PRE2019 3 Group16: Difference between revisions
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== Approach == | == Approach == | ||
== Planning == | |||
Week 1: Choosing a subject | |||
Week 2: Planning subject, objectives, users, state-of-the-art, approach, planning, milestones, deliverables, who will do what | |||
Week 3: Research of algorithms | Wiki: finalize subject; finalize objectives; introduction; state-of-the-art | |||
Week 4: Research of algorithms | Implementation and testing of algorithms | Wiki: users; state-of-the-art | |||
Week 5: Finalize research of algorithms | Implementation and testing of algorithms | Wiki: description of algorithms; finalize users; finalize state-of-the-art | |||
Week 6: Finalize implementation and testing | Wiki: finalize description of algorithms; descriptions of results; discussion; future work | |||
Week 7: Finalize wiki | |||
Week 8: Finishing the final presentation and presenting | |||
== Milestones == | == Milestones == | ||
Week 1: Choosing a subject | Week 1: Choosing a subject | ||
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Week 8: Finishing the final presentation and presenting | Week 8: Finishing the final presentation and presenting | ||
==Deliverables == | == Deliverables == | ||
1. Implementation of one or more algorithms that work with the Li & Lim benchmark instances and produce valid solutions such that the number of vehicles is minimized and then the total distance is minimized as much as possible. | 1. Implementation of one or more algorithms that work with the Li & Lim benchmark instances and produce valid solutions such that the number of vehicles is minimized and then the total distance is minimized as much as possible. | ||
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3. Final presentation that will explain our project and results. | 3. Final presentation that will explain our project and results. | ||
==State of the Art== | == State of the Art == | ||
References (of the work of all people that have managed to beat an existing record): | References (of the work of all people that have managed to beat an existing record): | ||
Revision as of 18:22, 12 February 2020
Group Members
| Name | Student Number | Study | |
|---|---|---|---|
| Zakaria Ameziane | 1005559 | Computer Science | z.ameziane@student.tue.nl |
| Cahitcan Uzman | 1284304 | Computer Science | c.uzman@student.tue.nl |
| Efe Utku | |||
| Roel den Hoet | |||
| Venislav Varbanov | 1284401 | Computer Science | v.varbanov@student.tue.nl |
Subject
What/How (AI) algorithms can be used by future self driving delivery cars to efficiently solve the pickup-and-delivery problem with time-windows, where a fleet of delivery vehicles must collect and deliver items according to the demand of customers and their opening hours. The objectives are to minimize the fleet size and to assign a sequence of customers to each truck of the fleet minimizing the total distance traveled.
Objectives
Users
Approach
Planning
Week 1: Choosing a subject
Week 2: Planning subject, objectives, users, state-of-the-art, approach, planning, milestones, deliverables, who will do what
Week 3: Research of algorithms | Wiki: finalize subject; finalize objectives; introduction; state-of-the-art
Week 4: Research of algorithms | Implementation and testing of algorithms | Wiki: users; state-of-the-art
Week 5: Finalize research of algorithms | Implementation and testing of algorithms | Wiki: description of algorithms; finalize users; finalize state-of-the-art
Week 6: Finalize implementation and testing | Wiki: finalize description of algorithms; descriptions of results; discussion; future work
Week 7: Finalize wiki
Week 8: Finishing the final presentation and presenting
Milestones
Week 1: Choosing a subject
Week 2: Planning subject, objectives, users, state-of-the-art, approach, planning, milestones, deliverables, who will do what
Week 3: Research of algorithms | Wiki: finalize subject; finalize objectives; introduction; state-of-the-art
Week 4: Research of algorithms | Implementation and testing of algorithms | Wiki: users; state-of-the-art
Week 5: Finalize research of algorithms | Implementation and testing of algorithms | Wiki: description of algorithms; finalize users; finalize state-of-the-art
Week 6: Finalize implementation and testing | Wiki: finalize description of algorithms; descriptions of results; discussion; future work
Week 7: Finalize wiki
Week 8: Finishing the final presentation and presenting
Deliverables
1. Implementation of one or more algorithms that work with the Li & Lim benchmark instances and produce valid solutions such that the number of vehicles is minimized and then the total distance is minimized as much as possible.
2. Wiki page that contains all the information about our project including the results of running the algorithm(s) on all instances from the Li & Lim benchmark and a comparison with the current records.
