Simulation-Optimization in Logistics, Transportation, and SCM

Dear Colleagues,

Transportation, logistics, and supply chain systems and networks constitute one of the pillars of modern economies and societies. From sustainable traffic management in smart cities or air transportation to green and socially responsible logistics practices, many enterprises and governments around the world have to make decisions that affect the efficiency of these complex systems. Typically, optimization algorithms are employed to deal with these challenges, and simulation approaches are utilized when considering scenarios under uncertainty. However, better results might be achieved by hybridizing both optimization algorithms with simulation techniques to deal with real-life transportation, logistics, and SCM challenges, which often are large-scale and NP-hard problems under uncertainty conditions. Hence, simheuristic algorithms (combining metaheuristics with simulation) as well as other simulation-optimization approaches constitute an effective way to support decision makers in such complex scenarios.

This Special Issue aims at presenting a collection of high-quality papers on simulation-optimization in transportation, logistics, and supply chain management. Simulation-optimization algorithms, including simheuristics and simulation-based optimization, and their practical applications in the solving of rich and realistic scenarios under uncertainty are welcome. The Special Issue is open to well-known researchers in these topics. In particular, this Special Issue is strongly connected to the topics covered in the Winter Simulation Conference (WSC) track on logistics, transportation, and SCM, which includes a stream in simheuristic algorithms as well. Extended versions of the best papers presented at the WSC’19 and WSC’20 (as well as at other conferences of similar quality) are also invited. 

Prof. Dr. Angel A. Juan
Prof. Dr. Markus Rabe
Prof. Dr. David Goldsman
Prof. Dr. Javier Faulin
Guest Editors

Manuscript Submission Information

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