W
Wout Dullaert
Researcher at VU University Amsterdam
Publications - 163
Citations - 5926
Wout Dullaert is an academic researcher from VU University Amsterdam. The author has contributed to research in topics: Vehicle routing problem & Supply chain. The author has an hindex of 38, co-authored 161 publications receiving 5111 citations. Previous affiliations of Wout Dullaert include Nottingham Trent University & Antwerp Maritime Academy.
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A memetic algorithm for bi-objective integrated forward/reverse logistics network design
TL;DR: This paper proposes a model for integrated logistics network design to avoid the sub-optimality caused by a separate, sequential design of forward and reverse logistics networks, and develops a bi-objective mixed integer programming formulation to minimize the total costs and maximize the responsiveness of a logistics network.
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Horizontal cooperation in logistics : Opportunities and impediments
TL;DR: In this paper, the authors present the results of a large-scale survey on the potential benefits of and impediments for horizontal cooperation in Flanders, finding a reliable party to lead the cooperation and constructing a fair allocation mechanism for the benefits are the impediments that respondents agree with most.
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A Multi-Objective Robust Optimization Model for Logistics Planning in the Earthquake Response Phase
TL;DR: In this paper, a multiobjective, multi-mode, mult-commodity, and multi-period stochastic model is proposed to manage the logistics of both commodities and injured people in the earthquake response.
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Horizontal Cooperation in Transport and Logistics: A Literature Review
TL;DR: In this paper, a broad review of horizontal cooperation in transport and logistics is presented, focusing on the drivers, impediments, and facilitators of the various types of cooperation and closely related literature.
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A penalty-based edge assembly memetic algorithm for the vehicle routing problem with time windows
TL;DR: Experimental results on Solomon's and Gehring and Homberger benchmarks demonstrate that the algorithm outperforms previous approaches and is able to improve 184 best-known solutions out of 356 instances.