Comparison of IP and CNF Models for Control of Automated Valet Parking Systems

In automated valet parking system, a central computer controls a number of robots which have the capability to move in two directions, under cars, lift a car up, carry it to another parking slot, and drop it. We study the theoretical throughput limitations of these systems: Given a car park layout, an initial configuration of … Read more

Dynamic Data-Driven Estimation of Non-Parametric Choice Models

We study non-parametric estimation of choice models, which was introduced to alleviate unreasonable assumptions in traditional parametric models, and are prevalent in several application areas. Existing literature focuses only on the static observational setting where all of the observations are given upfront, and lacks algorithms that provide explicit convergence rate guarantees or an a priori … Read more

Gas Storage Valuation in Incomplete Markets

Natural gas storage valuation is an important problem in energy trading, yet most valuation approaches are based on heuristics or ignore that gas markets are incomplete. We propose an exact valuation model for incomplete gas markets based on multistage stochastic programming. Market incompleteness structurally changes the problem of storage valuation and asset backed trading and … Read more

Locality sensitive heuristics for solving the Data Mule Routing Problem

A usual way to collect data in a Wireless Sensor Network (WSN) is by the support of a special agent, called data mule, that moves between sensor nodes and performs all communication between them. In this work, the focus is on the construction of the route that the data mule must follow to serve all … Read more

Demand Modeling in the Presence of Unobserved Lost Sales

We present an integrated optimization approach to parameter estimation for discrete choice demand models where data for one or more choice alternatives are censored. We employ a mixed-integer program (MIP) to jointly determine the prediction parameters associated with the customer arrival rate and their substitutive choices. This integrated approach enables us to recover proven, (near-) … Read more

The Multiple Part Type Cyclic Flow Shop Robotic Cell Scheduling Problem: A Novel and Comprehensive Mixed Integer Linear Programming Approach

This paper considers the problem of cyclic ow shop robotic cell scheduling deploying several single and dual gripper robots. In this problem, dierent part types are successively processed on multiple machines with dierent pickup criteria including free pickup, pickup within time-windows and no-waiting times. The parts are transported between the machines by the robots. We … Read more

Multiechelon Lot Sizing: New Complexities and Inequalities

We study a multiechelon supply chain model that consists of a production level and several transportation levels, where the demands can exist in the production echelon as well as any transportation echelons. With the presence of stationary production capacity and general cost functions, our model integrates production, inventory and transportation decisions and generalizes existing literature … Read more

Capacitated ring arborescence problems with profits

In this work we introduce profit-oriented capacitated ring arborescence problems and present exact and heuristic algorithms. These combinatorial network design problems ask for optimized bi-level networks taking into account arc costs and node profits. Solutions combine circuits on the inner level with arborescences on the outer level of the networks. We consider the prize-collecting, the … Read more

Risk-based Loan Pricing: Portfolio Optimization Approach With Marginal Risk Contribution

We consider a lender (bank) who determines the optimal loan price (interest rates) to offer to prospective borrowers under uncertain risk and borrowers’ response. A borrower may or may not accept the loan at the price offered, and in the presence of default risk, both the principal loaned and the interest income become uncertain. We … Read more

Semidefinite Programming Approach to Russell Measure Model

Throughout its evolution, data envelopment analysis (DEA) has mostly relied on linear programming, particularly because of simple primal-dual relations and the existence of standard software for solving linear programs. Although also non-linear models, such as Russell measure or hyperbolic measure models, have been introduced, their use in applications has been limited mainly because of their … Read more