考量工時平衡機制之多次轉移式司機助手車輛途徑問題

簡易檢索 / 詳目顯示

回結果列表

研究生：	黃彥禎 Yen-Zhen Huang
論文名稱：	考量工時平衡機制之多次轉移式司機助手車輛途徑問題 Multiple Transferable Driver Helper Dispatching Problem with Workload Balance
指導教授：	呂志豪 Shih-hao Lu
口試委員:	郭人介 Ren-Jieh Kuo 黃振皓 Chen-Hao Huang
學位類別：	碩士 Master
系所名稱：	管理學院 - 企業管理系 Department of Business Administration
論文出版年：	2023
畢業學年度：	111
語文別：	英文
論文頁數：	80
中文關鍵詞：	車輛途徑問題、轉移式司機助手、工時平衡法、爬山演算法、OR-Tools
外文關鍵詞：	Vehicle Routing Problem, Transferable Driver Helper, Workload Balance, Hill Climbing Algorithm, OR-Tools
相關次數：	點閱：335 下載：14
分享至:	分享至facebook 分享至twitter

查詢本校圖書館目錄查詢臺灣博碩士論文知識加值系統勘誤回報

本研究目的為解決物流業者，在國定假日或購物季等高峰期間，司機工作時間
過長及派送效率不佳等問題。提出兩台卡車搭配一位可轉移助手(2D1H)、三台卡車搭
配一位可轉移助手(3D1H)和三台卡車搭配兩位可轉移助手(3D2H)，模型旨在通過將卡
車與可轉移助手合作來平衡工作負荷並提高送貨效率。研究結果證明使用助手來輔助
司機和卡車的優勢，並成功解決了各種情境下的工作負荷平衡問題該應用何種模型。

在測試不同服務區域、長工時工作點比例和卡車及助手數量後，提供了適當的
路徑規劃模型。調整司機與助手的出發與抵達時間以消除某一卡車優先抵達交換點須
等待另一卡車的閒置時間並顯著的提高送貨效率。本研究採用了 Google 的 OR-tools 和
爬山演算法來優化送貨效率、平衡工作負荷，並為提出了考量工時平衡機制之多次轉
移式司機助手車輛途徑問題（MTDHWB）獲得可行解。

研究結果顯示，選擇使用哪種模型取決於物流或快遞公司的對於派送服務的目
的。提出的 2D1H、3D1H 和 3D2H 模型是平衡工時為目的的最佳選擇，選擇適當的模型取
決於服務區域、長工時工作點比例和卡車及助手數量。2D1H 和 3D2H 不管在都會區、郊
區或者鄉村，都是最合的路徑規劃選擇，而當客戶密度不是很高（約為 10%）時，3D1H
模型可能是一個合適的選項，在相同資源下，2D1H+1D的混和模型會略勝3D1H。然而，
需要進行多樣且多次的測試以得出對使用助手和車隊有效用、有效益且平衡的助手輔
助送貨服務。總體而言，本研究有助於提升現今物流品質，且發展新型態的物流派送
系統。

This study focused on addressing the issue of working hour overload in the logistics
industry during peak periods, such as national holidays or shopping seasons. The proposed models, two drivers with one single helper model (2D1H), three drivers with one helper (3D1H)and three drivers with two helpers (3D2H), aimed to balance the workload and enhance the delivery efficiency by assigning trucks to collaborate with transferable helpers. The study demonstrated the advantages of using helpers to support drivers and trucks and successfully solved the issue of workload balance in every scenario.The study also offered appropriate routing models for different service areas, proportions of long-working-time nodes, and numbers of trucks and helpers. Adjusting the departure and arrival times was identified as an important factor in eliminating idle time and significantly enhancing delivery efficiency. The study implemented Google’s OR-tools and the Hill Climbing Algorithm to optimize the delivery efficiency, balance the workload and obtain feasible solutions for the proposed models of Multiple Transferable Driver Helper Dispatching Problem with Workload Balance (MTDHWB).The findings showed that the determination of which model to use depended on the intended purpose of the logistics or delivery company. The proposed models were optimal choices for balancing workload, and the selection of the appropriate model depended on the service area, proportion of long-working-time nodes, and number of trucks and helpers. Both 2D1H and 3D2H are the most suitable and excellent, while the 3D1H model may be a suitable option when the density is not very high, around 10%. However, further testing is required to obtain more insights on the use of helpers and fleets to support delivery service effectively, efficiently, and equivalently. Overall, this study contributes to the development of efficient and effective logistics and delivery systems.

