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Daniel Guimarans
Researcher at Hogeschool van Amsterdam
Publications - 37
Citations - 689
Daniel Guimarans is an academic researcher from Hogeschool van Amsterdam. The author has contributed to research in topics: Vehicle routing problem & Constraint programming. The author has an hindex of 11, co-authored 34 publications receiving 534 citations. Previous affiliations of Daniel Guimarans include Autonomous University of Barcelona & Monash University.
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Rich Vehicle Routing Problem: Survey
TL;DR: This work surveys the state of the art in the field of Vehicle Routing Problem research, summarizing problem combinations, constraints defined, and approaches found and concludes that the Rich VRP arises: combining multiple constraints for tackling realistic problems.
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A Biased-Randomised Large Neighbourhood Search for the two-dimensional Vehicle Routing Problem with Backhauls
TL;DR: A hybrid algorithm is presented that integrates biased-randomised versions of vehicle routing and packing heuristics within a Large Neighbourhood Search metaheuristic framework to better guide the local search process.
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Combining biased randomization with iterated local search for solving the multidepot vehicle routing problem
TL;DR: This approach, which only uses a few parameters, combines “biased randomization”—use of nonsymmetric probability distributions to generate randomness—with the iterated local search (ILS) metaheuristic.
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A simheuristic approach for the two-dimensional vehicle routing problem with stochastic travel times
TL;DR: A model of the 2L-VRP with stochastic travel times that also includes penalty costs generated by overtime is offered and a hybrid simheuristic algorithm is proposed that combines Monte Carlo simulation, an iterated local search framework, and biased-randomised routing and packing heuristics.
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A bi-objective approach for scheduling ground-handling vehicles in airports
TL;DR: A new approach for scheduling ground-handling vehicles, tackling the problem with a global perspective and inferring interesting criteria on how to optimize each resource, considering the effect on other operations is proposed.