18 12.37 40.67
10 15×7 8.527 4.29 148.43 13.3 47.33
11 20×10 10.79 5.66 302.54 23.68 70.76
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