Table of Contents
ABSTRACT ............................................................................................................................................. i
摘要......................................................................................................................................................... ii
致謝........................................................................................................................................................ iii
Table of Contents ....................................................................................................................................iv
List of Figures..........................................................................................................................................vi
List of Tables..........................................................................................................................................vii
CHAPTER 1 INTRODUCTION............................................................................................................. 1
1-1 Research Background.................................................................................................................... 1
1-2 Research Objective........................................................................................................................ 4
1-3 Research Scope ............................................................................................................................. 5
1-4 Dissertation Organization.............................................................................................................. 6
CHAPTER 2 LITERATURE REVIEW.................................................................................................. 7
2-1 Vehicle Routing Problem (VRP)................................................................................................... 7
2-2 Vehicle Routing Problem with Workload balance (VRPWB).................................................... 10
2-3 Driver Helper Dispatching Problem (DHDP) ............................................................................. 12
2-4 Heuristic for solving vehicle routing problem ............................................................................ 17
CHAPTER 3 RESEARCH METHODOLOGY.................................................................................... 21
3-1 The assumption of Mathematical model ..................................................................................... 21
3-2 Notations..................................................................................................................................... 22
3-3 Mathematical Formulation .......................................................................................................... 23
3-4 Multiple Transferable Driver Helper Dispatching Problem with Workload Balance (MTDHWB)
........................................................................................................................................................... 25
3-5 MTDHWB with Google's OR-Tools........................................................................................... 29
3-6 Performance Evaluation .............................................................................................................. 37
CHAPTER 4 EXPERIMENTAL RESULTS........................................................................................ 41
4-1 Design of Experiment ................................................................................................................. 41
4-2 Significance................................................................................................................................. 44
4-3 Experimental Results .................................................................................................................. 47
4-3-1 Results of Model 2D1H ....................................................................................................... 47
4-3-2 Results of Model 3D1H ....................................................................................................... 49
4-3-3 Results of Model 3D2H ....................................................................................................... 51
4-4 Summary ..................................................................................................................................... 53

CHAPTER 5 CONCLUSION AND DISCUSSION ............................................................................. 54
5-1 Conclusions and Contribution..................................................................................................... 54
5-2 Managerial Implication ............................................................................................................... 54
5-3 Limitation and Future Research .................................................................................................. 55
References ............................................................................................................................................. 57
Appendix ............................................................................................................................................... 67



List of Figures
Fig 1- 1. Research organization.............................................................................................. 6
Fig 2- 1. An illustrate pattern of Vehicle routing problem..................................................... 8
Fig 2- 2. An example of VRPWB workload and routes....................................................... 10
Fig 2- 3. A solution illustration of Two-Echelon Vehicle Routing Problem ....................... 16
Fig 3- 1. Model Illustration of MTDHWB (2D1H).............................................................. 27
Fig 3- 2. Model Illustration of MTDHWB (3D1H).............................................................. 27
Fig 3- 3. Model Illustration of MTDHWB (3D2H).............................................................. 27
Fig 3- 4. The departure and arrival time adjustment with 2 trucks (Before)........................ 28
Fig 3- 5. The departure and arrival time adjustment with 2 trucks (After) .......................... 28
Fig 3- 6. The departure and arrival time adjustment with 3 trucks (Before)........................ 29
Fig 3- 7. The departure and arrival time adjustment with 3 trucks (After) .......................... 29
Fig 3- 8. Cluster first and routing second phrase.................................................................. 30
Fig 3- 9. Select the exchange point for workload balance of 2D1H model.......................... 32
Fig 3- 10. Programming process of 3D1H model................................................................. 35
Fig 3- 11. Programming process of 3D2H model................................................................. 36
Fig 3- 12. The benchmarks comparisons with 2D1H model................................................ 38
Fig 3- 13. The benchmarks comparisons with 3D1H model................................................ 39
Fig 3- 14. The benchmarks comparisons with 3D2H model................................................ 40
Fig 4- 1. The experimental result of 2D1H model at metropolitan area .............................. 48
Fig 4- 2. The experimental result of 2D1H model at suburban area.................................... 48
Fig 4- 3. The experimental result of 2D1H model at rural area........................................... 48
Fig 4- 4. The experimental result of 3D1H model at metropolitan area ............................. 50
Fig 4- 5. The experimental result of 3D1H model at suburban area.................................... 50
Fig 4- 6. The experimental result of 3D1H model at rural area........................................... 50
Fig 4- 7. The experimental result of 3D2H model at metropolitan area .............................. 52
Fig 4- 8. The experimental result of 3D2H model at suburban area.................................... 52
Fig 4- 9. The experimental result of 3D2H model at rural area........................................... 52

