Merve Bodur

Optimizing Flexibility and Commitments: Partially-Adaptive Multi-Stage Matrix Assembly Layout Design Problem

Categories Stochastic Programming

Matrix assembly layouts provide manufacturing flexibility under product variety and demand volatility through locally reconfigurable production stations. Periodic redesign exploits this flexibility, yet existing models allow only annual revisions. Finer calendars expose a flexibility-commitment trade-off: advance layout commitments support coordination, supplier agreements, and discounts. We introduce the layout revision timing problem, jointly choosing revision points … Read more

Incorporating Service Reliability in Multi-depot Vehicle Scheduling: A Chance-Constrained Approach

Categories Cutting Plane Approaches, Stochastic Programming, Transportation Tags , , ,

The multi-depot vehicle scheduling problem (MDVSP) is a critical planning challenge for transit agencies. We introduce a novel approach to MDVSP by incorporating service reliability through chance-constrained programming (CCP), targeting the pivotal issue of travel time uncertainty and its impact on transit service quality. Our model guarantees service reliability measured by on-time performance (OTP), a … Read more

Neural Approximate Dynamic Programming for the Ultra-fast Order Dispatching Problem

Categories Applications - OR and Management Sciences, Dynamic Programming, Transportation Tags , , ,

Same-Day Delivery (SDD) services aim to maximize the fulfillment of online orders while minimizing delivery delays but are beset by operational uncertainties such as those in order volumes and courier planning. Our work aims to enhance the operational efficiency of SDD by focusing on the ultra-fast Order Dispatching Problem (ODP), which involves matching and dispatching … Read more

Data-driven distributionally robust optimization: Intersecting ambiguity sets, performance analysis and tractability

Categories Robust Optimization Tags , , ,

We consider stochastic programs in which the probability distribution of uncertain parameters is unknown and partial information about it can only be captured from limited data. We use distributionally robust optimization (DRO) to model such problems. As opposed to the commonly used approach for DRO problems that suggests creating an ambiguity set by following a specific … Read more

Two-stage and Lagrangian Dual Decision Rules for Multistage Adaptive Robust Optimization

Categories Robust Optimization, Stochastic Programming Tags , ,

In this work, we design primal and dual bounding methods for multistage adaptive robust optimization (MSARO) problems motivated by two decision rules rooted in the stochastic programming literature. From the primal perspective, this is achieved by applying decision rules that restrict the functional forms of only a certain subset of decision variables resulting in an … Read more

Stochastic Dynamic Lot-sizing with Supplier-Driven Substitution and Service Level Constraints

Categories Production and Logistics, Stochastic Programming Tags , , ,

We consider a multi-stage stochastic lot-sizing problem with service level constraints and supplier-driven product substitution. A firm has multiple products and it has the option to meet demand from substitutable products at a cost. Considering the uncertainty in future demands, the firm wishes to make ordering decisions in every period such that the probability that … Read more

Tightening Quadratic Convex Relaxations for the Alternating Current Optimal Transmission Switching Problem

Categories (Mixed) Integer Nonlinear Programming, Convex Optimization, Nonlinear Optimization Tags , , , ,

The Alternating Current Optimal Transmission Switching (ACOTS) problem incorporates line switching decisions into the AC Optimal Power Flow (ACOPF) framework, offering well-known benefits in reducing operational costs and enhancing system reliability. ACOTS optimization models contain discrete variables and nonlinear, non-convex constraints, which make it difficult to solve. In this work, we develop strengthened quadratic convex … Read more

Leveraging Decision Diagrams to Solve Two-stage Stochastic Programs with Binary Recourse and Logical Linking Constraints

Categories Stochastic Programming Tags , , ,

Two-stage stochastic programs with binary recourse are challenging to solve and efficient solution methods for such problems have been limited. In this work, we generalize an existing binary decision diagram-based (BDD-based) approach of Lozano and Smith (Math. Program., 2018) to solve a special class of two-stage stochastic programs with binary recourse. In this setting, the … Read more

Markov Chain-based Policies for Multi-stage Stochastic Integer Linear Programming with an Application to Disaster Relief Logistics

Categories (Mixed) Integer Linear Programming, Stochastic Programming Tags , , , ,

We introduce an aggregation framework to address multi-stage stochastic programs with mixed-integer state variables and continuous local variables (MSILPs). Our aggregation framework imposes additional structure to the integer state variables by leveraging the information of the underlying stochastic process, which is modeled as a Markov chain (MC). We demonstrate that the aggregated MSILP can be … Read more

Neur2SP: Neural Two-stage Stochastic Programming

Categories Stochastic Programming Tags , ,

Stochastic programming is a powerful modeling framework for decision-making under uncertainty. In this work, we tackle two-stage stochastic programs (2SPs), the most widely applied and studied class of stochastic programming models. Solving 2SPs exactly requires evaluation of an expected value function that is computationally intractable. Additionally, having a mixed-integer linear program (MIP) or a nonlinear … Read more