126 research outputs found

    Sea Container Terminals

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    Due to a rapid growth in world trade and a huge increase in containerized goods, sea container terminals play a vital role in globe-spanning supply chains. Container terminals should be able to handle large ships, with large call sizes within the shortest time possible, and at competitive rates. In response, terminal operators, shipping liners, and port authorities are investing in new technologies to improve container handling infrastructure and operational efficiency. Container terminals face challenging research problems which have received much attention from the academic community. The focus of this paper is to highlight the recent developments in the container terminals, which can be categorized into three areas: (1) innovative container terminal technologies, (2) new OR directions and models for existing research areas, and (3) emerging areas in container terminal research. By choosing this focus, we complement existing reviews on container terminal operations

    The synergistic effect of operational research and big data analytics in greening container terminal operations: a review and future directions

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    Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the โ€˜big dataโ€™ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research

    Solution methods for an integrated lot sizing and scheduling problem

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    In this talk we present results of an ongoing study, based on a problem of a textile factory. The core problem is an integrated lotsizing and scheduling one, characterized by sets of parallel machines, arbitrary demands and due dates for products, a compatibility matrix between machines and components and release dates of machines. In a solution, the quantities to produce by product/component/size are split among smaller lots, the machines in which those lots will be produced are determined, as well as the order in which they will be done. We present a MIP model and results of a VNS heuristic

    Adaptive autotuning mathematical approaches for integrated optimization of automated container terminal

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    With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.info:eu-repo/semantics/publishedVersio

    Control of free-ranging automated guided vehicles in container terminals

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    Container terminal automation has come to the fore during the last 20 years to improve their efficiency. Whereas a high level of automation has already been achieved in vertical handling operations (stacking cranes), horizontal container transport still has disincentives to the adoption of automated guided vehicles (AGVs) due to a high degree of operational complexity of vehicles. This feature has led to the employment of simple AGV control techniques while hindering the vehicles to utilise their maximum operational capability. In AGV dispatching, vehicles cannot amend ongoing delivery assignments although they have yet to receive the corresponding containers. Therefore, better AGV allocation plans would be discarded that can only be achieved by task reassignment. Also, because of the adoption of predetermined guide paths, AGVs are forced to deploy a highly limited range of their movement abilities while increasing required travel distances for handling container delivery jobs. To handle the two main issues, an AGV dispatching model and a fleet trajectory planning algorithm are proposed. The dispatcher achieves job assignment flexibility by allowing AGVs towards to container origins to abandon their current duty and receive new tasks. The trajectory planner advances Dubins curves to suggest diverse optional paths per origin-destination pair. It also amends vehicular acceleration rates for resolving conflicts between AGVs. In both of the models, the framework of simulated annealing was applied to resolve inherent time complexity. To test and evaluate the sophisticated AGV control models for vehicle dispatching and fleet trajectory planning, a bespoke simulation model is also proposed. A series of simulation tests were performed based on a real container terminal with several performance indicators, and it is identified that the presented dispatcher outperforms conventional vehicle dispatching heuristics in AGV arrival delay time and setup travel time, and the fleet trajectory planner can suggest shorter paths than the corresponding Manhattan distances, especially with fewer AGVs.Open Acces

    Future Greener Seaports:A Review of New Infrastructure, Challenges, and Energy Efficiency Measures

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    Recently, the application of renewable energy sources (RESs) for power distribution systems is growing immensely. This advancement brings several advantages, such as energy sustainability and reliability, easier maintenance, cost-effective energy sources, and ecofriendly. The application of RESs in maritime systems such as port microgrids massively improves energy efficiency and reduces the utilization of fossil fuels, which is a serious threat to the environment. Accordingly, ports are receiving several initiatives to improve their energy efficiency by deploying different types of RESs based on the power electronic converters. This paper conducts a systematic review to provide cutting-edge state-of-the-art on the modern electrification and infrastructure of seaports taking into account some challenges such as the environmental aspects, energy efficiency enhancement, renewable energy integration, and legislative and regulatory requirements. Moreover, the technological methods, including electrifications, digitalization, onshore power supply applications, and energy storage systems of ports, are addressed. Furthermore, details of some operational strategies such as energy-aware operations and peak-shaving are delivered. Besides, the infrastructure scheme to enhance the energy efficiency of modern ports, including port microgrids and seaport smart microgrids are delivered. Finally, the applications of nascent technologies in seaports are presented