List of Tables
Table 3- 1. The notations for sets used in the model............................................................ 22
Table 3- 2. The decision variables used in the model .......................................................... 23
Table 3- 3. The fixed parameters required in the model ...................................................... 23
Table 4- 1. Setting of test instances...................................................................................... 42
Table 4- 2. Experimental combinations................................................................................ 42
Table 4- 3. Performance of proposed method of 2 drivers and 1 helper by factors............. 44
Table 4- 4. Performance of proposed method of 3 drivers and 1 helper by factors ............. 45
Table 4- 5. Performance of proposed method of 3 drivers and 2 helpers by factors............ 46




                                

Baxter, J. (1984). Depot location: A technique for the avoidance of local optima. European
Journal of Operational Research, 18(2), 208–214. https://doi.org/10.1016/0377-
2217(84)90186-3
Benjamin, A. M., & Abdul-Rahman, S. (2016). Driver’s workload comparison in waste
collection vehicle routing problem. AIP Conference Proceedings.
https://doi.org/10.1063/1.4966071
Bodin, L. (1983). Routing and Scheduling of Vehicles and Crews. Computer & Operations
Research, 10(2), 69–211. https://cir.nii.ac.jp/crid/1570291225647023232
Bowen, D. (2020). DRO Expectations vs Reality. Info.etruckbizforfxg.com.
https://info.etruckbizforfxg.com/blog/dro-expectations-vs-reality
Bowerman, R. L., Calamai, P. H., & Brent Hall, G. (1994). The spacefilling curve with
optimal partitioning heuristic for the vehicle routing problem. European Journal of
Operational Research, 76(1), 128–142. https://doi.org/10.1016/0377-2217(94)90011-
6
Bowerman, R., Hall, B., & Calamai, P. (1995). A multi-objective optimization approach to
urban school bus routing: Formulation and solution method. Transportation Research
Part A: Policy and Practice, 29(2), 107–123. https://doi.org/10.1016/0965-
8564(94)E0006-U
Brandão, J. (2011). A tabu search algorithm for the heterogeneous fixed fleet vehicle routing
problem. Computers & Operations Research, 38(1), 140–151.
https://doi.org/10.1016/j.cor.2010.04.008
Brandstätter, C., & Reimann, M. (2018). The Line-haul Feeder Vehicle Routing Problem:
Mathematical model formulation and heuristic approaches. European Journal of
Operational Research, 270(1), 157–170. https://doi.org/10.1016/j.ejor.2018.03.014
Chen, H.-K., Chou, H.-W., & Hsu, C.-Y. (2011). The Linehaul-Feeder Vehicle Routing
Problem with Virtual Depots and Time Windows. Mathematical Problems in
Engineering, 2011, 1–15. https://doi.org/10.1155/2011/759418
Christofides, N. (1976). The vehicle routing problem. RAIRO - Operations Research -
Recherche Opérationnelle, 10(V1), 55–70. https://eudml.org/doc/104635
Clarke, G., & Wright, J. W. (1964). Scheduling of Vehicles from a Central Depot to a
Number of Delivery Points. Operations Research, 12(4), 568–581.
https://doi.org/10.1287/opre.12.4.568
CLEVON. (2022, June 16). Cleveron Mobility has partnered with DHL Express, the world’s
leading logistics service provider. Clevon. https://clevon.com/blog/cleveron-mobilityhas-partnered-with-dhl-express-the-worlds-leading-logistics-service-provider/
Conley, P. (2023, March 29). NRF forecasts as much as a 12% rise in online and other nonstore sales in 2023. Digital Commerce 360.
