Heuristic approach for optimising reliable supply chain network using drones in last-mile delivery under uncertainty

References

• Agatz, N., Bouman, P., & Schmidt, M. (2018). Optimization approaches for the traveling salesman problem with drone. Transportation Science, 52(4), 965–981. https://doi.org/10.1287/trsc.2017.0791
   Web of Science ®Google Scholar
• Alem, D., Clark, A., & Moreno, A. (2016). Stochastic network models for logistics planning in disaster relief. European Journal of Operational Research, 255(1), 187–206. https://doi.org/10.1016/j.ejor.2016.04.041
   Web of Science ®Google Scholar
• Arifin, M. D. (2011). Location allocation problem using algorithm and simulated annealing: A case study based on school in Enschede [Master's thesis]. Retrieved from University of Twente Repository: [https://essay.utwente.nl/84903/].
   Google Scholar
• Azizi, N. (2019). Managing facility disruption in hub-and-spoke networks: Formulations and efficient solution methods. Annals of Operations Research, 272(1), 159–185. https://doi.org/10.1007/s10479-017-2517-0
   Web of Science ®Google Scholar
• Azizi, N., Chauhan, S., Salhi, S., & Vidyarthi, N. (2016). The impact of hub failure in hub-and-spoke networks: Mathematical formulations and solution techniques. Computers & Operations Research, 65, 174–188. https://doi.org/10.1016/j.cor.2014.05.012
   Web of Science ®Google Scholar
• Berman, O., Krass, D., & Menezes, M. B. (2007). Facility reliability issues in network p-median problems: Strategic centralization and co-location effects. Operations Research, 55(2), 332–350. https://doi.org/10.1287/opre.1060.0348
   Web of Science ®Google Scholar
• Berman, O., Larson, R. C., & Fouska, N. (1992). Optimal location of discretionary service facilities. Transportation Science, 26(3), 201–211. https://doi.org/10.1287/trsc.26.3.201
   Web of Science ®Google Scholar
• Capar, I., Kuby, M., Leon, V. J., & Tsai, Y. J. (2013). An arc cover–path-cover formulation and strategic analysis of alternative-fuel station locations. European Journal of Operational Research, 227(1), 142–151. https://doi.org/10.1016/j.ejor.2012.11.033
   Web of Science ®Google Scholar
• Cardona-Valdés, Y., Álvarez, A., & Pacheco, J. (2014). Metaheuristic procedure for a bi-objective supply chain design problem with uncertainty. Transportation Research Part B: Methodological, 60, 66–84. https://doi.org/10.1016/j.trb.2013.11.010
   Web of Science ®Google Scholar
• Chopra, S., & Meindl, P. (2007). Supply chain management. Strategy, planning & operation. In Das summa summarum des management (pp. 265–275). Gabler.