    Focusing on the case analysis of advanced smart ports

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ธ€๋กœ๋ฒŒํ–‰์ •์ „๊ณต, 2023. 2. Lee, Soo-young.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ตœ๊ทผ ๊ฐ๊ด‘๋ฐ›๊ณ  ์žˆ๋Š” ์Šค๋งˆํŠธ ํ•ญ๋งŒ์˜ ๊ฐœ๋…๊ณผ ํ•ญ๋งŒ ๊ฒฝ์Ÿ๋ ฅ๊ณผ์˜ ๊ด€๊ณ„๋ฅผ ๊ณ ์ฐฐํ•ด ๋ณด๊ณ , ์„ ์ง„ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์— ๋Œ€ํ•œ ๋‹ค๊ฐ์ ์ธ ๋ถ„์„์„ ํ†ตํ•ด ์šฐ๋ฆฌ๋‚˜๋ผ ์Šค๋งˆํŠธ ํ•ญ๋งŒ ๋ฐœ์ „ ๋ฐฉํ–ฅ์— ๋Œ€ํ•œ ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด A. Molavi ์™ธ์˜ ์—ฐ๊ตฌ์—์„œ ํ™•๋ฆฝ๋œ ์Šค๋งˆํŠธ ํ•ญ๋งŒ ํ‰๊ฐ€ ์ฒ™๋„์˜ 4๊ฐ€์ง€ ์ธก๋ฉด, ์šด์˜์ธก๋ฉด(Operation), ํ™˜๊ฒฝ์ธก๋ฉด(Environment), ์—๋„ˆ์ง€ ์ธก๋ฉด(Energy), ๊ทธ๋ฆฌ๊ณ  ์•ˆ์ „๊ณผ ๋ณด์•ˆ ์ธก๋ฉด(Safety & Security)์˜ ๋ถ„์„ํ‹€์„ ํ™œ์šฉํ•˜์—ฌ ์Šค๋งˆํŠธ ํ•ญ๋งŒ ๊ฐœ๋ฐœ๊ณผ ๋ฐœ์ „์— ๊ฐ€์žฅ ์•ž์„  ๋„ค๋œ๋ž€๋“œ์˜ ๋กœํ…Œ๋ฅด๋‹ด ํ•ญ๋งŒ๊ณผ ๋…์ผ์˜ ํ•จ๋ถ€๋ฅดํฌ ํ•ญ๋งŒ์˜ ์ •์ฑ… ๋ถ„์„์„ ์‹œ๋„ํ•˜์˜€๋‹ค. A. Molavi ์™ธ์˜ ์—ฐ๊ตฌ๋Š” ์ธก์ • ๊ฐ€๋Šฅํ•œ ์Šค๋งˆํŠธํ™” ์ง€์ˆ˜๋ฅผ ๋ฐœ์ „์‹œ์ผœ ๊ฐ ํ•ญ๋งŒ์˜ ์Šค๋งˆํŠธํ™” ์ •๋„๋ฅผ ๊ฐ€๋Š ํ•˜๊ณ  ์žฅ๋‹จ์ ์„ ํŒŒ์•…ํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜๊ธฐ ์œ„ํ•œ ์ทจ์ง€์—์„œ ๊ฐœ๋ฐœ๋˜์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์Šค๋งˆํŠธ ํ•ญ๋งŒ์˜ ํ‰๊ฐ€ ์ฒ™๋„๋ฅผ ํ™œ์šฉํ•˜๋˜ ์งˆ์ ์ธ ๋ถ„์„์œผ๋กœ ์ ‘๊ทผํ•˜์—ฌ ์ •์ฑ… ํ™œ์šฉ ์ธก๋ฉด์—์„œ ์œ ์šฉํ•œ ์‹œ์‚ฌ์ ์„ ๋„์ถœํ•˜๋Š”๋ฐ ๋ชฉ์ ์„ ๋‘์—ˆ๋‹ค. ๋˜ํ•œ ๋™์ผํ•œ ํ‹€์„ ํ™œ์šฉํ•˜์—ฌ ํ˜„์žฌ ๋ถ€์‚ฐ ์ปจํ…Œ์ด๋„ˆ ํ„ฐ๋ฏธ๋„์˜ ์Šค๋งˆํŠธ ํ•ญ๋งŒ ๋ฐœ์ „ ๊ณ„ํš์„ ๋ถ„์„ํ•˜๊ณ  ๋ฐœ์ „๋ฐฉํ–ฅ ์„ค์ •์— ๋„์›€์„ ์ฃผ๊ณ ์ž ํ•˜์˜€๋‹ค. ์šฐ์„  ์šด์˜ ์ธก๋ฉด์—์„œ ์„ ์ง„ ์Šค๋งˆํŠธ ํ•ญ๋งŒ๋“ค์€ ํ•ญ๋งŒ ๋‚ด ํ•˜์—ญ ์ „ ๊ณผ์ •์˜ ์™„์ „ ์ž๋™ํ™”๋ฅผ ๋‹ฌ์„ฑํ•˜์˜€๊ณ , ์ด์— ๊ทธ์น˜์ง€ ์•Š๊ณ  ํ•ญ๋งŒ ๋‚ด ๋ชจ๋“  ๊ณผ์ •์„ 4์ฐจ ์‚ฐ์—…ํ˜๋ช…์˜ ์ฒจ๋‹จ ๊ธฐ์ˆ ๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ๋ฌด์ธํ™”์™€ ํšจ์œจํ™”๋ฅผ ์ถ”๊ตฌํ•˜์˜€๋‹ค. ์ด ๊ณผ์ •์—์„œ A.I, IoT, ๋ธ”๋ก์ฒด์ธ ๋“ฑ 4์ฐจ ์‚ฐ์—…ํ˜๋ช…์˜ ํ•ต์‹ฌ ๊ธฐ์ˆ ๋“ค์„ ์ ๊ทน ํ™œ์šฉํ•˜์—ฌ ํ•ญ๋งŒ์˜ ์ „์ฒด์ ์ธ ๋ชจ์Šต์„ ๋ณ€ํ™”์‹œ์ผœ ๊ฐ€๊ณ  ์žˆ์œผ๋ฉฐ, ๋น„์šฉ์ ˆ๊ฐ๊ณผ ์ƒ์‚ฐ์„ฑ ์ฆ๋Œ€ ๋“ฑ ์ง์ ‘์ ์ธ ํšจ๊ณผ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๊ธ€๋กœ๋ฒŒ ๋ฌผ๋ฅ˜์˜ ํ•ต์‹ฌ ๊ตฌ์‹ฌ์ ์œผ๋กœ์จ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์˜ ๊ฐ€๋Šฅ์„ฑ์„ ๋ฐœ์ „์‹œ์ผœ ๋‚˜๊ฐ€๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ํ•ญ๋งŒ ๊ฒฝ์Ÿ๋ ฅ ํ–ฅ์ƒ์€ ๋ฌผ๋ก  ๋ฌผ๋ฅ˜ ํฌํ„ธ๋กœ์จ์˜ ์ง€์œ„๋ฅผ ์„ ์ ํ•˜๊ธฐ ์œ„ํ•œ ๊ฒฝ์Ÿ๋„ ์‹ฌํ™”๋˜๊ณ  ์žˆ๋‹ค. ํ™˜๊ฒฝ ์ธก๋ฉด์—์„œ๋Š” ์นœํ™˜๊ฒฝ ํ•ญ๋งŒ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๋Œ€๋˜๊ณ  ์žˆ๋‹ค. ํ•ญ๋งŒ์€ ๋” ์ด์ƒ ๋„์‹œ์™€ ๋ถ„๋ฆฌ๋˜์–ด ์กด์žฌํ•˜๋Š” ๋…๋ฆฝ๋œ ์˜์—ญ์ด ์•„๋‹Œ, ์ธ์ ‘ ๋„์‹œ ์ฃผ๋ฏผ๋“ค๊ณผ ์ƒํ˜ธ ์˜ํ–ฅ์„ ์ฃผ๊ณ ๋ฐ›์œผ๋ฉฐ ๋ฐœ์ „ํ•˜๋Š” ํ˜ธํ˜œ์ ์ธ ๊ด€๊ณ„๋ฅผ ๊ตฌ์ถ•ํ•ด์•ผ ํ•œ๋‹ค๋Š”๋ฐ ๊ณต๊ฐ๋Œ€๊ฐ€ ํ˜•์„ฑ๋˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ทธ๋™์•ˆ ํ•ญ๋งŒ ํ™œ๋™์„ ํ†ตํ•ด ์•ผ๊ธฐ๋˜์—ˆ๋˜ ํ™˜๊ฒฝ ์˜ค์—ผ ๋ฌธ์ œ๋ฅผ ์ค„์ด๊ณ  ์ง€์—ญ์‚ฌํšŒ์— ๊ธฐ์—ฌํ•˜๊ธฐ ์œ„ํ•œ ๋…ธ๋ ฅ๋“ค์ด ํ™œ๋ฐœํžˆ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. ์ „๋ ฅ์— ๊ธฐ๋ฐ˜ํ•œ ์นœํ™˜๊ฒฝ ํ•˜์—ญ์žฅ๋น„๋กœ ๋Œ€์ฒดํ•˜๊ณ , ์„ ๋ฐ•์˜ ์—ฐ๋ฃŒ๋ฅผ ์นœํ™˜๊ฒฝ ์—ฐ๋ฃŒ๋กœ ์ „ํ™˜ํ•˜๋Š” ๋…ธ๋ ฅ์ด ์ง„ํ–‰ ์ค‘์ด๋‹ค. ํ•ญ๋งŒ ๋‚ด ์œ ํœด๋ถ€์ง€๋ฅผ ํ™œ์šฉํ•ด ์‹ ์žฌ์ƒ์—๋„ˆ์ง€๋ฅผ ๋ฐœ์ „ํ•˜๊ณ  ์ธ๊ทผ ์ง€์—ญ์— ๊ณต๊ธ‰ํ•˜๋Š” ๋ฐฉ์•ˆ๊ณผ, ํ•ญ๋งŒ์˜ ํ™˜๊ฒฝ ๋ฌธ์ œ๋ฅผ IoT ๊ธฐ์ˆ ์„ ํ™œ์šฉํ•˜์—ฌ ์‹ค์‹œ๊ฐ„์œผ๋กœ ๊ฐ์‹œํ•˜๊ณ  ๊ณต์œ ํ•˜๋Š” ์‹œ์Šคํ…œ์„ ๊ตฌ์ถ•ํ•˜์—ฌ ํ•ญ๋งŒ์˜ ์ง€์† ๊ฐ€๋Šฅํ•œ ๋ฐœ์ „์„ ์˜๋„ํ•˜๋ฉฐ ํƒ„์†Œ ์ค‘๋ฆฝ ์‚ฌํšŒ๋กœ์˜ ์ง„์ „์— ์ค‘์š”ํ•œ ์—ญํ• ์„ ์ž์ฒ˜ํ•˜๊ณ  ์žˆ๋‹ค. ์—๋„ˆ์ง€ ์ธก๋ฉด์—์„œ๋Š” ์Šค๋งˆํŠธ ํ•ญ๋งŒ์ด ๋ฏธ๋ž˜ ์ˆ˜์†Œ ์‚ฌํšŒ์˜ ํ•ต์‹ฌ ๊ณต๊ธ‰ ๊ธฐ์ง€๊ฐ€ ๋  ์ „๋ง์ด๋‹ค. ํ•ด์ƒ ๋ฌผ๋ฅ˜์™€ ์œก์ƒ ๋ฌผ๋ฅ˜๊ฐ€ ๊ฒฐํ•ฉ๋˜๋Š” ๊ธฐ๋Šฅ์  ์ด์ ์„ ํ™œ์šฉํ•˜์—ฌ ์ˆ˜์†Œ์˜ ์ƒ์‚ฐ๊ณผ ์ €์žฅ, ๋ถ„๋ฐฐ ๋“ฑ ์ˆ˜์†Œ ๊ฒฝ์ œ์˜ ํ•ต์‹ฌ ์ธํ”„๋ผ๋ฅผ ํ•ญ๋งŒ ๋‚ด ๊ตฌ์ถ•ํ•˜๊ณ  ํ•ญ๋งŒ ๊ธฐ๋Šฅ๊ณผ์˜ ๊ฒฐํ•ฉ์„ ์‹œ๋„ํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์„ ์ง„ ํ•ญ๋งŒ๋“ค์€ ๋Œ€๊ทœ๋ชจ ํŒŒ์ดํ”„ ๋ผ์ธ์„ ๊ฑด์„คํ•˜๋Š” ํ”„๋กœ์ ํŠธ๋“ค์„ ์ง„ํ–‰ํ•˜๋ฉฐ ๋ฏธ๋ž˜๋ฅผ ์ค€๋น„ํ•˜๊ณ  ์žˆ๋‹ค. ์•ˆ์ „๊ณผ ๋ณด์•ˆ ์ธก๋ฉด์—์„œ๋Š” ํ•ญ๋งŒ์ด ์ฒจ๋‹จ ๊ธฐ์ˆ  ํ™œ์šฉ์˜ ๊ฒฝ์—ฐ์žฅ์ด ๋˜๊ณ  ์žˆ๋‹ค. ํ•ญ๊ณต ๋ฐ ํ•ด์ƒ, ์ˆ˜์ค‘ ๋“œ๋ก  ๋“ฑ ์ฒจ๋‹จ ์žฅ๋น„๋“ค์„ ํ™œ์šฉํ•˜์—ฌ ๋“œ๋„“์€ ํ•ญ๋งŒ์„ ๊ฐ€์ƒ ํ˜„์‹ค์„ธ๊ณ„์ธ ํŠธ์œˆ ํƒ€์›Œ์— ์ด์‹ํ•˜๊ณ  ์ธ๊ณต์ง€๋Šฅ์— ์˜ํ•œ ์‹ค์‹œ๊ฐ„ ๊ด€๋ฆฌ ๊ฐ๋…์ด ๊ฐ€๋Šฅํ•œ ์‹œ์Šคํ…œ์ด ๊ตฌ์ถ•๋˜๊ณ  ์žˆ๋‹ค. ํ•ญ๋งŒ ๋‚ด ํ•˜์—ญ์ž‘์—…์˜ ๋ฌด์ธํ™”๋Š” ์•ˆ์ „์‚ฌ๊ณ ์˜ ์œ„ํ—˜์„ ํš๊ธฐ์ ์œผ๋กœ ์ค„์ผ ์ˆ˜ ์žˆ์„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์‚ฌ๊ฐ ์ง€๋Œ€๊ฐ€ ์—†๋Š” ๊ด€๋ฆฌ ๊ฐ๋…๋„ ๊ฐ€๋Šฅํ•ด์ ธ ํ•ญ๋งŒ ๋‚ด ์žฌ๋‚œ์‚ฌ๊ณ ์™€ ๋ฐ€์ž…๊ตญ ๋“ฑ์˜ ๋ฌธ์ œ๋ฅผ ๊ทผ๋ณธ์ ์œผ๋กœ ๋ณ€ํ™”์‹œํ‚ฌ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ์„ ์ง„ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์—์„œ ์ถ”๊ตฌํ•˜๋Š” ๊ทผ๋ณธ์ ์ธ ๋ฐฉํ–ฅ์€ ์„ธ๊ณ„ ๋ฌผ๋ฅ˜์˜ ํ•ต์‹ฌ ํฌํ„ธ์„ ๊ตฌ์ถ•ํ•˜๋Š” ๊ฒƒ์ด๋ฉฐ ์ด๋ฅผ ์œ„ํ•ด ํ•ญ๋งŒ์˜ ์—ญํ• ์€ ๊ธฐ์กด์˜ ์ง€์—ญ์ ์ธ ํ•œ๊ณ„๋ฅผ ๋„˜์–ด ๊ธฐ๋Šฅ์ ์œผ๋กœ ๊ทธ๋ฆฌ๊ณ  ๋ฌผ๋ฆฌ์ ์œผ๋กœ ํŒฝ์ฐฝํ•˜๊ณ  ์žˆ๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ฒฝ์šฐ ์ผ์ฐ์ด ์ž๋™ํ™” ํ•ญ๋งŒ์˜ ๋ฐœ์ „์„ ์‹œ์ž‘ํ•œ ์œ ๋Ÿฝ ํ•ญ๋งŒ์€ ๋ฌผ๋ก  ์ธ๊ทผ ์ค‘๊ตญ๊ณผ ์‹ฑ๊ฐ€ํฌ๋ฅด์˜ ์ž๋™ํ™” ํ•ญ๋งŒ๊ณผ ๋น„๊ตํ•ด๋„ ๋’ค์ณ์ง€๊ณ  ์žˆ๋Š” ๊ฒƒ์ด ํ˜„์‹ค์ด๋‹ค. ์ด๋ฅผ ๋งŒํšŒํ•˜๊ธฐ ์œ„ํ•ด ์ค‘์•™ ์ •๋ถ€ ์ฐจ์›์—์„œ ์Šค๋งˆํŠธ ํ•ด์ƒ๋ฌผ๋ฅ˜์ฒด๊ณ„ ๊ตฌ์ถ• ์ „๋žต์„ ์ˆ˜๋ฆฝํ•˜๊ณ  2030๋…„ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์˜ ๋ณธ๊ฒฉ์ ์ธ ์šด์˜์„ ๊ณ„ํšํ•˜๊ณ  ์žˆ๋‹ค. ํ•˜์ง€๋งŒ ๋ณธ ๊ณ„ํš์€ ์ „๋ฐ˜์ ์ธ ๋ฌผ๋ฅ˜ ๊ธฐ๋Šฅ ์ค‘ ํ•˜์œ„ ์š”์†Œ๋กœ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์„ ์ธ์‹ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ์ด๋Š” ์Šค๋งˆํŠธ ํ•ญ๋งŒ์„ ์ž๋™ํ™” ํ•ญ๋งŒ์ด๋ผ๋Š” ์ข์€ ์ธก๋ฉด์—์„œ๋งŒ ๋ฐ”๋ผ๋ณด๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ, ํ•ญ๋งŒ์˜ ๋ฏธ๋ž˜ ์ž ์žฌ๋ ฅ์— ๋Œ€ํ•œ ์„ ์ง„ ํ•ญ๋งŒ๋“ค์˜ ์ธ์‹๊ณผ๋Š” ํฐ ์ฐจ์ด๊ฐ€ ์žˆ๋‹ค๊ณ  ํ•˜๊ฒ ๋‹ค. ๋˜ํ•œ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์˜ ๋ฐœ์ „ ๊ณผ์ •์—์„œ ๋ฏผ๊ฐ„ ๊ธฐ์—…๊ณผ ํ•ญ๋งŒ ์ดํ•ด๊ด€๊ณ„์ž๋“ค์ด ์ ๊ทน์ ์œผ๋กœ ์ฐธ์—ฌํ•˜๊ณ  ํ˜‘๋ ฅํ•˜์—ฌ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์˜ ๋ชจ์Šต์„ ๊ทธ๋ ค๊ฐ€๋Š” ์„ ์ง„ ํ•ญ๋งŒ๊ณผ๋Š” ๋‹ฌ๋ฆฌ ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ฒฝ์šฐ ์—ฌ์ „ํžˆ ์ •๋ถ€ ์ฃผ๋„ ๋ฐœ์ „ ๋ฐฉ์‹์„ ๊ณ ์ˆ˜ํ•˜๊ณ  ์žˆ์œผ๋ฉฐ, ๊ฐ€์žฅ ์ฃผ๋„์ ์ธ ์—ญํ• ์„ ํ•ด์•ผ ํ•  ํ•ญ๋งŒ ๊ณต์‚ฌ๋“ค์˜ ์—ญํ• ์ด ๋ฏธ๋ฏธํ•œ ๊ฒƒ์€ ํ•œ๊ณ„๋ผ๊ณ  ํ•˜๊ฒ ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ํ–ฅํ›„ ํƒ„์†Œ ์ค‘๋ฆฝ ์‚ฌํšŒ๋กœ์˜ ์ดํ–‰์˜๋ฌด ๋“ฑ ํ™˜๊ฒฝ์ ์ธ ๋ฌธ์ œ์™€ ์นœํ™˜๊ฒฝ ์—๋„ˆ์ง€๋กœ์˜ ์ „ํ™˜์ด ์ค‘์š”์‹œ๋˜๊ณ  ์žˆ๋Š” ์‹œ์ ์—์„œ ์ด์— ๋Œ€ํ•œ ๊ทผ๋ณธ์ ์ธ ์ „ํ™˜๊ณ„ํš์ด๋‚˜ ํ•ญ๋งŒ์˜ ์ƒˆ๋กœ์šด ์—ญํ• ์— ๋Œ€ํ•œ ๊ณ ๋ฏผ์ด ๋ถ€์กฑํ•œ ๊ฒƒ๋„ ๋น„๊ต ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๋„์ถœํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์œ ๋Ÿฝ์˜ ํ•ญ๋งŒ๋“ค๊ณผ๋Š” ๋‹ฌ๋ฆฌ ์ˆ˜์†Œ ๊ฒฝ์ œ๋กœ์˜ ์ดํ–‰์— ์žˆ์–ด ํ•ญ๋งŒ์˜ ํ•ต์‹ฌ์  ์—ญํ• ์ด ๋น ์ ธ ์žˆ๋‹ค๋Š” ๊ฒƒ์€ ์Šค๋งˆํŠธ ํ•ญ๋งŒ์— ๋Œ€ํ•œ ์ธ์‹ ๋ถ€์กฑ์—์„œ ๋น„๋กฏ๋œ ๊ฒƒ์œผ๋กœ ๋ณด์ด๋ฉฐ ์ด์— ๋Œ€ํ•œ ์ •์ฑ…์  ๊ฐœ์„ ์ด ํ•„์š”ํ•œ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค.This study examines the relationship between the concept of smart ports and port competitiveness, which have recently been in the spotlight, and attempts to derive implications for Korea's smart port development direction through various analysis of advanced smart ports. To this end, this research attempted to analyze the policies of Rotterdam Port in the Netherlands and Hamburg Port in Germany, which are most advanced in smart port development and development, using the analysis framework of four smart port evaluation measures established in A. Molavi et al. In terms of operation, advanced smart ports achieved complete automation of the entire loading and unloading process in the port, and not only this, but all processes in the port were pursued for unmanned and efficient use of the advanced technologies of the 4th Industrial Revolution. In terms of the environment, interest in eco-friendly ports is increasing. There is a consensus that ports should no longer be independent areas that exist separately from cities, but should establish reciprocal relationships that interact and develop with residents of neighboring cities. In terms of energy, smart ports are expected to become a key supply base for the future hydrogen society. Taking advantage of