Problem definition: Overbooking is commonly employed by outpatient clinics to counteract no-shows. State-of-the-art appointment scheduling systems are composed of a machine learning component, which predicts the individual patients’ no-show probability, and an optimization component, which...

This paper presents an optimal planning procedure and operational scheduling of micro energy grids (MEGs). The optimization objectives are to minimize cost, carbon dioxide emissions, and energy deficiency. The energy sources and conversion technologies included in this study are renewable based...

Given a set 𝑆 of 𝑛 points in the plane and a constant 𝛼,(𝑛, 1, 1, 𝛼)-center problem is to find two closed disks which each covers the whole 𝑆, the diameter of the bigger one is minimized, and the distance of the two centers is at least 𝛼. Constrained (𝑛, 1, 1,...

Problem definition: The effect of the minimum wage is an important yet controversial topic that has received attention for decades. Our study is the first to take an operational lens and empirically study the impact of the minimum wage on firms' scheduling practices. Methodology/results: Using a...

We test the efficient market hypothesis by using machine learning to forecast future stock returns from historical performance. These forecasts strongly predict the cross section of future stock returns. The predictive power holds in most subperiods, is strong among the largest 500 stocks, and...

The problem of dispatching shuttles to serve trip requests can be mathematically formulated as a dial-a-ride problem (DARP). With on-demand mobility services gaining more popularity due to the recent developments in gig economy, communication technologies, and urbanization, the real-time...

This paper presents new formulations for multi-activity shift scheduling problems (MASSP). In these formulations, the rules governing the feasibility of shifts are encoded in state-expanded networks in which nodes are associated with states and arcs represent assignments inducing state...