   Google Scholar
• Clarke, R. (2014). Understanding the drone epidemic. Computer Law & Security Review, 30(3), 230–246. https://doi.org/10.1016/j.clsr.2014.03.002
   Web of Science ®Google Scholar
• Cui, T., Ouyang, Y., & Shen, Z. J. M. (2010). Reliable facility location design under the risk of disruptions. Operations Research, 58(4-part-1), 998–1011. https://doi.org/10.1287/opre.1090.0801
   Web of Science ®Google Scholar
• D'Andrea, R. (2014). Guest editorial can drones deliver? IEEE Transactions on Automation Science and Engineering, 11(3), 647–648. https://doi.org/10.1109/TASE.2014.2326952
   Web of Science ®Google Scholar
• Dorling, K., Heinrichs, J., Messier, G. G., & Magierowski, S. (2017). Vehicle routing problems for drone delivery. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 47(1), 70–85. https://doi.org/10.1109/TSMC.2016.2582745
   Web of Science ®Google Scholar
• Döyen, A., Aras, N., & Barbarosoğlu, G. (2012). A two-echelon stochastic facility location model for humanitarian relief logistics. Optimization Letters, 6(6), 1123–1145. https://doi.org/10.1007/s11590-011-0421-0
   Web of Science ®Google Scholar
• Drezner, Z. (1987). Heuristic solution methods for two location problems with unreliable facilities. Journal of the Operational Research Society, 38(6), 509–514. https://doi.org/10.1057/jors.1987.88
   Web of Science ®Google Scholar
• Drezner, Z., & Wesolowsky, G. O. (2003). Network design: Selection and design of links and facility location. Transportation Research Part A: Policy and Practice, 37(3), 241–256. https://doi.org/10.1016/S0965-8564(02)00014-9
   Web of Science ®Google Scholar
• Farrokh, M., Azar, A., Jandaghi, G., & Ahmadi, E. (2018). A novel robust fuzzy stochastic programming for closed loop supply chain network design under hybrid uncertainty. Fuzzy Sets and Systems, 341, 69–91. https://doi.org/10.1016/j.fss.2017.03.019
   Web of Science ®Google Scholar
• Fattahi, M., Mahootchi, M., Govindan, K., & Husseini, S. M. M. (2015). Dynamic supply chain network design with capacity planning and multi-period pricing. Transportation Research Part E: Logistics and Transportation Review, 81, 169–202. https://doi.org/10.1016/j.tre.2015.06.007
   Web of Science ®Google Scholar
• Figliozzi, M. A., Tucker, C., & Polikakhina, P. (2018). Drone deliveries logistics, efficiency, safety and last mile trade-offs.
   Google Scholar
• Finn, R. L., & Wright, D. (2012). Unmanned aircraft systems: Surveillance, ethics and privacy in civil applications. Computer Law & Security Review, 28(2), 184–194. https://doi.org/10.1016/j.clsr.2012.01.005
   Web of Science ®Google Scholar
• Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2014). Cost modelling and simulation of last-mile characteristics in an innovative B2C supply chain environment with implications on urban areas and cities. Procedia - Social and Behavioral Sciences, 125, 398–411. https://doi.org/10.1016/j.sbspro.2014.01.1483
   Google Scholar
• Gong, D., Gen, M., Xu, W., & Yamazaki, G. (1995). Hybrid evolutionary method for obstacle location-allocation. Computers & Industrial Engineering, 29(1–4), 525–530. https://doi.org/10.1016/0360-8352(95)00128-N
   Google Scholar
• Govindan, K., Fattahi, M., & Keyvanshokooh, E. (2017). Supply chain network design under uncertainty: A comprehensive review and future research directions. European Journal of Operational Research, 263(1), 108–141. https://doi.org/10.1016/j.ejor.2017.04.009
   Web of Science ®Google Scholar
• Govindan, K., Jafarian, A., & Nourbakhsh, V. (2015). Bi-objective integrating sustainable order allocation and sustainable supply chain network strategic design with stochastic demand using a novel robust hybrid multi-objective metaheuristic. Computers & Operations Research, 62, 112–130. https://doi.org/10.1016/j.cor.2014.12.014
   Web of Science ®Google Scholar
• Grippa, P., Behrens, D. A., Wall, F., & Bettstetter, C. (2019). Drone delivery systems: Job assignment and dimensioning. Autonomous Robots, 43(2), 261–274. https://doi.org/10.1007/s10514-018-9768-8
   Web of Science ®Google Scholar
