The proposed approach consistently maintains non-positive empirical SLA gaps and achieves up to $30\% higher resource utilization than a price-optimization baseline without adaptive reserve control.
Abstract
This paper studies a multi-tenant resource allocation problem in a satellite open radio access network (O-RAN) wholesale setting, where heterogeneous traffic classes share a time-varying limited satellite capacity over a finite planning horizon. We formulate a joint pricing and reserve allocation problem from a service provider perspective, where tenant-specific demand exhibits price elasticity and stochastic service requirements subject to strict service-level agreement (SLA) constraints, leading to a coupled economic and reliability-driven bottleneck. A deterministic reformulation is adopted to approximate probabilistic SLA requirements through tractable margin constraints, enabling coordinated control of horizon-wide contract prices and time-varying reserves. The resulting problem is non-convex due to interdependent decisions across tenants, time windows, and service classes. To address this, an alternating optimization (AO) scheme is developed separating pricing and allocation decisions while preserving feasibility and SLA guarantees. Numerical results show that the proposed method achieves near-optimal profit within approximately $1\%$ of a global benchmark, while reducing runtime by up to $22\times$. In contrast, considered baseline schemes incur profit losses exceeding $15\%$ or fail to satisfy SLA constraints. The proposed approach consistently maintains non-positive empirical SLA gaps and achieves up to $30\%$ higher resource utilization than a price-optimization baseline without adaptive reserve control. These results demonstrate that joint economic and resource control enables the provider to efficiently exploit scarce satellite network capacity with reliable service delivery and scalable computation.
This work investigates the uncertainty-aware joint pricing and matching problem for dynamic high-capacity ride-sharing services, where passengers are assumed to be price-elastic and decide whether to accept a ride-sharing offer based on the upfront prices provided by the platform. We formulate the studied problem as a two-stage stochastic program, where the first stage optimizes upfront price decisions for passengers, and the second-stage recourse problem captures passenger-vehicle assignment based on passengers'uncertain choices. To enhance computational efficiency, we introduce a novel relaxation-based gradient descent-guided search algorithm that leverages the problem's structural properties. Initially, the algorithm generates a feasible solution for the first-stage problem via relaxation. It then iteratively improves the solution via a search process guided by the derived gradient information. In particular, scenario reduction is applied to eliminate unnecessary scenarios when calculating the gradient, thereby reducing the overall computational burden. Numerical experiments demonstrate that, compared to solving the stochastic program directly, the proposed algorithm can accelerate computation speed by thousands of times while achieving optimality gaps of no more than 1.1%. Finally, we validate the benefits of considering passengers'choice uncertainty through large-scale simulation using real-world datasets and road networks over two large cities. The results demonstrate that, on average, the proposed method can increase the revenue by 5.2% and the service rate by 8.2% compared to the baseline approaches. This study provides a valuable reference for transportation network companies to design pricing strategies for ride-sharing to enhance service efficiency and improve revenue.
Wang Chen, Xinglu Liu, Kai-Hang Zhang et al.· 0 citations
Results show that considering both storage and multi-period fairness is an interesting approach for modern DOE design, which in turn requires a multi-period, co-designed approach.
Pedro Salomão Quessongo, Daniel Gebbran, C. Unsihuay-Vila· 0 citations
Urban passenger hubs, such as airports and railway stations, generate concentrated last-mile demand from arriving passengers to spatially dispersed urban destinations. Fluctuating passenger arrivals and changing vehicle availability can create a mismatch between accepted demand and available service capacity. This paper aims to coordinate zone-level pricing and vehicle dispatching, so that fare-responsive accepted demand can be better aligned with available vehicle resources, while balancing operator financial performance and service reliability. Under the zonal pricing scheme, the transit operator determines a quoted zone-level fare for each service zone at every decision epoch. Newly arriving service requests accept the service when the quoted fare does not exceed their maximum acceptable per-passenger fare, after which the fare is committed. A rolling-horizon optimization model jointly determines zone-level fares and dispatching plans as request states and vehicle states evolve over time. The fare discretization property reduces the continuous pricing decision to a finite candidate zone-level fare selection problem, and a customized Rolling-Horizon Adaptive Large Neighborhood Search (RH-ALNS) algorithm is developed to solve the resulting problem efficiently. Case studies based on Nanjingnan Railway Station in Nanjing, China, demonstrate the operational value of coordinating pricing and dispatching decisions. In the baseline case, the proposed method achieves a passenger service rate of 76.75%, an accepted-passenger fulfillment rate of 96.07%, and an operating surplus of 1.145 CNY per passenger-kilometer. Holding the RH-ALNS dispatching method fixed, dynamic zonal pricing increases the objective value by 4.39%, the operating surplus per passenger-kilometer by 5.46%, and accepted-passenger fulfillment by 3.63 percentage points relative to fixed zonal fares. The findings indicate that coordinating dynamic zonal pricing with vehicle dispatching can better align accepted demand with available vehicle resources and provide practical guidance for designing reliable, resource-efficient, and financially balanced hub-based demand-responsive last-mile transit services.
