F
Fabien Tricoire
Researcher at Vienna University of Economics and Business
Publications - 42
Citations - 1156
Fabien Tricoire is an academic researcher from Vienna University of Economics and Business. The author has contributed to research in topics: Vehicle routing problem & Heuristic (computer science). The author has an hindex of 14, co-authored 38 publications receiving 950 citations. Previous affiliations of Fabien Tricoire include Johannes Kepler University of Linz & École des mines de Nantes.
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A bi-objective home care scheduling problem: Analyzing the trade-off between costs and client inconvenience
TL;DR: A metaheuristic algorithm, embedding a large neighborhood search heuristic in a multi-directional local search framework, is proposed to solve the home care routing and scheduling problem as a bi-objective problem.
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Heuristics for the multi-period orienteering problem with multiple time windows
TL;DR: This work presents the multi-period orienteering problem with multiple time windows (MuPOPTW), a new routing problem combining objective and constraints of the orienteering Problem (OP) and teamorienteering problem (TOP), constraints from standard vehicle routing problems, and original constraints from a real-world application.
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The bi-objective stochastic covering tour problem
TL;DR: A branch-and-cut technique applied to a sample-average version of the problem obtained from a fixed random sample of demand vectors is used within an epsilon-constraint algorithm for the computational solution of the resulting bi-objective two-stage stochastic program with recourse.
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A set-covering based heuristic algorithm for the periodic vehicle routing problem
TL;DR: A hybrid optimization algorithm for mixed-integer linear programming, embedding both heuristic and exact components, based on the linear programming (LP) relaxation of a set-covering-like integer linear programming formulation of the problem, with additional constraints is presented.
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Multi-directional local search
TL;DR: Experimental results show that the proposed algorithmic framework is a viable option when solving multi-objective optimization problems, and systematically provides solution sets of comparable quality with state-of-the-art methods applied to benchmark instances of these problems, within reasonable CPU effort.