https://www.digitalcommerce360.com/2023/03/29/nrf-forecasts-12-percent-rise-inonline-sales-in-2023/
Cordeau, J.-F., Laporte, G., Savelsbergh, M. W. P., & Vigo, D. (2007, January 1). Chapter 6
Vehicle Routing (C. Barnhart & G. Laporte, Eds.). ScienceDirect; Elsevier.
https://www.sciencedirect.com/science/article/pii/S0927050706140062
Dantzig, G. B., & Ramser, J. H. (1959). The Truck Dispatching Problem. Management
Science, 6(1), 80–91.
https://econpapers.repec.org/article/inmormnsc/v_3a6_3ay_3a1959_3ai_3a1_3ap_3a8
0-91.htm
Deng, P., Amirjamshidi, G., & Roorda, M. (2020). A vehicle routing problem with movement
synchronization of drones, sidewalk robots, or foot-walkers. Transportation Research
Procedia, 46, 29–36. https://doi.org/10.1016/j.trpro.2020.03.160
Domínguez-Martín, B., Rodríguez-Martín, I., & Salazar-González, J.-J. (2018). The driver
and vehicle routing problem. Computers & Operations Research, 92, 56–64.
https://doi.org/10.1016/j.cor.2017.12.010
Drexl, M. (2012). Synchronization in Vehicle Routing—A Survey of VRPs with Multiple
Synchronization Constraints. Transportation Science, 46(3), 297–316.
https://doi.org/10.1287/trsc.1110.0400
Drexl, M. (2014). A Generic Heuristic for Vehicle Routing Problems with Multiple
Synchronization Constraints. Working Papers.
https://ideas.repec.org/p/jgu/wpaper/1412.html
E-Commerce in Taiwan - Outlook of 2023 and Retail Trends - OOSGA. (2023). Oosga.com.
https://oosga.com/e-commerce/twn/
Erdoğan, S., & Miller-Hooks, E. (2012). A Green Vehicle Routing Problem. Transportation
Research Part E: Logistics and Transportation Review, 48(1), 100–114.
https://doi.org/10.1016/j.tre.2011.08.001
Events, Uk. M. &. (2022, April 21). VIDEO: Deutsche Post DHL carrier neutral parcel
lockers at train stations. Parcel and Postal Technology International.
https://www.parcelandpostaltechnologyinternational.com/analysis/video-deutschepost-dhl-carrier-neutral-parcel-lockers-at-train-stations.html
Filipec, M., Skrlec, D., & Krajcar, S. (1998, October 1). An efficient implementation of
genetic algorithms for constrained vehicle routing problem. IEEE Xplore.
https://doi.org/10.1109/ICSMC.1998.724987
Foa, S., Coppola, C., Grani, G., & Palagi, L. (2022). Solving the vehicle routing problem with
deep reinforcement learning. ArXiv:2208.00202 [Cs, Math].
https://arxiv.org/abs/2208.00202
Gao, J., Zheng, X., Gao, F., Tong, X., & Han, Q. (2022). Heterogeneous Multitype Fleet
Green Vehicle Path Planning of Automated Guided Vehicle with Time Windows in
Flexible Manufacturing System. Machines, 10(3), 197.
https://doi.org/10.3390/machines10030197
Gendreau, M., Laporte, G., & Séguin, R. (1996). Stochastic vehicle routing. European Journal
of Operational Research, 88(1), 3–12. https://doi.org/10.1016/0377-2217(95)00050-x
Goel, A., Archetti, C., & Savelsbergh, M. (2012). Truck driver scheduling in Australia.
Computers & Operations Research, 39(5), 1122–1132.
https://doi.org/10.1016/j.cor.2011.05.021
Goel, A., & Rousseau, L.-M. (2011). Truck driver scheduling in Canada. Journal of
Scheduling. https://doi.org/10.1007/s10951-011-0249-6
Goetschalckx, M., & Jacobs-Blecha, C. (1989). The vehicle routing problem with backhauls.
European Journal of Operational Research, 42(1), 39–51.
https://econpapers.repec.org/article/eeeejores/v_3a42_3ay_3a1989_3ai_3a1_3ap_3a39
-51.htm
Hemmelmayr, V. C., Cordeau, J.-F., & Crainic, T. G. (2012). An adaptive large neighborhood
search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics.
Computers & Operations Research, 39(12), 3215–3228.
https://doi.org/10.1016/j.cor.2012.04.007
Holland, J. H. (1992, August 29). Adaptation in Natural and Artificial Systems. MIT Press.
https://mitpress.mit.edu/9780262581110/adaptation-in-natural-and-artificial-systems/
Hoos, H. H., & Stützle, T. (2005, January 1). 2 - SLS METHODS (H. H. Hoos & T. Stützle,
Eds.). ScienceDirect ; Morgan Kaufmann.
https://www.sciencedirect.com/science/article/pii/B9781558608726500196
Howell, E. (2023, April 7). Hill Climbing Optimization Algorithm: A Simple Beginner’s
Guide. Medium. https://towardsdatascience.com/hill-climbing-optimization-algorithmsimply-explained-dbf1e1e3cf6c
Ibarra-Rojas, O. J., & Silva-Soto, Y. (2021). Vehicle routing problem considering equity of
demand satisfaction. Optimization Letters. https://doi.org/10.1007/s11590-021-01704-
5
Inman, D. (2022, November 29). Record 196.7 Million Consumers Shop Over Thanksgiving
Holiday Weekend. NRF. https://nrf.com/media-center/press-releases/record-1967-
million-consumers-shop-over-thanksgiving-holiday-weekend
InsightAce Analytic Pvt. LTd. (2023, March 24). Last Mile Delivery Market to Reach USD
357.45 Billion to 2031 | Reveals Exclusive InsightAce Study. Yahoo Finance.
https://finance.yahoo.com/news/last-mile-delivery-market-reach130700059.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmN
vbS8&guce_referrer_sig=AQAAALqhMKekg7z7so5EIwlga7p2MLuSE5Scg78D7A6
rWoRzmQXCSIQhaD6XVEG9Ku6dDeERXLQ26WXfMXVfh-vwB8PxUI6SmVJAr
James, J., & Felix, R. (2020, January 24). Shipping Carriers Compared: DHL Vs. FedEx Vs.
UPS in 2020. Lateshipment.com Blog. https://www.lateshipment.com/blog/overviewof-fedex-ups-and-dhl/
Jozefowiez, N., Semet, F., & Talbi, E.-G. (2002). Parallel and Hybrid Models for Multiobjective Optimization: Application to the Vehicle Routing Problem. Parallel Problem
Solving from Nature — PPSN VII, 271–280. https://doi.org/10.1007/3-540-45712-
7_26
Jozefowiez, N., Semet, F., & Talbi, E.-G. (2009). An evolutionary algorithm for the vehicle
routing problem with route balancing. European Journal of Operational Research,
195(3), 761–769. https://doi.org/10.1016/j.ejor.2007.06.065
Julia, S., & Chandana, K. S. (2020). A NEW APPROACH FOR SOLVING THE
DISRUPTION IN VEHICLE ROUTING PROBLEM DURING DELIVERY.
Современные информационные технологии и ИТ-образование, 16(2), 371–378.
https://cyberleninka.ru/article/n/a-new-approach-for-solving-the-disruption-in-vehicle-
routing-problem-during-delivery
Kalakanti, A. K., Verma, S., Paul, T., & Yoshida, T. (2019, September 1). RL SolVeR Pro:
Reinforcement Learning for Solving Vehicle Routing Problem. IEEE Xplore.
https://doi.org/10.1109/AiDAS47888.2019.8970890
KANAZAWA, H., SUZUKI, M., ONODA, T., & YOKOZAWA, N. (2006). Excess workload
and sleep-related symptoms among commercial long-haul truck drivers. Sleep and
Biological Rhythms, 4(2), 121–128. https://doi.org/10.1111/j.1479-8425.2006.00218.x
Keenan, M. (2022, November 14). Global Ecommerce Statistics and Trends to Launch Your
Business Beyond Borders. Enterprise Ecommerce Blog - Enterprise Business
Marketing, News, Tips & More. https://www.shopify.com/enterprise/globalecommerce-statistics
Keskinturk, T., & Yildirim, M. B. (2011, June 1). A genetic algorithm metaheuristic for
bakery distribution vehicle routing problem with load balancing. IEEE Xplore.
https://doi.org/10.1109/INISTA.2011.5946077