Rong Fu, Hao-Ran Huang, Jingxu Chen et al.· Sustainability· 0 citations
This paper investigates centralized scheduling of mobile service agents under staggered multi-wave task arrivals, limited service capacity, service-time windows, and cross-wave capacity reservation requirements. A spatiotemporal candidate-arc representation is developed to discretize the continuous scheduling process, where each arc encodes an agent-task-time triple along with predicted service points, approximate paths, endurance consumption, and execution costs. Based on the feasible arc set, a mixed-integer linear programming (MILP) model is formulated to jointly optimize task coverage, standby-agent activation, coordinated service times, spatiotemporal conflict avoidance, and cross-wave capacity reservation. Numerical experiments are conducted on a 3-wave, 37-task benchmark as well as extended scenarios with varying resource scarcity and task scales. Results show that in multi-wave rolling execution, the MILP strategy achieves a total weighted coverage rate of 71.19%, outperforming the nearest-neighbor greedy baseline by 5.56 percentage points; the advantage is most pronounced in the second-wave peak (+17.07%), directly validating the capacity reservation mechanism. Further sensitivity analysis reveals that the optimization gain of MILP is resource-sensitive—significant under abundant resources and gradually converging under extreme scarcity. The reported results demonstrate how global constrained selection balances current weighted coverage against retained service capacity for subsequent waves, and provide quantitative references for practical parameter tuning.
Miao Shen, Chuan-Fu Guo, Peng Wang et al.· 2026 12th International Conf...· 0 citations
Deploying heavy-duty electric trucks under real-world uncertainty is operationally challenging, particularly when multiple vehicles compete for limited public charging resources and face uncertain wait times. This research studies the Fixed-Route Vehicle Charging Problem and formulates it as a multistage stochastic program under charging congestion uncertainty. The delivery system is modeled as a discrete-event process triggered by physical route milestones, and charging congestion uncertainty is represented by a Markovian transition. To address the resulting mixed-integer structure, we apply stochastic dual dynamic programming (SDDP) as a tactical planning approach, while accommodating discrete vehicle dynamics within the convexity requirements. Computational experiments on a California logistics network showed that the proposed approach performed effectively across multiple geographically diverse delivery routes. Compared to a multistage stochastic integer programming benchmark, SDDP achieved a competitive solution quality while reducing training time as the number of route instances increased. Out-of-sample simulations further demonstrated that policies derived from SDDP remained robust under distributional shifts toward more congested scenarios. Overall, this study establishes SDDP as a tractable and scalable framework for generating high-quality operational policies for heavy-duty electric fleets under congestion uncertainty.
Ziyan Li, Nikolay Aristov, E. Dugundji· Transportation Research Reco...· 0 citations
Urban battery swapping station (BSS) planning is difficult because site opening, service-module allocation, user assignment, battery degradation pressure, travel burden, and congestion are tightly coupled. A plan that minimizes investment alone may create long queues, whereas a plan that only reduces waiting can overbuild costly and underused capacity. This study formulates the urban BSS siting–sizing problem as an operation-aware mixed-integer nonlinear model and evaluates feasible plans with payoff-table global-criterion normalization. To search this rugged planning space, we propose a Stable Portfolio Hyper-Heuristic (SPHH) that combines Greedy construction, BO-guided large-neighborhood search, simulated annealing, and optimized annealing with reheating, followed by feasible-incumbent preservation and non-worsening post-processing. The formal campaign contains 18 synthetic cases, 10 independent repeats, and five algorithms, yielding 900 optimization records. Across the 18 cases, SPHH produced the lowest mean GC score and reduced the mean GC value on average relative to the baseline algorithms. Nonparametric Friedman and Holm-adjusted Wilcoxon tests confirmed statistically significant differences among methods, although the advantage over the strongest baseline was not universal. These results indicate that SPHH is most useful as an offline planning selector that improves recommendation stability when additional computation is acceptable, rather than as a universally faster optimizer.
