Min-Max Regret Scheduling To Minimize the Total Weight of Late Jobs With Interval Uncertainty

We study the single machine scheduling problem with the objective to minimize the total weight of late jobs. It is assumed that the processing times of jobs are not exactly known at the time when a complete schedule must be dispatched. Instead, only interval bounds for these parameters are given. In contrast to the stochastic optimization approach, we consider the problem of finding a robust schedule, which minimizes the maximum regret of a solution. Heuristic algorithm based on mixed-integer linear programming is presented and examined through computational experiments

Data mining in distributedcomputer systems

The thesis presents a survey of techniques for accurate prediction of traffic distribution in computer network systems

Data mining in distributedcomputer systems

The thesis presents a survey of techniques for accurate prediction of traffic distribution in computer network systems

Adaptive pricing mechanisms for on-demand mobility

We consider on-demand car rental systems for public transportation. In these systems, demands are often unbalanced across different parking stations, necessitating costly manual relocations of vehicles. To address this so-called "deadheading-effect" and maximise the operator's revenue, we propose two novel pricing mechanisms. These adaptively adjust the prices between origin and destination stations depending on their current occupancy, probabilistic information about the customers' valuations and estimated relocation costs. In so doing, the mechanisms incentivise drivers to help rebalance the system and place a premium on trips that lead to costly relocations. We evaluate the mechanisms in a series of experiments using real historical data from an existing on-demand mobility system in a French city. We show that our mechanisms achieve an up to 64% increase in revenue for the operator and at the same time up to 36% fewer relocations