02611nam 2200397 450 991068338200332120230703104749.03-0365-1261-6(CKB)5700000000354410(NjHacI)995700000000354410(EXLCZ)99570000000035441020230703d2023 uy 0engur|||||||||||txtrdacontentcrdamediacrrdacarrierSimulation-optimization in logistics, transportation, and SCM /by Angel a Juan, Markus Rabe, David Goldsman, editorsBasel :MDPI - Multidisciplinary Digital Publishing Institute,[2023]1 online resource (270 pages)3-0365-1260-8 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 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.Business logisticsBusiness logisticsSafety measuresBusiness logistics.Business logisticsSafety measures.658.7Juan Angel aRabe MarkusGoldsman DavidNjHacINjHaclBOOK9910683382003321Simulation-Optimization in Logistics, Transportation, and SCM3086640UNINA