Kok, A. L., Meyer, C. M., Kopfer, H., & Schutten, J. M. J. (2010). A Dynamic Programming
Heuristic for the Vehicle Routing Problem with Time Windows and European
Community Social Legislation. Transportation Science, 44(4), 442–454.
https://www.jstor.org/stable/25769513
Korosec, K. (2021, May 29). Walmart to launch autonomous delivery service with Ford and
Argo AI. TechCrunch. https://techcrunch.com/2021/09/15/walmart-to-launchautonomous-delivery-service-with-ford-and-argo-ai/
Ku, Y.-C. (2022, June 9). Vehicle Routing Problem with Transferable Hybrid Driver Helper.
Ndltd.ncl.edu.tw. https://hdl.handle.net/11296/kq58v3
Kumar, D. A., & Rangan, C. P. (2022). Approximation algorithms for the Traveling Salesman
Problem with range condition. RAIRO Theor. Informatics Appl.
https://www.semanticscholar.org/paper/Approximation-algorithms-for-the-TravelingSalesman-Kumar-Rangan/74bbae37a226fd982f51286a0af0ee4c3afe5990
Kuo, R. J., Edbert, E., Zulvia, F. E., & Lu, S.-H. (2023). Applying NSGA-II to vehicle
routing problem with drones considering makespan and carbon emission. Expert
Systems with Applications, 221, 119777. https://doi.org/10.1016/j.eswa.2023.119777
Laporte, G. (1992). The vehicle routing problem: An overview of exact and approximate
algorithms. European Journal of Operational Research, 59(3), 345–358.
https://doi.org/10.1016/0377-2217(92)90192-c
Laporte, G., Ropke, S., & Vidal, T. (2014). Chapter 4: Heuristics for the Vehicle Routing
Problem. Vehicle Routing, 87–116. https://doi.org/10.1137/1.9781611973594.ch4
Lee, T., & Ueng, J. (1999, December 1). A study of vehicle routing problems with load‐
balancing.
https://www.emerald.com/insight/content/doi/10.1108/09600039910300019/full/html
Li, J., Fang, Y., & Tang, N. (2022). A cluster-based optimization framework for vehicle
routing problem with workload balance. Computers & Industrial Engineering, 169,
108221. https://doi.org/10.1016/j.cie.2022.108221
Lin, S. (1965). Computer Solutions of the Traveling Salesman Problem. Bell System
Technical Journal, 44(10), 2245–2269. https://doi.org/10.1002/j.1538-
7305.1965.tb04146.x
Lin, S.-W., Yu, V. F., & Chou, S.-Y. (2009). Solving the truck and trailer routing problem
based on a simulated annealing heuristic. Computers & Operations Research, 36(5),
1683–1692. https://doi.org/10.1016/j.cor.2008.04.005
Lin, S.-W., Yu, V. F., & Chou, S.-Y. (2010). A note on the truck and trailer routing problem.
Expert Systems with Applications, 37(1), 899–903.
https://doi.org/10.1016/j.eswa.2009.06.077
Linfati, R., Yáñez-Concha, F., & Escobar, J. W. (2022). Mathematical Models for the Vehicle
Routing Problem by Considering Balancing Load and Customer Compactness.
Sustainability, 14(19), 12937. https://doi.org/10.3390/su141912937
López-Sánchez, A. D., Hernández-Díaz, A. G., Vigo, D., Caballero, R., & Molina, J. (2014).
A multi-start algorithm for a balanced real-world Open Vehicle Routing Problem.
European Journal of Operational Research, 238(1), 104–113.
https://doi.org/10.1016/j.ejor.2014.04.008
Lu, S., Suzuki, Y., & Clottey, T. (2020). The Last Mile: Managing Driver Helper Dispatching
for Package Delivery Services. Journal of Business Logistics.
https://doi.org/10.1111/jbl.12242
Lu, S., Suzuki, Y., & Clottey, T. (2022). Improving the efficiency of last‐mile package
deliveries using hybrid driver helpers. Decision Sciences.
https://doi.org/10.1111/deci.12559
Lu, S.-H. (2017). Driver Helper Dispatching Problems: Three Essays - ProQuest.