3. Final presentation that will explain our project and results.
State of the Art
References (of the work of all people that have managed to beat an existing record):
BBM - Baldacci, Bartolini, and Mingozzi. An Exact Algorithm for the Pickup and Delivery Problem. Operations Research 59(2), pp. 414–426 (2011).
BVH - Bent, R. and Van Hentenryck. P. A Two-Stage Hybrid Algorithm for Pickup and Delivery Vehicle Routing Problems with Time Windows. In Principles and Practice of Constraint Programming (2003).
CLS - Curtois, T., Landa-Silva, D., Qu, Y. and Laesanklang, W., 2018. Large neighbourhood search with adaptive guided ejection search for the pickup and delivery problem with time windows. EURO Journal on Transportation and Logistics, 7(2), pp.151-192.
CVB - Christiaens J. and Vanden Berghe G. A Fresh Ruin & Recreate Implementation for Capacitated Vehicle Routing Problems. To be submitted.
CVB2 - Christiaens J. and Vanden Berghe G. Preliminary title: Slack Induction by String Removals for Vehicle Routing Problems.
DK - Dirk Koning. Using Column Generation for the Pickup and Delivery Problem with Disturbances, Technical Report, Department of Computer Science, Utrecht University, 2011.
EOE - Eirik Krogen Hagen, EOE Koordinering DA. Exploring infeasible and feasible regions of the PDPTW through penalty based tabu search. Working paper.
EMIF - Evgeny Makarov & Ilya Fiks (swatmobile.io).
H - Keld Helsgaun, Working paper, Roskilde University, Denmark (2016).
K – Richard Kelly: Hybrid Ejection Chains and Adaptive LNS for the PDPTW. Working paper.
Li & Lim - Li H. and A. Lim: A MetaHeuristic for the Pickup and Delivery Problem with Time Windows, In Proceedings of the 13th International Conference on Tools with Artificial Intelligence, Dallas, TX, USA, 2001.
MFS - Evgeny Makarov, Ilya Fiks, Eugene Sorokhtin (swatmobile.io). Unpublished.
NB1 - J. Nalepa and M. Blocho. "Enhanced Guided Ejection Search for the Pickup and Delivery Problem with Time Windows", Intelligent Information and Database Systems: Proc. 8th Asian Conference, ACIIDS 2016, pages 388–398. Springer, Heidelberg, 2016.
Q - Quintiq. http://www.quintiq.com/optimization-world-records.aspx.
R - Ropke S. Heuristic and exact algorithms for vehicle routing problems. (2005) . Ph.D. thesis, Computer Science Department, University of Copenhagen (DIKU), Copenhagen
RC - Ropke S. and J.-F. Cordeau. Branch and cut and price for the pickup and delivery problem with time windows. Transportation Sci. 43(3)267–286 (2009).
RP - S. Ropke & D. Pisinger, An Adaptive Large Neighborhood Search Heuristic for the Pickup and Delivery Problem with Time Windows, Technical Report, Department of Computer Science, University of Copenhagen, 2004.
SAM::OPT - Hasle G., O. Kloster: Industrial Vehicle Routing Problems. Chapter in Hasle G., K-A Lie, E. Quak (eds): Geometric Modelling, Numerical Simulation, and Optimization. ISBN 978-3-540-68782-5, Springer 2007.
SB - Carlo Sartori, Luciana Buriol. A matheuristic approach to the PDPTW (to be submitted).
SCR - Piotr Sielski (psielski@emapa.pl), Piotr Cybula, Marek Rogalski, Mariusz Kok, Piotr Beling, Andrzej Jaszkiewicz, Przemysław Pełka. Emapa S.A. www.emapa.pl "Development of universal methods of solving advanced VRP problems with the use of machine learning", unpublished research funded by The National Centre for Research and Development, project number: POIR.01.01.01-00-0012/19. "Optimization of advanced VRP problem variants", unpublished. Computing grant 358 funded by Poznan Supercomputing and Networking Center.
Shobb - http://shobb.narod.ru/vrppd.html
TS - TetraSoft A/S: MapBooking Algoritm for Pickup and Delivery Solutions with Time Windows and Capacity restraints.
WM - Ganzhong Luo (luoganzhong@water-mirror.com), Lei Gao (gaolei@water-mirror.com), Zhixin Liu, Yaning Li, Mingxiang Chen, Qichang Chen, Nuoyi Zhu. "New Algorithms for VRPTW & PDPTW", unpublished result of WATERMIRROR AI.
Other references: