Container Terminal Overland Collection and Distribution Logistics Generalized Computing Model with Computational Logistics and Case Study

Li, Bin; Liu, Ronghua; Yuan, Y.

doi:10.1109/ccdc.2019.8832507

Cited by 3 publications

(3 citation statements)

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“…So we proposed a definition of computational logistics within the conceptual framework of computational thinking preliminarily on the IEEE 54th Annual Conference on Decision and Control (CDC 2015) in December 2015 [51], and then elaborated the logistics generalized computation for container terminals (LGC-CT) which was a core concept in the following discussion, and its planning, scheduling, control and decision based on computational logistics [52]- [54]. Furthermore, we have launched the empirical research on the typical large-scale container terminal in China with computational logistics [55].…”

Section: B Computational Logistics Originated From Container Terminal...mentioning

confidence: 99%

“…The medium-term scheduler is the berth load balancing to VOLUME 7, 2019 improve berth utilization and job throughput according to the hull form, ship route, berthing affinity and collection and distribution cycle. The short-term scheduler is the allocated number of QC and YC according to the LGC-CT context that has been defined in the previous study [54].…”

Section: Tactical Job Scheduling and Resource Allocation Strategy Def...mentioning

confidence: 99%

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Container Terminal Oriented Logistics Generalized Computational Complexity

Sun

Yao

et al. 2019

IEEE Access

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The planning and scheduling of container terminal logistics systems (CTLS) are the multiobjective and multiple strong constraints combinatorial optimization challenges under the uncertain environments, and those are provided with high goal orientation, dynamics, context-sensitivity, coupling, timeliness, and complexity. The increasingly sophisticated decision-making for CTLS is one of the most pressing problems for the programming and optimization method available. This paper discusses CTLS in terms of logistics generalized computation complexity based on computational thinking, great principles of computing, and computational lens, which three are abbreviated with 3CTGPL, and makes a definition of container terminal oriented logistics generalized computational complexity (CTO-LGCC) and container terminal logistics generalized computation comprehensive performance perspective (CTL-GCCPP) from the dimensions of time, space, communication, processor, and memory access. Both can analyze, generalize, migrate, translate, localize, modificate, and evaluate the above-complicated problems and lay solid foundations and establish a feedback improvement framework for the computational model and scheduling algorithms of the CTLS, which is an essential complement to the modeling and optimization methodology and solutions to CTLS with computational logistics. Finally, aimed at the logistics service cases for a largescale container terminal, the simulation is designed and implemented for different scheduling algorithms, and the qualitative and quantitative comprehensive analysis is executed for the concomitant CTO-LGCC that demonstrates and verifies the feasibility and credibility of the CTO-LGCC and CTL-GCCPP from the viewpoint of the practice of container terminal decision-making support on the tactical level.

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Section: B Computational Logistics Originated From Container Terminal...mentioning

confidence: 99%

Section: Tactical Job Scheduling and Resource Allocation Strategy Def...mentioning

confidence: 99%

Container Terminal Oriented Logistics Generalized Computational Complexity

Sun

Yao

et al. 2019

IEEE Access

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“…Fifth, the planning and scheduling mechanism and algorithm embedded in operating system and virtual machine was transferred and customized into the operation of CTLS [57,58]. Lastly, we applied the computational logistics into the performance evaluation of CTLS for the typical scenario and acquired a new perspective and insight into CTLS based on the LGC-CT [59,60]. Now, we continue our previous work, and put forward the CTL-GCA to model, optimize, evaluate, and guide the running of container terminals, especially for the sustainable development and green production of CTLS.…”

Section: Literature Reviewmentioning

confidence: 99%

Container Terminal Logistics Generalized Computing Architecture and Green Initiative Computational Pattern Performance Evaluation

Song

2019

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Container terminals are the typical representatives of complex supply chain logistics hubs with multiple compound attributes and multiple coupling constraints, and their operations are provided with the strong characteristics of dynamicity, nonlinearity, coupling, and complexity (DNCC). From the perspective of computational logistics, we propose the container terminal logistics generalized computing architecture (CTL-GCA) by the migration, integration, and fusion of the abstract hierarchy, design philosophy, execution mechanism, and automatic principles of computer organization, computing architecture, and operating system. The CTL-GCA is supposed to provide the problem-oriented exploration and exploitation elementary frameworks for the abstraction, automation, and analysis of green production at container terminals. The CTL-GCA is intended to construct, evaluate, and improve the solution to planning, scheduling, and decision at container terminals, which all are nondeterministic polynomial hard problems. Subsequently, the logistics generalized computational pattern recognition and performance evaluation of a practical container terminal service case study is launched by the qualitative and quantitative approach from the sustainable perspective of green production. The case study demonstrates the application, utilization, exploitation, and exploration of CTL-GCA preliminarily, and finds the unsustainable patterns of production at the container terminal. From the above, we can draw the following conclusions. For one thing, the CTL-GCA makes a definition of the abstract and automatic running architecture of logistics generalized computation for container terminals (LGC-CT), which provides an original framework for the design and implementation of control and decision mechanism and algorithm. For another, the CTL-GCA can help us to investigate the roots of DNCC thoroughly, and then the CTL-GCA makes for conducting the efficient and sustainable running pattern recognition of LGC-CT. It is supposed to provide a favorable guidance and supplement to define, design, and implement the agile, efficient, sustainable, and robust task scheduling and resource allocation for container terminals by computational logistics whether in the strategy level or the tactical one.

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