Benders’ decomposition (BD) is a framework for solving optimization problems by removing some variables and modeling their contribution to the original problem via so-called Benders cuts. While many advanced optimization techniques can be applied in a BD framework, one central technique has not been applied systematically in BD: symmetry handling. The main reason for this … Read more
We study linear chance-constrained problems where the coefficients follow a Gaussian mixture distribution. We provide mixed-binary quadratic programs that give inner and outer approximations of the chance constraint based on piecewise linear approximations of the standard normal cumulative density function. We show that $O\left(\sqrt{\ln(1/\tau)/\tau} \right)$ pieces are sufficient to attain $\tau$-accuracy in the chance constraint. … Read more
This survey proposes a unifying conceptual framework and taxonomy that systematically integrates Machine Learning (ML) and Reinforcement Learning (RL) with classical paradigms for Constraint Satisfaction and Boolean Satisfiability solving. Unlike prior reviews that focus on individual applications, we organize the literature around solver architecture, linking each major phase—constraint propagation, heuristic decision-making, conflict analysis, and meta-level … Read more
Combinatorial decision problems lie at the intersection of Operations Research (OR) and Artificial Intelligence (AI), encompassing structured optimization tasks such as submodular selection, dynamic programming, planning, and scheduling. These problems exhibit exponential growth in decision complexity, driven by interdependent choices coupled through logical, temporal, and resource constraints. Classical optimization frameworks—including integer programming, submodular optimization, and … Read more
This paper proposes a mixed-integer nonlinear programming approach for joint scheduling of long-term maintenance decisions and short-term production for groups of complex machines with multiple interacting components. We introduce an abstract model where the production and the condition of machines are described by convex functions, allowing the model to be employed for various application areas … Read more
We introduce a two-stage robust optimization (RO) framework for the integrated planning of a drone-based disaster relief operations problem (DDROP). Given sets of demand points, candidate locations for establishing drone-supported relief facilities, facility types, drone types, and relief items types, our first-stage problem solves the following problems simultaneously: (i) a location problem that determines the … Read more
In the aftermath of an earthquake, one of the most critical needs is medical care for the injured. A large number of individuals require immediate attention, often overwhelming the available healthcare resources. The sudden surge in demand, coupled with limited resources, route congestion and infrastructure damage, makes immediate medical care provision a significant challenge. This … Read more
The rapid growth of online meal delivery has introduced complex logistical challenges, where platforms must dynamically assign orders to couriers while accounting for demand uncertainty, courier autonomy, and service efficiency. Traditional dispatching methods, often focused on short-term cost minimization, fail to capture the long-term implications of assignment decisions on system-wide performance. This paper presents a … Read more
For mixed-integer programs (MIPs), strong branching is a highly effective variable selection method to reduce the number of nodes in the branch-and-bound algorithm. Extending it to nonlinear problems is conceptually simple but practically limited. Branching on a binary variable fixes the variable to 0 or 1, whereas branching on a continuous variable requires an additional … Read more
This study explores the integration of market structure learning into pricing strategies to maximize revenue in e-commerce and retail environments. We consider the problem of determining the revenue maximizing price of a single product in a market of heterogeneous consumers segmented by their product valuations; and analyze the pricing strategies for varying levels of prior … Read more