This paper introduces a sim-exact methodology for stochastic combinatorial optimization problems. The approach combines exact optimization models with Monte Carlo or discrete-event simulation to evaluate candidate solutions under uncertainty. The method iteratively adjusts a control parameter based on simulation feedback and solves a sequence of deterministic optimization problems. Unlike scenario-based stochastic programming, the approach does not rely on explicit scenario enumeration, and unlike simheuristics, it preserves optimality with respect to each deterministic subproblem. The methodology is tested on the vehicle routing problem with stochastic demands under different levels of demand variability. Results are compared with a simheuristic approach and a sample average approximation (SAA) method. The results show that sim-exact performance is comparable to simheuristics, with no statistically significant differences in most cases, while SAA shows weaker performance under medium and high variability.

This paper addresses a dynamic multi-period pricing problem that incorporates time-varying contextual information and inventory constraints. Sales are modeled as a function of both price and a multidimensional context vector, which may include factors such as customer location, income, loyalty, competitor prices, and promotional activity. This formulation captures complex market dynamics over a finite selling horizon. The problem is formulated as a quadratic programming model, and two alternative solution approaches are proposed. The first uses a multivariate regression model to approximate the sales function linearly, allowing an exact quadratic programming solution that serves as a benchmark. The second is a ‘learnheuristic’ algorithm that combines a nonlinear sales learning model with metaheuristic optimization to generate high-quality pricing strategies under realistic operational constraints. Computational experiments demonstrate the effectiveness of the proposed learnheuristic approach.

Rich and real-world variants of the well-known vehicle routing problem (VRP) involve customer demands that are uncertain and influenced by contextual factors. Classical metaheuristics rely on deterministic demand estimates, while simheuristics incorporate stochasticity through simulation but generally assume fixed demand distributions. This paper proposes a novel sim-learnheuristic framework for the VRP with context-dependent stochastic demands. The proposed sim-learnheuristics integrates a biased-randomized heuristic, Monte Carlo simulation, and a machine learning model that dynamically selects the probability distribution governing each customer’s demand according to the current operational context. Combining predictive analytics with simulation–optimization allows the proposed methodology to capture both stochastic variability and context-dependent demand behavior during the search process. The framework is evaluated through an extensive computational study using benchmark instances. Its performance is compared with that of a deterministic metaheuristic and a classical simheuristic in terms of expected routing cost, route failures, safety stock requirements, and computational robustness. Results show that context-aware demand modeling improves the representation of uncertainty and produces solutions with lower expected operational costs than traditional approaches.

This paper addresses the Dynamic Multi-Period Team Orienteering Problem for autonomous drones. We model the reward at each node as a function of factors affecting customer satisfaction with logistics service quality, such as on-time service commitment, and visit time, capturing temporal preferences and service degradation effects commonly observed in real-world applications. To solve this problem, we propose a two-stage approach. First, we employ machine learning techniques to linearly approximate the reward function, enabling an exact mixed-integer linear programming formulation that serves as a computational benchmark. As an alternative, we introduce a novel learnheuristic algorithm that integrates nonlinear reward learning with heuristic optimization, efficiently producing high-quality drone routing plans under energy constraints. Computational experiments illustrate the robustness and effectiveness of the proposed approach in generating mission strategies under dynamic environmental conditions.