Preface: Special Issue on Operations Research Models and Algorithms in Transportation
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Preface: Special Issue on Operations Research Models and Algorithms in Transportation Lu Zhen1 Published online: 27 August 2020 © Operations Research Society of China, Periodicals Agency of Shanghai University, Science Press, and Springer-Verlag GmbH Germany, part of Springer Nature 2020
Operations research, popularly known as OR, is a scientific research method or mathematical technique for providing decision making aid in the management of large organizations or organized systems. In the real world, transportation, a large organized system, is the movement of humans, animals, and goods from one location to another. The transportation problem is significantly complicated because of its wide range. The modes of transportation include air, land (rail and road), water, cable, pipeline, and space. The field of transportation can also be divided into infrastructure, vehicles, and operations. Hence, transportation is essential for the development of civilizations, and has been an active topic for decades, while operations research is one of its key contents. This special issue examines the current progress, challenges and approaches in transportation through operations research. Thus, the scope of this issue covers high-quality original research focusing on the use of OR techniques (mathematical programming models, algorithms and simulation-based approaches, etc.) to deal with transportation problems. This issue comprises nine papers by multiple scholars at home and abroad and explores the exploitation of operations research models (ORMs) and algorithms in transportation. The topics in this collection of selected papers encompass a broad area of ORMs and algorithms in transportation, including automatic guided vehicle routing, on-ramp lane arrangement, autonomous vessel scheduling, bus transportation, cross-docking flow shop problem and airline flight transportation, etc. The papers are summarized as follows. Lu Zhen, Yi-Wei Wu, Si Zhang, Qiu-Ji Sun, and Qi Yue propose a decision framework for port managers to efficiently design the automatic guided vehicle routing. Their decision framework deals with time-varying traffic conditions and makes automatic guided vehicle allocation plans more reasonable within a relatively short computation time. Extensive numerical experiments on a grid graph are conducted to validate
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Lu Zhen [email protected] School of Management, Shanghai University, Shanghai 200444, China
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the efficiency of the proposed decision framework. They also propose an efficient queueing rule in automatic guided vehicle routing scheduling. Xu Wang, Xiao-Bo Qu, and Pan Li compare the performances of two on-ramp lane arrangements (added lane and zip merging) in terms of travel time and CO2 emissions to help traffic engineers choose the appropriate on-ramp arrangement based on a given CO2 emission target. Moreover, they evaluate the relationships between travel time, emissions and different proportions of heavy goods vehicles for the above-mentioned two on-ramp lane managements. Wei Zhang an
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