Www.proquest.com. https://www.proquest.com/docview/1917793972?pqorigsite=gscholar&fromopenview=true
Lu, S.-H., Kuo, R. J., Ho, Y.-T., & Nguyen, A.-T. (2022). Improving the efficiency of lastmile delivery with the flexible drones traveling salesman problem. Expert Systems
with Applications, 209, 118351. https://doi.org/10.1016/j.eswa.2022.118351
Mahajan, K., Velaga, N. R., Kumar, A., Choudhary, A., & Choudhary, P. (2019). Effects of
driver work-rest patterns, lifestyle and payment incentives on long-haul truck driver
sleepiness. Transportation Research Part F: Traffic Psychology and Behaviour, 60,
366–382. https://doi.org/10.1016/j.trf.2018.10.028
Mancini, S. (2016). A real-life Multi Depot Multi Period Vehicle Routing Problem with a
Heterogeneous Fleet: Formulation and Adaptive Large Neighborhood Search based
Matheuristic. Transportation Research Part C: Emerging Technologies, 70, 100–112.
https://doi.org/10.1016/j.trc.2015.06.016
Mancini, S., Gansterer, M., & Hartl, R. F. (2021). The collaborative consistent vehicle routing
problem with workload balance. European Journal of Operational Research, 293(3),
955–965. https://doi.org/10.1016/j.ejor.2020.12.064
Matl, P., Hartl, R. F., & Vidal, T. (2018). Workload Equity in Vehicle Routing Problems: A
Survey and Analysis. Transportation Science, 52(2), 239–260.
https://doi.org/10.1287/trsc.2017.0744
MICH, D. (2021, September 15). Ford, Argo AI, and Walmart to Launch Autonomous
Vehicle Delivery Service in Three U.S. Cities | Ford Media Center. Media.ford.com.
https://media.ford.com/content/fordmedia/fna/us/en/news/2021/09/15/ford-argo-aiand-walmart.html
Murray, C. C., & Chu, A. G. (2015). The flying sidekick traveling salesman problem:
Optimization of drone-assisted parcel delivery. Transportation Research Part C:
Emerging Technologies, 54, 86–109. https://doi.org/10.1016/j.trc.2015.03.005
Nair, T. R. G., & Sooda, K. (2010). Comparison of Genetic Algorithm and Simulated
Annealing Technique for Optimal Path Selection In Network Routing.
ArXiv:1001.3920 [Cs]. https://arxiv.org/abs/1001.3920
Nuha, H., Wati, P. E. D. K., & Widiasih, W. (2018). A Comparison of Exact Method -
Metaheuristic Method in Determination for Vehicle Routing Problem. MATEC Web
of Conferences, 204, 02017. https://doi.org/10.1051/matecconf/201820402017
Osman, I. H. (1993). Metastrategy simulated annealing and tabu search algorithms for the
vehicle routing problem. Annals of Operations Research, 41(4), 421–451.
https://doi.org/10.1007/bf02023004
Pisinger, D., & Ropke, S. (2018). Large Neighborhood Search. Handbook of Metaheuristics,
99–127. https://doi.org/10.1007/978-3-319-91086-4_4
Pullen, H., & Webb, M. (1967, November 29). A Computer Application to a Transport
Scheduling Problem. Computer Journal, 10, 10-13. - References - Scientific Research
Publishing. Www.scirp.org.
https://www.scirp.org/(S(351jmbntvnsjt1aadkozje))/reference/referencespapers.aspx?r
eferenceid=1429162
Rhodes, K., Nehring, R., Wilk, B., & Patel, N. (2007, April 1). UPS Helper Dispatch
Analysis. IEEE Xplore. https://doi.org/10.1109/SIEDS.2007.4374034
Rocki, K., & Suda, R. (2012, May 1). Accelerating 2-opt and 3-opt Local Search Using GPU
in the Travelling Salesman Problem. IEEE Xplore.
https://doi.org/10.1109/CCGrid.2012.133
Sacramento, D., Pisinger, D., & Ropke, S. (2019). An adaptive large neighborhood search
metaheuristic for the vehicle routing problem with drones. Transportation Research
Part C: Emerging Technologies, 102, 289–315.