the functional advantages of combining marine logistics and land logistics, the core infrastructure of the hydrogen economy, such as hydrogen production, storage, and distribution, is built in ports and attempted to combine them with port functions. In terms of safety and security, ports are becoming a competition for the use of advanced technology. Using high-tech equipment such as aviation, sea, and underwater drones, a system that allows real-time management and supervision by artificial intelligence is being established by transplanting a wide port into a virtual reality twin tower. In the case of Korea, the reality is that it is lagging behind not only European ports that started the development of automated ports early but also automated ports in neighboring China and Singapore. To make up for this, the central government has established a "smart maritime logistics system construction strategy" and plans to operate smart ports in earnest in 2030. However, this plan recognizes smart ports as a sub-factor of the overall logistics function, which only looks at smart ports in the narrow aspect of automated ports, which is very different from advanced ports' perceptions of the future potential of ports. In addition, unlike advanced ports in which private companies and port stakeholders actively participate and cooperate in the development of smart ports, Korea still adheres to the government-led development method, and the role of port authorities to play the most leading role is insignificant. In addition, at a time when environmental problems such as the obligation to transition to a carbon-neutral society in the future and the transition to eco-friendly energy are becoming important, this comparative study was able to derive the lack of concern about the fundamental transition plan or the new role of ports. Unlike ports in Europe, the absence of a key role in the transition to a hydrogen economy seems to stem from a lack of awareness of smart ports, and policy improvements are