"Computational Optimization Approaches for Distributing Service Points for Mobility Applications and Smart Charging of Electric Vehicles",
Technical University Vienna, 2022.
For many business models in the mobility domain an optimal distribution of service points in a customer community is needed. Examples are charging stations of electric vehicles (EVs), bicycle sharing stations, battery swapping stations, or repair stations. Two main challenges are to get the necessary data about the community and environment in order to estimate user demands, local constraints of potential locations, and other properties and to identify optimal service station locations based on these data. Traditionally, these two tasks are considered in a separated fashion. Obtaining input data for the optimization step in a classical way essentially always is inherently incomplete and error prone for larger practical scenarios since manifold aspects play roles in complex, often non-obvious ways, and not all of them can be captured with appropriate estimations of their impacts. In this work we present approaches for solving both challenges, the data acquisition and the optimization, in a combined way by a cooperation of a preference-based optimization algorithm and customers. Instead of estimating customer demands upfront, customers
are incorporated directly into the optimization process, i.e., users can interact with the optimization algorithm by expressing their preferences for where to best place service points. Potential customers further know local situations and their particular properties, including also special aspects that cannot be easily captured in a classical data acquisition approach. The expected benefits of such an approach are a faster and cheaper data acquisition, the direct integration of users into the whole planning process, possibly a stronger emotional link of the users to the product, and ultimately better and more accepted optimization results.
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