• Gutiérrez, G. J., Kouvelis, P., & Kurawarwala, A. A. (1996). A robustness approach to uncapacitated network design problems. European Journal of Operational Research, 94(2), 362–376. https://doi.org/10.1016/0377-2217(95)00160-3
   Web of Science ®Google Scholar
• Ha, Q. M., Deville, Y., Pham, Q. D., & Hà, M. H. (2018). On the min-cost traveling salesman problem with drone. Transportation Research Part C: Emerging Technologies, 86, 597–621. https://doi.org/10.1016/j.trc.2017.11.015
   Web of Science ®Google Scholar
• Hasani, A., Mokhtari, H., & Fattahi, M. (2021). A multi-objective optimization approach for green and resilient supply chain network design: A real-life case study. Journal of Cleaner Production, 278, 123199. https://doi.org/10.1016/j.jclepro.2020.123199
   Web of Science ®Google Scholar
• Hatefi, S. M., Jolai, F., Torabi, S. A., & Tavakkoli-Moghaddam, R. (2015). A credibility-constrained programming for reliable forward–reverse logistics network design under uncertainty and facility disruptions. International Journal of Computer Integrated Manufacturing, 28(6), 664–678. https://doi.org/10.1080/0951192X.2014.900863
   Web of Science ®Google Scholar
• He, F., Yin, Y., & Zhou, J. (2015). Deploying public charging stations for electric vehicles on urban road networks. Transportation Research Part C: Emerging Technologies, 60, 227–240. https://doi.org/10.1016/j.trc.2015.08.018
   Web of Science ®Google Scholar
• Hodgson, M. J. (1990). A flow-capturing location-allocation model. Geographical Analysis, 22(3), 270–279. https://doi.org/10.1111/j.1538-4632.1990.tb00210.x
   Web of Science ®Google Scholar
• Hong, I., Kuby, M., & Murray, A. T. (2018). A range-restricted recharging station coverage model for drone delivery service planning. Transportation Research Part C: Emerging Technologies, 90, 198–212. https://doi.org/10.1016/j.trc.2018.02.017
   Web of Science ®Google Scholar
• Hosage, C. M., & Goodchild, M. F. (1986). Discrete space location–allocation solutions from genetic algorithms. Annals of Operations Research, 6(2), 35–46. https://doi.org/10.1007/BF02027381
   Google Scholar
• Hosseini-Motlagh, S. M., Samani, M. R. G., & Homaei, S. (2020). Blood supply chain management: Robust optimization, disruption risk, and blood group compatibility (a real-life case). Journal of Ambient Intelligence and Humanized Computing, 11(3), 1085–1104. https://doi.org/10.1007/s12652-019-01315-0
   Web of Science ®Google Scholar
• Jabbarzadeh, A., Haughton, M., & Khosrojerdi, A. (2018). Closed-loop supply chain network design under disruption risks: A robust approach with real world application. Computers & Industrial Engineering, 116, 178–191. https://doi.org/10.1016/j.cie.2017.12.025
   Web of Science ®Google Scholar
• Klibi, W., & Martel, A. (2012). Modeling approaches for the design of resilient supply networks under disruptions. International Journal of Production Economics, 135(2), 882–898. https://doi.org/10.1016/j.ijpe.2011.10.028
   Web of Science ®Google Scholar
• Kouvelis, P., Kurawarwala, A. A., & Gutierrez, G. J. (1992). Algorithms for robust single and multiple period layout planning for manufacturing systems. European Journal of Operational Research, 63(2), 287–303. https://doi.org/10.1016/0377-2217(92)90032-5
   Web of Science ®Google Scholar
• Kuby, M., & Lim, S. (2005). The flow-refueling location problem for alternative-fuel vehicles. Socio-Economic Planning Sciences, 39(2), 125–145. https://doi.org/10.1016/j.seps.2004.03.001
   Google Scholar
• Kuby, M., Lines, L., Schultz, R., Xie, Z., Kim, J. G., & Lim, S. (2009). Optimization of hydrogen stations in Florida using the flow-refueling location model. International Journal of Hydrogen Energy, 34(15), 6045–6064. https://doi.org/10.1016/j.ijhydene.2009.05.050
   Web of Science ®Google Scholar
• Kung, E., Seaton, S. E., Ramnarayan, P., & Pagel, C. (2022). Using a genetic algorithm to solve a non-linear location allocation problem for specialised children’s ambulances in England and Wales. Health Systems, 11(3), 161–171. https://doi.org/10.1080/20476965.2021.1908176
   Web of Science ®Google Scholar
• Li, X., & Ouyang, Y. (2010). A continuum approximation approach to reliable facility location design under correlated probabilistic disruptions. Transportation Research Part B: Methodological, 44(4), 535–548. https://doi.org/10.1016/j.trb.2009.09.004