needed.Chapter 1. Introduction ๏ผ‘ 1.1. Study Background ๏ผ‘ 1.2. Scope and Method of Study ๏ผ’ Chapter 2. Theoretical Discussions and Prior Study Reviews ๏ผ” 2.1. Theoretical discussion of smart ports ๏ผ” 2.1.1. Significance of Ports ๏ผ” 2.1.2. Development of Ports ๏ผ• 2.1.3. Prior Study of Smart Ports ๏ผ– 2.1.4. Smart Port Index (SPI) ๏ผ™ 2.2. Theoretical discussion of port competitiveness ๏ผ‘๏ผ‘ 2.2.1 The Concept of Port Competitiveness ๏ผ‘๏ผ‘ 2.2.2. A Prior Study on Port Competitiveness ๏ผ‘๏ผ“ 2.2.3. Port Competitiveness and Performance Evaluation ๏ผ‘๏ผ• 2.3. The relationship between smart ports and port competitiveness ๏ผ‘๏ผ— 2.3.1. Smart Port Components and Port Competitiveness ๏ผ‘๏ผ— 2.3.2. Trends in Smart Port Development ๏ผ’๏ผ“ 2.4. Results of previous study review ๏ผ’๏ผ— 3.1. Analysis Targets and Data ๏ผ’๏ผ˜ 3.2. Analytical Model ๏ผ’๏ผ™ Chapter 3. Case Analysis ๏ผ“๏ผ’ 3.1. Port of Rotterdam (Netherlands) ๏ผ“๏ผ’ 3.1.1. Background and Status of Smart Port Introduction ๏ผ“๏ผ’ 3.1.2. Operational Aspects of Smart Port ๏ผ“๏ผ” 3.1.3. Environmental Aspects of Smart Port ๏ผ“๏ผ— 3.1.4. Energy Aspects of Smart Port ๏ผ“๏ผ™ 3.1.5. Safety and Security Aspects of Smart Port ๏ผ”๏ผ‘ 3.1.6. Implications ๏ผ”๏ผ“ 3.2. Port of Hamburg (Germany) ๏ผ”๏ผ• 3.2.1. Background and Status of Smart Port Introduction ๏ผ”๏ผ• 3.2.2. Operational Aspects of Smart Port ๏ผ”๏ผ˜ 3.2.3. Environmental Aspects of Smart Port ๏ผ•๏ผ‘ 3.2.4. Energy Aspects of Smart Port ๏ผ•๏ผ“ 3.2.5. Safety and Security Aspects of Smart Port ๏ผ•๏ผ• 3.2.5. Implications ๏ผ•๏ผ– 3.3. Port of Busan (S.Korea) ๏ผ•๏ผ˜ 3.3.1. Background and Status of Smart Port Introduction ๏ผ•๏ผ˜ 3.3.2. Operational Aspects of Smart Port ๏ผ–๏ผ 3.3.3. Environmental Aspects of Smart Port ๏ผ–๏ผ’ 3.3.4. Energy Aspects of Smart Port ๏ผ–๏ผ“ 3.3.5. Safety and Security Aspects of Smart Port ๏ผ–๏ผ” Chapter 4. Conclusion ๏ผ–๏ผ– 4.1. Results of Research ๏ผ–๏ผ– 4.2. Policy Implications ๏ผ—๏ผ 4.3. Limitations of Research ๏ผ—๏ผ” Bibliography ๏ผ—๏ผ– Abstract in Korean ๏ผ˜๏ผ’์„

