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Demand forecasting lies at the heart of any revenue management system. It aims to estimate the quantity of a product or service that will be purchased in the future. In this paper, we perform railway demand forecasting for a major European railroad company by taking various contributing parameters into account. To have multipurpose results, the current problem is explored in two different aggregation levels. At the high level, the problem is defined as prediction of the total number of bookings for all trains departing on a specific departure date and within a certain time range. Moreover, in a more disaggregated level, the prediction models aim to compute the total number of bookings within each booking period for all trains leaving in a specific time range of a certain departure date. Using state-of-the-art machine learning methods and various heuristic feature construction techniques, remarkable results with high forecast accuracy and reasonable computational complexity are achieved in both aggregation levels. This paper aims to contribute to the application of ML techniques in RM by introducing new heuristic feature engineering techniques, exploring the importance of accurate clustering, and implementing state-of-the-art machine learning methods in the context of railway industry.

A new business opportunity beckons with the emergence of prosumers. This article proposes an innovative business model to harness the potential of aggregating behind-the-meter residential storage in which the aggregator compensates participants for using their storage system on an on-demand basis. A bilevel optimization model is developed to evaluate the potential of this proposed business model and determine the optimal compensation scheme for the participants. A realistic Texas case study confirms the year-round profitability of the model, showing that participants could earn on average nearly $1500 per year, and the aggregator could make an average profit of nearly $2000 per participant annually. The case study confirms that the proposed business model has potential, and that the main driver for a successful implementation is a suitable setting of the compensation paid to participants for using their energy storage system.

This paper proposes a new semidefinite programming relaxation for the satisfiability problem. This relaxation is an extension of previous relaxations arising from the paradigm of partial semidefinite liftings for 0/1 optimization problems. The construction of the relaxation depends on a choice of permutations of the clauses, and different choices may lead to different relaxations. We then consider the Tseitin instances, a class of instances known to be hard for certain proof systems, and prove that for any choice of permutations, the proposed relaxation is exact for these instances, meaning that a Tseitin instance is unsatisfiable if and only if the corresponding semidefinite programming relaxation is infeasible.

This paper is concerned with the application of semidefinite programming to the satisfiability problem, and in particular with using semidefinite liftings to efficiently obtain proofs of unsatisfiability. We focus on the Tseitin satisfiability instances which are known to be hard for many proof systems. For Tseitin instances based on toroidal grid graphs, we present an explicit semidefinite programming problem with dimension linear in the size of the Tseitin instance, and prove that it characterizes the satisfiability of these instances, thus providing an explicit certificate of satisfiability or unsatisfiability.

Semidefinite optimization, commonly referred to as semidefinite programming, has been a remarkably active area of research in optimization during the last decade. For combinatorial problems in particular, semidefinite programming has had a truly significant impact. This paper surveys some of the results obtained in the application of semidefinite programming to satisfiability and maximum-satisfiability problems. The approaches presented in some detail include the ground-breaking approximation algorithm of Goemans and Williamson for MAX-2-SAT, the Gap relaxation of de Klerk, van Maaren and Warners, and strengthenings of the Gap relaxation based on the Lasserre hierarchy of semidefinite liftings for polynomial optimization problems. We include theoretical and computational comparisons of the aforementioned semidefinite relaxations for the special case of 3-SAT, and conclude with a review of the most recent results in the application of semidefinite programming to SAT and MAX-SAT.

•A novel method for identification of the Knee-pointin cell capacity degradation curves is developed.•The new concept of knee-onset, for Knee-point early indication, is introduced along with robust identification algorithms.•We show a strong linear relation between knee-onset, knee-point and end-of-life, where predicting one yields the others.•Machine learning techniques for the early prediction of knee-point and -onset using only early-cycle data are used.•The uncertainty of the predictions is methodologically quantified, providing reliable risk assessment for decision making.