In networks with limited resources, the concept of offloading computation to Mobile Edge Computing (MEC) has emerged as a promising research direction with the advent of new services in fifth-generation (5G) networks....
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Data-poisoning attacks can disrupt the efficient operations of transportation systems by misdirecting traffic flows via falsified data. One challenge in countering these attacks is to reduce the uncertainties on the t...
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We introduce multi-attribute optimization under preference uncertainty, a novel approach for optimization-based decision support when the decision-maker’s preferences are uncertain. Here, each feasible design is asso...
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The combination of renewable energy sources, storage systems, and electric vehicle stations in a microgrid framework represents a useful solution for electric mobility integration in the distribution network. The coor...
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Neural networks (NN) have been successfully applied to approximate various types of complex control laws, resulting in low-complexity NN -based controllers that are fast to evaluate. However, when approximating contro...
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We obtain explicit forms of the current best known asymptotic upper bounds for gaps between cubefree integers. In particular, we show that the interval (Formula presented) contains a cubefree integer for any (Formula ...
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The implementation of a multistate planning model for power generation-transmission expansion, as opposed to traditional independent planning frameworks, addresses the escalating energy demand and the need for efficie...
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In the ActivationkDisjointst-Paths (Activationk-DP) problem we are given a graph G=(V,E) with activation costs {cuvu,cuvv} for every edge uv∈E, a source-sink pair s,t∈V, and an integer k. The goal is to compute an e...
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In the face of increasingly complex international relations, the core algorithm SCUC/SCED in the field of power dispatch, which has long relied on foreign optimization solvers, faces enormous technical risks of being ...
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Ridepooling services require efficient optimization algorithms to simultaneously plan routes and pool users in shared rides. We consider a static dial-a-ride problem (DARP) where a series of origin-destination request...
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Ridepooling services require efficient optimization algorithms to simultaneously plan routes and pool users in shared rides. We consider a static dial-a-ride problem (DARP) where a series of origin-destination requests have to be assigned to routes of a fleet of vehicles. Thereby, all requests have associated time windows for pick-up and delivery, and may be denied if they can not be serviced in reasonable time or at reasonable cost. Rather than using a spatial representation of the transportation network we suggest an event-based formulation of the problem, resulting in significantly improved computational times. While the corresponding MILP formulations require more variables than standard models, they have the advan-tage that capacity, pairing and precedence constraints are handled implicitly. The approach is tested and validated using a standard IP-solver on benchmark data from the literature. Moreover, the impact of, and the trade-off between, different optimization goals is evaluated on a case study in the city of Wuppertal (Germany). (c) 2021 Elsevier B.V. All rights reserved.
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