Consolidation in Crowdshipping with Scheduled Transfer Lines: A Surrogate-Based Network Design Framework
References
- (2024) Consistent routing for local same-day delivery via micro-hubs. OR Spectrum 46(2):375–409.Crossref, Google Scholar
- (2025) Feed-forward neural networks as a mixed-integer program. Engineering with Computers. 41(4):2411–2429. Crossref, Google Scholar
- (2020) Managing computational complexity using surrogate models: A critical review. Res. Engrg. Design 31(3):275–298.Crossref, Google Scholar
- (2022) Adaptive clinical trial designs with surrogates: When should we bother? Management Sci. 68(3):1982–2002.Link, Google Scholar
- (2016) The vehicle routing problem with occasional drivers. Eur. J. Oper. Res. 254(2):472–480.Crossref, Google Scholar
- (2019) Crowdsourced delivery—A dynamic pickup and delivery problem with ad hoc drivers. Transportation Sci. 53(1):222–235.Link, Google Scholar
- (2006) Metamodel-based simulation optimization. Handbooks Oper. Res. Management Sci. 13:535–574.Google Scholar
- (2022) Electrifying last-mile delivery: A total cost of ownership comparison of battery-electric and diesel trucks in Europe. Working paper, International Council on Clean Transportation (ICCT), Washington, DC.Google Scholar
- (2021) Machine learning for combinatorial optimization: A methodological tour d’horizon. Eur. J. Oper. Res. 290(2):405–421.Crossref, Google Scholar
- (2024) Dynamic project expediting: A stochastic shortest-path approach. Management Sci. 70(6):3748–3768.Link, Google Scholar
- (2013) Surrogate modeling for the fast optimization of energy systems. Energy 57:653–662.Crossref, Google Scholar
- (2017) Crowd logistics: An opportunity for more sustainable urban freight transport? Eur. Transport Res. Rev. 9(3):39.Crossref, Google Scholar
- Capgemini (2023) Navigating the complex web of last-mile deliveries. Accessed August 2, 2025, https://www.capgemini.com/insights/expert-perspectives/navigating-the-complex-web-of-last-mile-deliveries/.Google Scholar
- (2024) Real-time spatial–intertemporal pricing and relocation in a ride-hailing network: Near-optimal policies and the value of dynamic pricing. Oper. Res. 72(5):2097–2118.Link, Google Scholar
- (2020) Crowdshipping and same-day delivery: Employing in-store customers to deliver online orders. Production Oper. Management 29(9):2153–2174.Crossref, Google Scholar
- (2018) Deep neural networks and mixed integer linear optimization. Constraints 23(3):296–309.Crossref, Google Scholar
- (2025) Optimizing last-mile delivery through crowdshipping on public transportation networks. Transportation Res. Part C Emerging Tech. 179:105250.Crossref, Google Scholar
- (2022) Introduction to Logistics Systems Management: With Microsoft Excel and Python Examples (John Wiley & Sons, Hoboken, NJ).Google Scholar
- (2019) ReLU networks as surrogate models in mixed-integer linear programs. Comput. Chemical Engrg. 131:106580.Crossref, Google Scholar
- (2021) Surrogate-based simulation optimization. Emerging Optim. Methods Model. Techniques Appl. 2021(October):287–311.Google Scholar
- (1989) Multilayer feedforward networks are universal approximators. Neural Networks 2(5):359–366.Crossref, Google Scholar
- (1997) Prediction intervals for artificial neural networks. J. Amer. Statist. Assoc. 92(438):748–757.Crossref, Google Scholar
- IMM Open Data Portal (2012) Istanbul transportation master plan household survey. Accessed August 8, 2023, https://data.ibb.gov.tr/en/dataset/istanbul-ulasim-ana-plani-hanehalki-arastirmasi.Google Scholar
- (2021) Designing multi-tier, multi-service-level, and multi-modal last-mile distribution networks for omni-channel operations. Eur. J. Oper. Res. 294(3):1059–1077.Crossref, Google Scholar
- (2023) Public transport-based crowd-shipping with backup transfers. Transportation Sci. 57(1):174–196.Link, Google Scholar
- (1993) Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks 6(6):861–867.Crossref, Google Scholar
- (2026) Joint optimization of train services and freight delivery in a metro-based underground logistics system. Transportation Res. Part B Methodological 204:103377.Crossref, Google Scholar
- Lider Kurye (2023) The right address for high-quality and reliable courier service. Accessed March 23, 2023, https://www.liderkurye.com.tr/.Google Scholar
- (2017) The vehicle routing problem with occasional drivers and time windows. Comput. Oper. Res. 217:577–587.Google Scholar
- (2020) Crowd-shipping with time windows and transshipment nodes. Comput. Oper. Res. 113:104806.Crossref, Google Scholar
- (2020) Optimization of tree ensembles. Oper. Res. 68(5):1605–1624.Link, Google Scholar
- (2023) Crowdshipping for sustainable urban logistics: A systematic review of the literature. Transportation Res. Part E Logist. Transportation Rev. 178:103289.Crossref, Google Scholar
- (2024) Public transportation-based crowd-shipping initiatives: Are users willing to participate? Why not? Transportation Res. Part A Policy Practice 182:104019.Crossref, Google Scholar
- (2017) Modeling the acceptability of crowdsourced goods deliveries: Role of context and experience effects. Transportation Res. Part E Logist. Transportation Rev. 105:18–38.Crossref, Google Scholar
- (2018) Crowd-shipping of small parcels in a physical internet. Working paper, Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel.Google Scholar
- (2023) Optimization of sparsity-constrained neural networks as a mixed integer linear program. J. Optim. Theory Appl. 199(3):931–954.Crossref, Google Scholar
- (2024) Crowdshipping problem with dynamic compensations and transshipments. Transportation Res. Part C Emerging Tech. 166:104796.Crossref, Google Scholar
- (2019) Deterministic global optimization with artificial neural networks embedded. J. Optim. Theory Appl. 180(3):925–948.Crossref, Google Scholar
- (2026) Service network design for a metro-based crowdsourced urban delivery system under demand and supply uncertainty. Transportation Res. Part C Emerging Tech. 188:105695.Crossref, Google Scholar
- (2026) Dynamic control of a make-to-order system under model uncertainty. Management Sci. 72(3):2228–2246.Link, Google Scholar
- (2021) Sobolev trained neural network surrogate models for optimization. Comput. Chemical Engrg. 153:107419.Crossref, Google Scholar
- (1987) Simulated annealing. Simulated Annealing: Theory and Applications, Mathematics and Its Applications, vol. 37 (Springer, Dordrecht, Netherlands), 7–15.Crossref, Google Scholar
- (2025) A comprehensive framework for residual analysis in regression and machine learning. J. Inform. Systems Engrg. Management 10(31s):1–18.Google Scholar
- (1998) Integer Programming (John Wiley & Sons, New York).Google Scholar
- (2022) Modeling design and control problems involving neural network surrogates. Comput. Optim. Appl. 83(3):759–800.Crossref, Google Scholar
- (2021) Express package routing problem with occasional couriers. Transportation Res. Part C Emerging Tech. 123:102994.Crossref, Google Scholar
- (2019a) Provably high-quality solutions for the meal delivery routing problem. Transportation Sci. 53(5):1372–1388.Link, Google Scholar
- (2019b) Service and capacity planning in crowd-sourced delivery. Transportation Res. Part C Emerging Tech. 100:177–199.Crossref, Google Scholar

