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200 00$aSimulation-optimization in logistics, transportation, and SCM /$fby Angel a Juan, Markus Rabe, David Goldsman, editors
210  1$aBasel :$cMDPI - Multidisciplinary Digital Publishing Institute,$d[2023]
215   $a1 online resource (270 pages)
311   $a3-0365-1260-8 
330   $aTransportation, 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 reprint presents a collection of selected articles on simulation optimization in transportation, logistics, and supply chain management. The reprint 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.
606   $aBusiness logistics
606   $aBusiness logistics$xSafety measures
615  0$aBusiness logistics.
615  0$aBusiness logistics$xSafety measures.
676   $a658.7
702   $aJuan$b Angel a
702   $aRabe$b Markus
702   $aGoldsman$b David
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906   $aBOOK
912   $a9910683382003321
996   $aSimulation-Optimization in Logistics, Transportation, and SCM$93086640
997   $aUNINA