Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW
Abstract
:1. Introduction
2. Materials and Methods
2.1. Test Problem: VRPTW, Two Objectives of Total Travel Cost and Total Customer Dissatisfaction
- : Arrival time at node ;
- : Wait time at node ;
- if there is no arc from node to node , and 1 otherwise. ;
- Total number of vehicles;
- Total number of customers;
- : Any arbitrary real number;
- : Euclidean distance between node and ;
- : Cost incurred on arc from node to ;
- : Travel time between node and ;
- : Demand at node ;
- : Capacity of vehicle ;
- : Earliest arrival time at node ;
- : Latest arrival time at node ;
- : Service time at node ;
- : Maximum route time allowed for vehicle .
2.2. BB Hypothesis: Goldberg’s Decomposition Theory (7 Steps) and BB Processing Pipeline (5 Steps)
2.3. The Framework across Tasks: SMO
2.3.1. Typical 3 Tasks: 2 Subtasks Boost the Core Task
2.3.2. In total, 4 bags, 4 × 2 Groups, 4 × 2 × 4 Tasks: E.g., Bag 00: Group 1, t1_wc (t_wc 1.0, t_wc 1.1), t2_wc and t2e_wc; Group 2, t1_nc (t_nc 1.0, t_nc 1.1), t2_nc and t2e_nc…
3. Results
3.1. Experimental Setup
3.2. Simulations and Comparisons
4. Discussion
4.1. Transferring and Learning between Key Bag 9, 10 and 12: Common Operator of Insertion, Core Function of Selection
4.2. State of the Art via Two Steps: Gathering Inner Strength (1, Core Task Is Strong Enough), Gathering Outer Storms (2, Boost Core Task via Transferring)
4.3. On the General Spectrum: Gathering Core Tasks beyond MAs Here, like Iterated Greedy Algorithms, Reinforcement Learning Based EA
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Bag 10 | Operators and Transfer Effectiveness | ||
---|---|---|---|
Operators | t2_wc VS t2e_wc | t2_nc VS t2e_nc | |
case 1 | WX S (g5) X S (g6) | ee | ee |
case 2 | WX S (g5) X S (g6) | ie | ee |
case 3 | WX S (g5) X S (g6) | ee | ie |
case 4 | WX S (g5) X S (g6) | ee | e |
case 5 | WX S (g5) X S (g6) | ee | ee |
case 6 | WX S (g5) X S (g6) | ee | ee |
case 7 | WX S (g5) X S (g6) | ee | ee |
case 8 | WX S (g5) X S (g6) | ee | ee |
case 9 | WX S (g5) X S (g6) | ee | ie |
Summary: Bag 10, 8 ee + 1 ie = ee, 6 ee + 2 ie + 1 e = e |
Bag 12 | Operators and Transfer Effectiveness | |
---|---|---|
Operators | t2_wc VS t2e_wc t2_nc VS t2e_nc | |
case 1~9 | WXL (g9) XL (g0) | L: positive selection W: negative selection |
Summary: With additional help of Bag 9, we find that L, W, and S own selection ability! |
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Xu, W.; Wang, X.; Guo, Q.; Song, X.; Zhao, R.; Zhao, G.; Yang, Y.; Xu, T.; He, D. Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW. Mathematics 2022, 10, 2888. https://doi.org/10.3390/math10162888
Xu W, Wang X, Guo Q, Song X, Zhao R, Zhao G, Yang Y, Xu T, He D. Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW. Mathematics. 2022; 10(16):2888. https://doi.org/10.3390/math10162888
Chicago/Turabian StyleXu, Wendi, Xianpeng Wang, Qingxin Guo, Xiangman Song, Ren Zhao, Guodong Zhao, Yang Yang, Te Xu, and Dakuo He. 2022. "Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW" Mathematics 10, no. 16: 2888. https://doi.org/10.3390/math10162888
APA StyleXu, W., Wang, X., Guo, Q., Song, X., Zhao, R., Zhao, G., Yang, Y., Xu, T., & He, D. (2022). Gathering Strength, Gathering Storms: Knowledge Transfer via Selection for VRPTW. Mathematics, 10(16), 2888. https://doi.org/10.3390/math10162888