Robust Workforce Management with Crowdsourced Delivery

References

• Alnaggar A, Gzara F, Bookbinder JH (2021) Crowdsourced delivery: A review of platforms and academic literature. Omega 98:102139.Crossref, Google Scholar
• Archetti C, Savelsbergh M, Speranza MG (2016) The vehicle routing problem with occasional drivers. Eur. J. Oper. Res. 254(2):472–480.Crossref, Google Scholar
• Behrendt A, Savelsbergh M, Wang H (2023) A prescriptive machine learning method for courier scheduling on crowdsourced delivery platforms. Transportation Sci. 57(4):889–907.Link, Google Scholar
• Ben-Tal A, Goryashko A, Guslitzer E, Nemirovski A (2004) Adjustable robust solutions of uncertain linear programs. Math. Program. 99(2):351–376.Crossref, Google Scholar
• Bertsimas D, Gupta V, Kallus N (2018) Data-driven robust optimization. Math. Program. 167:235–292.Crossref, Google Scholar
• Bertsimas D, Sim M, Zhang M (2019) Adaptive distributionally robust optimization. Management Sci. 65(2):604–618.Link, Google Scholar
• Birge JR, Louveaux F (2011) Introduction to Stochastic Programming (Springer Science & Business Media, New York).Crossref, Google Scholar
• Chen X, Li M (2021) Discrete convex analysis and its applications in operations: A survey. Production Oper. Management 30(6):1904–1926.Crossref, Google Scholar
• Chen W, Sim M (2009) Goal-driven optimization. Oper. Res. 57(2):342–357.Link, Google Scholar
• Chen LG, Tang Q (2022) Supply chain performance with target-oriented firms. Manufacturing Service Oper. Management 24(3):1714–1732.Link, Google Scholar
• Chen Z, Sim M, Xiong P (2020) Robust stochastic optimization made easy with RSOME. Management Sci. 66(8):3329–3339.Link, Google Scholar
• Chen H, Sun XA, Yang H (2023) Robust optimization with continuous decision-dependent uncertainty with applications to demand response management. SIAM J. Optim. 33(3):2406–2434.Crossref, Google Scholar
• Chen L, Sim M, Zhang X, Zhou M (2022) Robust explainable prescriptive analytics. Preprint, submitted May 11, https://dx.doi.org/10.2139/ssrn.4106222.Google Scholar
• Dayarian I, Savelsbergh M (2020) Crowdshipping and same-day delivery: Employing in-store customers to deliver online orders. Production Oper. Management 29(9):2153–2174.Crossref, Google Scholar
• Fournier N, Guillin A (2015) On the rate of convergence in Wasserstein distance of the empirical measure. Probab. Theory Related Fields 162(3):707–738.Crossref, Google Scholar
• Gao R, Kleywegt A (2023) Distributionally robust stochastic optimization with Wasserstein distance. Math. Oper. Res. 48(2):603–655.Link, Google Scholar
• Goh J, Hall NG (2013) Total cost control in project management via satisficing. Management Sci. 59(6):1354–1372.Link, Google Scholar
• Goyal A, Zhang Y, Benjaafar S (2023) Crowdsourcing last-mile delivery with hybrid fleets under uncertainties of demand and driver supply: Optimizing profitability and service level. Preprint, submitted January 11, https://ssrn.com/abstract=4322670.Google Scholar
• Liu F, Chen Z, Wang S (2023) Globalized distributionally robust counterpart. INFORMS J. Comput. 35(5):1120–1142.Link, Google Scholar
• Liu S, He L, Max Shen ZJ (2021) On-time last-mile delivery: Order assignment with travel-time predictors. Management Sci. 67(7):4095–4119.Link, Google Scholar
• Long Z, Sim M, Zhou M (2022) Robust satisficing. Oper. Res. 71(1):61–82.Link, Google Scholar
• Mao J (1970) Survey of capital budgeting: Theory and practice. J. Finance 25(2):349–360.Crossref, Google Scholar
• Mohajerin Esfahani P, Kuhn D (2018) Data-driven distributionally robust optimization using the Wasserstein metric: Performance guarantees and tractable reformulations. Math. Program. 171(1):115–166.Crossref, Google Scholar
• Murota K (1998) Discrete convex analysis. Math. Program. 83(1):313–371.Crossref, Google Scholar
• Nohadani O, Sharma K (2018) Optimization under decision-dependent uncertainty. SIAM J. Optim. 28(2):1773–1795.Crossref, Google Scholar
• Rechavi A, Toch E (2022) Crowd logistics: Understanding auction-based pricing and couriers’ strategies in crowdsourcing package delivery. J. Intelligent Transportation Systems 26(2):129–144.Crossref, Google Scholar
• Savelsbergh MW, Ulmer MW (2022) Challenges and opportunities in crowdsourced delivery planning and operations. 4OR 20(1):1–21.Crossref, Google Scholar
• Sim M, Tang Q, Zhou M, Zhu T (2024) The analytics of robust satisficing: Predict, optimize, satisfice, then fortify. Preprint, submitted May 13, https://dx.doi.org/10.2139/ssrn.3829562.Google Scholar
• Simon HA (1955) A behavioral model of rational choice. Quart. J. Econom. 68(1):99–118.Crossref, Google Scholar
• Smith JE, Winkler RL (2006) The optimizer’s curse: Skepticism and postdecision surprise in decision analysis. Management Sci. 52(3):311–322.Link, Google Scholar
• Solomon MM (1987) Algorithms for the vehicle routing and scheduling problems with time window constraints. Oper. Res. 35(2):254–265.Link, Google Scholar
• Torres F, Gendreau M, Rei W (2022) Vehicle routing with stochastic supply of crowd vehicles and time windows. Transportation Sci. 56(3):631–653.Link, Google Scholar
• Ulmer MW, Savelsbergh M (2020) Workforce scheduling in the era of crowdsourced delivery. Transportation Sci. 54(4):1113–1133.Link, Google Scholar
• Yildiz B, Savelsbergh M (2019) Service and capacity planning in crowd-sourced delivery. Transportation Res. Part C Emerging Tech. 100:177–199.Crossref, Google Scholar
• Zhang Y, Zhang Z, Lim A, Sim M (2021) Robust data-driven vehicle routing with time windows. Oper. Res. 69(2):469–485.Link, Google Scholar
• Zhou M, Sim M, Lam SW (2022) Advance admission scheduling via resource satisficing. Production Oper. Management 31(11):4002–4020.Crossref, Google Scholar