   Web of Science ®Google Scholar
• Li, X., & Yeh, A. G. O. (2005). Integration of genetic algorithms and GIS for optimal location search. International Journal of Geographical Information Science, 19(5), 581–601. https://doi.org/10.1080/13658810500032388
   Web of Science ®Google Scholar
• Lim, M., Daskin, M. S., Bassamboo, A., & Chopra, S. (2010). A facility reliability problem: Formulation, properties, and algorithm. Naval Research Logistics (NRL), 57(1), 58–70. https://doi.org/10.1002/nav.20385
   Web of Science ®Google Scholar
• Manopiniwes, W., & Irohara, T. (2017). Stochastic optimisation model for integrated decisions on relief supply chains: Preparedness for disaster response. International Journal of Production Research, 55(4), 979–996. https://doi.org/10.1080/00207543.2016.1211340
   Web of Science ®Google Scholar
• Mathew, N., Smith, S. L., & Waslander, S. L. (2015). Planning paths for package delivery in heterogeneous multirobot teams. IEEE Transactions on Automation Science and Engineering, 12(4), 1298–1308. https://doi.org/10.1109/TASE.2015.2461213
   Web of Science ®Google Scholar
• Meepetchdee, Y., & Shah, N. (2007). Logistical network design with robustness and complexity considerations. International Journal of Physical Distribution & Logistics Management, 37(3), 201–222.
   Google Scholar
• MirHassani, S. A., & Ebrazi, R. (2013). A flexible reformulation of the refueling station location problem. Transportation Science, 47(4), 617–628. https://doi.org/10.1287/trsc.1120.0430
   Web of Science ®Google Scholar
• Mohammadi, R., Ghomi, S. F., & Jolai, F. (2016). Prepositioning emergency earthquake response supplies: A new multi-objective particle swarm optimization algorithm. Applied Mathematical Modelling, 40(9-10), 5183–5199. https://doi.org/10.1016/j.apm.2015.10.022
   Web of Science ®Google Scholar
• 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
   Web of Science ®Google Scholar
• Pan, F., & Nagi, R. (2010). Robust supply chain design under uncertain demand in agile manufacturing. Computers & Operations Research, 37(4), 668–683. https://doi.org/10.1016/j.cor.2009.06.017
   Web of Science ®Google Scholar
• Pradhananga, R., Mutlu, F., Pokharel, S., Holguín-Veras, J., & Seth, D. (2016). An integrated resource allocation and distribution model for pre-disaster planning. Computers & Industrial Engineering, 91, 229–238. https://doi.org/10.1016/j.cie.2015.11.010
   Web of Science ®Google Scholar
• Qi, L., Shen, Z. J. M., & Snyder, L. V. (2010). The effect of supply disruptions on supply chain design decisions. Transportation Science, 44(2), 274–289. https://doi.org/10.1287/trsc.1100.0320
   Web of Science ®Google Scholar
• Rezapour, S., Farahani, R. Z., & Pourakbar, M. (2017). Resilient supply chain network design under competition: A case study. European Journal of Operational Research, 259(3), 1017–1035. https://doi.org/10.1016/j.ejor.2016.11.041
   Web of Science ®Google Scholar
• Riemann, R., Wang, D. Z., & Busch, F. (2015). Optimal location of wireless charging facilities for electric vehicles: Flow-capturing location model with stochastic user equilibrium. Transportation Research Part C: Emerging Technologies, 58, 1–12. https://doi.org/10.1016/j.trc.2015.06.022
   Web of Science ®Google Scholar
• Samani, M. R. G., & Hosseini-Motlagh, S. M. (2019). An enhanced procedure for managing blood supply chain under disruptions and uncertainties. Annals of Operations Research, 283(1), 1413–1462. https://doi.org/10.1007/s10479-018-2873-4
   Web of Science ®Google Scholar
• Samani, M. R. G., & Hosseini-Motlagh, S. M. (2021). A novel capacity sharing mechanism to collaborative activities in the blood collection process during the COVID-19 outbreak. Applied Soft Computing, 112, 107821. https://doi.org/10.1016/j.asoc.2021.107821
   PubMed Web of Science ®Google Scholar
• Sandbrook, C. (2015). The social implications of using drones for biodiversity conservation. Ambio, 44(4), 636–647. https://doi.org/10.1007/s13280-015-0714-0
   PubMed Web of Science ®Google Scholar
• Scott, J., & Scott, C. (2017). Drone delivery models for healthcare.
