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Richard F. Hartl

Researcher at University of Vienna

Publications -  314
Citations -  16091

Richard F. Hartl is an academic researcher from University of Vienna. The author has contributed to research in topics: Vehicle routing problem & Metaheuristic. The author has an hindex of 58, co-authored 305 publications receiving 14198 citations. Previous affiliations of Richard F. Hartl include Vienna University of Technology & Otto-von-Guericke University Magdeburg.

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Explaining Fashion Cycles: Imitators Chasing Innovators in Product Space

TL;DR: In this article, the problem of a fashion trendsetter confronting an imitator who can produce the same product at lower cost is considered, and three broad strategies can be optimal for the fashion leader: (1) never innovate, which milk profits from the initially advantageous position but ultimately concede the market without a fight.
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A tutorial on the deterministic Impulse Control Maximum Principle: Necessary and sufficient optimality conditions

TL;DR: In this paper, the authors consider a class of optimal control problems that allow jumps in the state variable and present the necessary optimality conditions of the Impulse Control Maximum Principle based on the current value formulation.
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Nature-inspired metaheuristics for multiobjective activity crashing

TL;DR: Proper solution procedures based on three major (nature-inspired) metaheuristics are developed for multiobjective combinatorial optimization within a reasonable computation time for real-world problems.
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Terrorism control in the tourism industry

TL;DR: In this paper, the authors tried to model some relevant aspects of these prey-predator relations and found that the optimal trajectory exhibits a cyclical strategy, where after starting out with a low number of tourists and terrorists, tourism investments are undertaken to increase tourism.
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One- and two-sided assembly line balancing problems with real-world constraints

TL;DR: In this paper, the authors consider balancing problems of one-and two-sided assembly lines with real-world constraints like task or machine incompatibilities, and show that GA outperforms TS in terms of computational time.