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Florian Jaehn

Publications and source records attributed to Florian Jaehn.

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Approximation guarantees for Hub Covering Problems

Hub Covering Problems are a subclass of Hub Location Problems. The objective is to select a set of hubs that enable paths between given origin-destination delivery tasks, while minimizing the total setup cost of the hubs. Two constraints must be satisfied: each path must include one or two hubs, and depending on the problem variant, the total path length or the length of the longest edge must not exceed prescribed limits. Such problems arise in a variety of practical applications, including urban planning, cargo delivery systems, airline networks, telecommunication networks, and e-mobility. Although Hub Covering Problems are especially important from a practical point of view, no systematic study of their approximability exists. In particular, it remains open whether some variants can be reduced to others in polynomial time, and no approximation bound is known. We close this gap by establishing a hierarchy among these problems, demonstrating that certain variants are indeed special cases of others. For four variants, we give a polynomial-time approximation algorithm, and for the other eight variants, we show that no polynomial-time algorithm can decide whether a solution exists unless P=NP.

cs.DM

Crane Scheduling Problem with Energy Saving

During loading and unloading steps, energy is consumed when cranes lift containers, while energy is often wasted when cranes drop containers. By optimizing the scheduling of cranes, it is possible to reduce energy consumption, thereby lowering operational costs and environmental impacts. In this paper, we introduce a single-crane scheduling problem with energy savings, focusing on reusing the energy from containers that have already been lifted and reducing the total energy consumption of the entire scheduling plan. We establish a basic model considering a one-dimensional storage area and provide a systematic complexity analysis of the problem. First, we investigate the connection between our problem and the semi-Eulerization problem and propose an additive approximation algorithm. Then, we present a polynomial-time Dynamic Programming (DP) algorithm for the case of bounded energy buffer and processing lengths. Next, adopting a Hamiltonian perspective, we address the general case with arbitrary energy buffer and processing lengths. We propose an exact DP algorithm and show that the variation of the problem is polynomially solvable when it can be transformed into a path cover problem on acyclic interval digraphs. We introduce a paradigm that integrates both the Eulerian and Hamiltonian perspectives, providing a robust framework for addressing the problem.

cs.DS