   Google Scholar
• Shen, Z. J. M., Zhan, R. L., & Zhang, J. (2011). The reliable facility location problem: Formulations, heuristics, and approximation algorithms. INFORMS Journal on Computing, 23(3), 470–482. https://doi.org/10.1287/ijoc.1100.0414
   Web of Science ®Google Scholar
• Sivanandam, S. N., & Deepa, S. N. (2008). Genetic algorithms. In Introduction to genetic algorithms (pp. 15–37). Springer. https://link.springer.com/chapter/10.1007/978-3-540-73190-0_2
   Google Scholar
• Snyder, L. V. (2003). Supply chain robustness and* reliability: Models and algorithms. Northwestern University.
   Google Scholar
• Snyder, L. V., & Daskin, M. S. (2005). Reliability models for facility location: The expected failure cost case. Transportation Science, 39(3), 400–416. https://doi.org/10.1287/trsc.1040.0107
   Web of Science ®Google Scholar
• Snyder, L. V., & Daskin, M. S. (2006). Stochastic p-robust location problems. IIE Transactions, 38(11), 971–985. https://doi.org/10.1080/07408170500469113
   Web of Science ®Google Scholar
• Sundar, K., & Rathinam, S. (2014). Algorithms for routing an unmanned aerial vehicle in the presence of refueling depots. IEEE Transactions on Automation Science and Engineering, 11(1), 287–294. https://doi.org/10.1109/TASE.2013.2279544
   Web of Science ®Google Scholar
• Tolooie, A., Maity, M., & Sinha, A. K. (2020). A two-stage stochastic mixed-integer program for reliable supply chain network design under uncertain disruptions and demand. Computers & Industrial Engineering, 148, 106722. https://doi.org/10.1016/j.cie.2020.106722
   Web of Science ®Google Scholar
• Upchurch, C., Kuby, M., & Lim, S. (2009). A model for location of capacitated alternative-fuel stations. Geographical Analysis, 41(1), 85–106. https://doi.org/10.1111/j.1538-4632.2009.00744.x
   Web of Science ®Google Scholar
• Vali-Siar, M. M., & Roghanian, E. (2022). Sustainable, resilient and responsive mixed supply chain network design under hybrid uncertainty with considering COVID-19 pandemic disruption. Sustainable Production and Consumption, 30, 278–300. https://doi.org/10.1016/j.spc.2021.12.003
   PubMed Web of Science ®Google Scholar
• Wang, Y. W., & Lin, C. C. (2009). Locating road-vehicle refueling stations. Transportation Research Part E: Logistics and Transportation Review, 45(5), 821–829. https://doi.org/10.1016/j.tre.2009.03.002
   Web of Science ®Google Scholar
• Yu, K., Budhiraja, A. K., & Tokekar, P. (2018, May 21-26). Algorithms for routing of unmanned aerial vehicles with mobile recharging stations. 2018 IEEE international conference on robotics and automation (ICRA) (pp. 5720–5725). IEEE.
   Google Scholar
• Zokaee, S., Jabbarzadeh, A., Fahimnia, B., & Sadjadi, S. J. (2017). Robust supply chain network design: An optimization model with real world application. Annals of Operations Research, 257(1), 15–44. https://doi.org/10.1007/s10479-014-1756-6
   Web of Science ®Google Scholar