A Branch-and-Benders Cut Algorithm for a Stochastic Service Network Design with Crowdsourced Capacity

References

• Alnaggar A, Gzara F, Bookbinder JH (2021) Crowdsourced delivery: A review of platforms and academic literature. Omega 98:102139.Crossref, Google Scholar
• Angulo G, Ahmed S, Dey SS (2016) Improving the integer L-shaped method. INFORMS J. Comput. 28(3):483–499.Link, 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
• Arslan AM, Agatz N, Kroon L, Zuidwijk R (2019) Crowdsourced delivery—A dynamic pickup and delivery problem with ad hoc drivers. Transportation Sci. 53(1):222–235.Link, Google Scholar
• Bai R, Wallace SW, Li J, Chong AYL (2014) Stochastic service network design with rerouting. Transportation Res. Part B: Methodological 60:50–65.Crossref, Google Scholar
• Basciftci B, Ahmed S, Gebraeel N (2024) Adaptive two-stage stochastic programming with an analysis on capacity expansion planning problem. Manufacturing Service Oper. Management 26(6):2121–2141.Google Scholar
• Behrendt A, Savelsbergh M, Wang H (2024) Task assignment, pricing, and capacity planning for a hybrid fleet of centralized and decentralized couriers. Transportation Res. Part C: Emerging Tech. 160:104533. Google Scholar
• Benders J (1962) Partitioning procedures for solving mixed-variables programming problems. Numerical Math. 4:238–252.Crossref, Google Scholar
• Birge JR, Louveaux F (2011) Introduction to Stochastic Programming (Springer Science & Business Media, Boston).Crossref, Google Scholar
• Bodur M, Dash S, Günlük O, Luedtke J (2017) Strengthened benders cuts for stochastic integer programs with continuous recourse. INFORMS J. Comput. 29(1):77–91.Link, Google Scholar
• Cerisola S, Baíllo Á, Fernández-López JM, Ramos A, Gollmer R (2009) Stochastic power generation unit commitment in electricity markets: A novel formulation and a comparison of solution methods. Oper. Res. 57(1):32–46.Link, Google Scholar
• Crainic TG (2000) Service network design in freight transportation. Eur. J. Oper. Res. 122(2):272–288.Crossref, Google Scholar
• Crainic TG, Gendreau M, Gendron B (2021) Network Design with Applications to Transportation and Logistics (Springer, Cham, Switzerland).Google Scholar
• Crainic TG, Hewitt M, Maggioni F, Rei W (2021) Partial benders decomposition: General methodology and application to stochastic network design. Transportation Sci. 55(2):414–435.Google Scholar
• Crainic TG, Hewitt M, Toulouse M, Vu DM (2018) Scheduled service network design with resource acquisition and management. EURO J. Transportation Logist. 7(3):277–309.Crossref, Google Scholar
• Dahle L, Andersson H, Christiansen M, Speranza MG (2019) The pickup and delivery problem with time windows and occasional drivers. Comput. Oper. Res. 109:122–133.Crossref, Google Scholar
• Dayarian I, Pazour J (2022) Crowdsourced order-fulfillment policies using in-store customers. Production Oper. Management 31(11):4075–4094.Crossref, 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
• Dayarian I, Rocco A, Erera A, Savelsbergh M (2022) Operations design for high-velocity intra-city package service. Transportation Res. Part B: Methodological 161:150–168.Crossref, Google Scholar
• Demir E, Burgholzer W, Hrušovskỳ M, Arıkan E, Jammernegg W, Van Woensel T (2016) A green intermodal service network design problem with travel time uncertainty. Transportation Res. Part B: Methodological 93:789–807.Crossref, Google Scholar
• Fontaine P, Crainic TG, Jabali O, Rei W (2021) Scheduled service network design with resource management for two-tier multimodal city logistics. Eur. J. Oper. Res. 294(2):558–570.Crossref, Google Scholar
• Geoffrion AM (1970a) Elements of large-scale mathematical programming part I: Concepts. Management Sci. 16(11):652–675.Link, Google Scholar
• Geoffrion AM (1970b) Elements of large-scale mathematical programming part II: Synthesis of algorithms and bibliography. Management Sci. 16(11):676–691.Link, Google Scholar
• Guastaroba G, Speranza MG, Vigo D (2016) Intermediate facilities in freight transportation planning: A survey. Transportation Sci. 50(3):763–789.Link, Google Scholar
• He E, Boland N, Nemhauser G, Savelsbergh M (2022) An exact algorithm for the service network design problem with hub capacity constraints. Networks 80(4):572–596.Crossref, Google Scholar
• Hewitt M (2022) The flexible scheduled service network design problem. Transportation Sci. 56(4):1000–1021.Google Scholar
• Hewitt M, Crainic TG, Nowak M, Rei W (2019) Scheduled service network design with resource acquisition and management under uncertainty. Transportation Res. Part B: Methodological 128:324–343.Crossref, Google Scholar
• Jiang X, Bai R, Wallace SW, Kendall G, Landa-Silva D (2021) Soft clustering-based scenario bundling for a progressive hedging heuristic in stochastic service network design. Comput. Oper. Res. 128:105182.Crossref, Google Scholar
