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Our proposed solution approach promotes fair and equitable distribution of resources across all beneficiaries by requiring that the expected fill rate for each customer meets a predefined threshold. A solution to the FSVRP-PD constitutes a set of routes with a minimal total routing cost, where the expected minimum fill rates are met for every customer. Finding such a solution requires solving two interdependent subproblems: route planning and sequential resource allocation. To this extent, we develop an exact branch-price-and-cut algorithm capable of solving instances with up to 75 customers. Resource allocation follows Rawlsian fairness criteria that maximize the minimum service level across all customers in a route. To enhance the performance of the algorithms, particularly in pricing problems, we propose several problem-specific bounding techniques. Through numerical experiments, we demonstrate that our approach outperforms traditional routing and resource allocation policies by yielding superior cost and service equity outcomes.<\/jats:p>\n                  <jats:p>Funding: This work was funded by the Dutch Research Council (NWO) DAta-dRiven E-Commerce Order FULfillment (DAREFUL) Project [Grant 629.002.211].<\/jats:p>\n                  <jats:p>Supplemental Material: The online appendix is available at https:\/\/doi.org\/10.1287\/trsc.2024.0556 .<\/jats:p>","DOI":"10.1287\/trsc.2024.0556","type":"journal-article","created":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T16:52:37Z","timestamp":1765990357000},"page":"264-283","source":"Crossref","is-referenced-by-count":1,"title":["Fair Stochastic Vehicle Routing with Partial 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