https://doi.org/10.1016/j.trc.2019.02.018
Sartori, C., Smet, P., Greet, V., & Berghe. (2021). Efficient Duration-Based Workload
Balancing for Interdependent Vehicle Routes.
https://doi.org/10.4230/OASIcs.ATMOS.2021.1
Shiyi, Y., Jianwen, F., Feng, C., & Xin, Z. (2020). Truck and Trailer Routing Problem
Solving by a Backtracking Search Algorithm. Journal of Systems Science and
Information, 8(3), 253–272. https://ideas.repec.org/a/bpj/jossai/v8y2020i3p253-
272n4.html
Sivaramkumar, V., Thansekhar, M., Saravanan, R., & Miruna Joe Amali, S. (2015). Multiobjective vehicle routing problem with time windows: Improving customer
satisfaction by considering gap time. Proceedings of the Institution of Mechanical
Engineers, Part B: Journal of Engineering Manufacture, 231(7), 1248–1263.
https://doi.org/10.1177/0954405415586608
Surana, P. (2019). Benchmarking Optimization Algorithms for Capacitated Vehicle Routing
Problems. Master’s Projects. https://doi.org/10.31979/etd.cjjg-7wvf
Tan, K. C., Chew, Y. H., & Lee, L. H. (2005). A Hybrid Multiobjective Evolutionary
Algorithm for Solving Vehicle Routing Problem with Time Windows. Computational
Optimization and Applications, 34(1), 115–151. https://doi.org/10.1007/s10589-005-
3070-3
Tan, S.-Y., & Yeh, W.-C. (2021). The Vehicle Routing Problem: State-of-the-Art
Classification and Review. Applied Sciences, 11(21), 10295.
https://doi.org/10.3390/app112110295
team, A. (2021, May 7). Vehicle Routing Software Problem: How to Effectively Solve.
Altamira. https://www.altamira.ai/blog/how-to-solve-the-vehicle-routing-problem/
UNCTAD. (2021, March 15). How COVID-19 triggered the digital and e-commerce turning
point | UNCTAD. UNCTAD. https://unctad.org/news/how-covid-19-triggered-digitaland-e-commerce-turning-point
UPS. (2020, January 29). UPS To Enhance ORION With Continuous Delivery Route
Optimization | About UPS. About UPS-US.
https://about.ups.com/us/en/newsroom/press-releases/innovation-driven/ups-toenhance-orion-with-continuous-delivery-route-optimization.html
Weber, A. N., & Badenhorst-Weiss, J. A. (2018). The last-mile logistical challenges of an
omnichannel grocery retailer: A South African perspective. Journal of Transport and
Supply Chain Management, 12. https://doi.org/10.4102/jtscm.v12i0.398
Wen, M., Cordeau, J.-F., Laporte, G., & Larsen, J. (2010). The dynamic multi-period vehicle
routing problem. Computers & Operations Research, 37(9), 1615–1623.
https://doi.org/10.1016/j.cor.2009.12.002
Williamson, Z. (2022, July 18). UPS saving millions at the pump, emphasizes importance of
planning ahead. KMTV 3 News Now Omaha.
https://www.3newsnow.com/news/local-news/ups-saving-millions-at-the-pumpemphasizes-importance-of-planning-ahead
Woods, C. (2023, February 21). The benefits of last mile delivery for businesses. Locate2u.
https://www.locate2u.com/last-mile-delivery/benefits-of-last-mile-delivery/
Wren, A., & Carr, J. D. (1971). COMPUTERS IN TRANSPORT PLANNING AND
OPERATION. Trid.trb.org. https://trid.trb.org/view/128808
Wren, A., & Holliday, A. (1972). Computer Scheduling of Vehicles from One or More
Depots to a Number of Delivery Points. Operational Research Quarterly (1970-1977),
23(3), 333. https://doi.org/10.2307/3007888
Xu, R., Guo, R., & Jia, Q. (2019). A Novel Hybrid Metaheuristic for Solving Automobile Part
Delivery Logistics With Clustering Customer Distribution. IEEE Access, 7, 106075–
106091. https://doi.org/10.1109/access.2019.2931622

簡易檢索 / 詳目顯示

相關論文