• Kim MJ (2019) Benefits and concerns of the sharing economy: Economic analysis and policy implications. KDI J. Econom. Policy 41(1):15–41.Google Scholar
• Kızıl KU, Yıldız B (2023) Public transport-based crowd-shipping with backup transfers. Transportation Sci. 57(1):164–186.Link, Google Scholar
• Klibi W, Martel A, Guitouni A (2010) The design of robust value-creating supply chain networks: A critical review. Eur. J. Oper. Res. 203(2):283–293.Crossref, Google Scholar
• Lanza G, Crainic TG, Rei W, Ricciardi N (2021) Scheduled service network design with quality targets and stochastic travel times. Eur. J. Oper. Res. 288(1):30–46.Crossref, Google Scholar
• Laporte G, Louveaux FV (1993) The integer L-shaped method for stochastic integer programs with complete recourse. Oper. Res. Lett. 13(3):133–142.Crossref, Google Scholar
• Liu C, Lin S, Shen ZJM, Zhang J (2023) Stochastic service network design: The value of fixed routes. Transportation Res. Part E: Logist. Transportation Rev. 174:103118.Crossref, Google Scholar
• Lium AG, Crainic TG, Wallace SW (2007) Correlations in stochastic programming: A case from stochastic service network design. Asia. Pacific J. Oper. Res. 24(02):161–179.Crossref, Google Scholar
• Lium A, Crainic TG, Wallace SW (2009) A study of demand stochasticity in service network design. Transportation Sci. 43:144–157.Link, Google Scholar
• Macrina G, Pugliese LDP, Guerriero F, Laganà D (2017) The vehicle routing problem with occasional drivers and time windows. Sforza A, Sterle C, eds. International Conference on Optimization and Decision Science (Springer, Cham, Switzerland), 577–587.Google Scholar
• Macrina G, Pugliese LDP, Guerriero F, Laporte G (2020) Crowd-shipping with time windows and transshipment nodes. Comput. Oper. Res. 113:104806.Crossref, Google Scholar
• McDaniel D, Devine M (1977) A modified benders’ partitioning algorithm for mixed integer programming. Management Sci. 24(3):312–319.Link, Google Scholar
• Mousavi K, Bodur M, Roorda MJ (2022) Stochastic last-mile delivery with crowd-shipping and mobile depots. Transportation Sci. 56(3):612–630.Link, Google Scholar
• Nieto-Isaza S, Fontaine P, Minner S (2022) The value of stochastic crowd resources and strategic location of mini-depots for last-mile delivery: A Benders decomposition approach. Transportation Res. Part B: Methodological 157:62–79.Crossref, Google Scholar
• Punel A, Ermagun A, Stathopoulos A (2018) Studying determinants of crowd-shipping use. Travel Behav. Soc. 12:30–40.Crossref, Google Scholar
• Puschmann T, Alt R (2016) Sharing economy. Bus. Inform. Systems Engrg. 58(1):93–99.Crossref, Google Scholar
• Rahmaniani R, Crainic TG, Gendreau M, Rei W (2017) The Benders decomposition algorithm: A literature review. Eur. J. Oper. Res. 259(3):801–817.Crossref, Google Scholar
• Rahmaniani R, Crainic TG, Gendreau M, Rei W (2018) Accelerating the Benders decomposition method: Application to stochastic network design problems. SIAM J. Optim. 28(1):875–903.Crossref, Google Scholar
• Rahmaniani R, Ahmed S, Crainic TG, Gendreau M, Rei W (2020) The Benders dual decomposition method. Oper. Res. 68(3):878–895.Link, Google Scholar
• Rajagopal A (2021) Crowd-Based Business Models—Using Collective Intelligence for Market Competitiveness (Palgrave Macmillan, Cham, Switzerland).Crossref, Google Scholar
• Satici O, Dayarian I (2024) Tactical and operational planning of express intra-city package services. Omega 122:102940.Crossref, Google Scholar
• Schor J, (2016) Debating the sharing economy. J. Self-Governance Management Econom. 4(3):7–22. Crossref, 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
• U.S. Census (2020) 2020 census urban areas facts. Accessed June 1, 2023, https://www.census.gov/programs-surveys/geography/guidance/geo-areas/urban-rural/2020-ua-facts.html. Google Scholar
• Van Slyke RM, Wets R (1969) L-shaped linear programs with applications to optimal control and stochastic programming. SIAM J. Appl. Math. 17(4):638–663.Crossref, Google Scholar
• Wang Z, Qi M (2020) Robust service network design under demand uncertainty. Transportation Sci. 54(3):676–689.Link, Google Scholar
• Wang X, Crainic TG, Wallace SW (2019) Stochastic network design for planning scheduled transportation services: The value of deterministic solutions. INFORMS J. Comput. 31(1):153–170.Link, Google Scholar
• Wieberneit N (2008) Service network design for freight transportation: A review. OR Spectrum 30(1):77–112.Crossref, Google Scholar
• Wu H, Herszterg I, Savelsbergh M, Huang Y (2023) Service network design for same-day delivery with hub capacity constraints. Transportation Sci. 57(1):273–287.Link, Google Scholar
• Zou J, Ahmed S, Sun XA (2019) Stochastic dual dynamic integer programming. Math. Programming 175:461–502.Crossref, Google Scholar