    The synergistic effect of operational research and big data analytics in greening container terminal operations: A review and future directions

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    Container Terminals (CTs) are continuously presented with highly interrelated, complex, and uncertain planning tasks. The ever-increasing intensity of operations at CTs in recent years has also resulted in increasing environmental concerns, and they are experiencing an unprecedented pressure to lower their emissions. Operational Research (OR), as a key player in the optimisation of the complex decision problems that arise from the quay and land side operations at CTs, has been therefore presented with new challenges and opportunities to incorporate environmental considerations into decision making and better utilise the โ€˜big dataโ€™ that is continuously generated from the never-stopping operations at CTs. The state-of-the-art literature on OR's incorporation of environmental considerations and its interplay with Big Data Analytics (BDA) is, however, still very much underdeveloped, fragmented, and divergent, and a guiding framework is completely missing. This paper presents a review of the most relevant developments in the field and sheds light on promising research opportunities for the better exploitation of the synergistic effect of the two disciplines in addressing CT operational problems, while incorporating uncertainty and environmental concerns efficiently. The paper finds that while OR has thus far contributed to improving the environmental performance of CTs (rather implicitly), this can be much further stepped up with more explicit incorporation of environmental considerations and better exploitation of BDA predictive modelling capabilities. New interdisciplinary research at the intersection of conventional CT optimisation problems, energy management and sizing, and net-zero technology and energy vectors adoption is also presented as a prominent line of future research
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