Simulation-Optimization in Logistics, Transportation, and SCM

• Other Authors: Juan, Angel A. (Editor), Rabe, Markus (Editor), Goldsman, David (Editor), Faulin, Javier (Editor)
• Format: Electronic Book Chapter
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: Information technology industries; Computer science; omnichannel retail stores; vehicle routing problem; pick-up and delivery; biased-randomized heuristics; simheuristics; scheduling; uncertainty; discrete event simulation; hybrid flow shop; scrap; local search; tabu search; machine qualifications; clustering; shortest processing time; maritime transportation; liner network design; synchronization; weather uncertainty; optimization simulation; discrete-event simulation; simulation-based optimization; assignment problem; neighborhood search; warehouse; nonlinear-flight-mechanics; neural networks; guidance, navigation, and control; machine learning; model; matlab-simulink; stochastic project scheduling; genetic algorithm; composite priority rules; agent-based simulation; horizontal cooperation; e-groceries; optimization; simulation; logistics; distribution networks; container terminal; meta-heuristic; horizontal transportation; hyper-parameter optimization; hybrid modeling; system dynamics; facility location Problems; Monte Carlo simulation; automated parcel lockers; last-mile delivery; location routing problem; heuristics; fuzzy logic; dockless bike-sharing system; Markovian queueing network; relocation; unequal demand
• Online Access: DOAB: download the publication; DOAB: description of the publication

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.