Stochastic modeling of parallel process flows in intra-logistics systems: Applications in container terminals and compact storage systems

Kumawat, Govind Lal; Roy, Debjit; Koster, René de; Adan, Ivo

doi:10.1016/j.ejor.2020.08.006

Cited by 19 publications

(6 citation statements)

References 35 publications

(49 reference statements)

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“…Digital containers play an important role in global logistics, as they can provide many advantages and promote improvement in logistics efficiency and sustainability [35,36]. The efficiency and sustainability of global logistics is obviously improved by employing digital containers, which is achieved by real-time information sharing, transportation plan optimization, and paper-based work reduction.…”

Section: Container Supply Chain Managementmentioning

confidence: 99%

Digital Technique-Enabled Container Logistics Supply Chain Sustainability Achievement

Lyu,

Zhou,

2023

Sustainability

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With the rapid development of digital technology, the smart sensor-based container equipment and intelligent logistics operations contribute to achieving the efficiency improvement and sustainability achievement of container supply chain under the IoT-based logistics 4.0 scenarios. This paper tries to study the state-of-the-art knowledge of the container logistics supply chain management motivated by digital techniques. Through data-driven analysis this review is performed to assist researchers and practitioners to better understand the container logistics management. The integrated research framework is designed by developing a bibliometric analysis study to address the research themes of the container logistics era. The related publications from the Web of Science database from 2003 to 2022 were indexed and 2897 reference samples are collected as the research data. In addition, the VosViewer is adopted to portray the network, co-occurrence, and co-word analysis by visualizing the collaborative relationships of collected samples. The results show that digital technology has been widely applied in container logistics supply chain management practices, contributing to resilience and sustainability improvement by intelligent operations. These research findings are also helpful for researchers by providing a deep penetrating insight into research opportunities and great potentials of container logistics supply chain by innovative digital technology-enabled practices.

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Section: Container Supply Chain Managementmentioning

confidence: 99%

Digital Technique-Enabled Container Logistics Supply Chain Sustainability Achievement

Lyu,

Zhou,

2023

Sustainability

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“…Besides, the former research uses deterministic models and the latter only takes retrieval transactions into consideration. Recently, Kumawat et al [ 28 ] propose a closed queueing network with two-phase servers to model the simultaneously operations of shuttle and transfer car in a shuttle-based compact storage system. However, their model only captures the parallel movements of shuttles and transfer car before their joint movement, meaning the transfer car still has to wait for shuttle moving within storage lane to pick up the load.…”

Section: Introductionmentioning

confidence: 99%

“…However, existing studies on shuttle-based compact storage systems either assume sequential operations between shuttle and transfer car or focus on the modeling of parallel movements of shuttle and transfer car before their joint movement in cross-aisle, both of which mean that the transfer car has to wait for shuttle retrieving the load. In practice, the simultaneous movements of different resources when processing a transaction have advantages in system performance over their sequential movements [ 28 ]. Despite the requirement for shorter response times and the performance benefits of parallel processing policy, most previous literatures mainly focus on the sequential processing policy and studies on the parallel processing policy are rare.…”

Section: Introductionmentioning

confidence: 99%

Modeling and performance analysis of shuttle-based compact storage systems under parallel processing policy

Deng

Chen

Zhao³

et al. 2021

PLoS ONE

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Short response time for order processing is important for modern warehouses, which can be potentially achieved by adopting appropriate processing policy. The parallel processing policy have advantages in improving performance of many autonomous storage and retrieval systems. However, researchers tend to assume a sequential processing policy managing the movement of independent resources in shuttle-based compact storage systems. This paper models and analyses a single-tier of specialized shuttle-based compact storage systems under parallel processing policy. The system is modeled as a semi-open queueing network with class switching and the parallel movement of shuttles and the transfer car is modeled using a fork-join queueing network. The analytical model is validated against simulations and the results show our model can accurately estimate the system performance. Numerical experiments and a real case are carried out to compare the performance of parallel and sequential processing policies. The results suggest a critical transaction arrival rate and depth/width ratio, below which the sequential processing policy outperforms the parallel processing policy. However, the advantage of sequential processing policy is decreasing with the increasing of shuttle number, transaction arrival rate and depth/width ratio. The results also suggest an optimal depth/width ratio with a value of 1.75 for minimizing the expected throughput time in the real system. Given the current system configurations, the parallel processing policy should be considered when the number of shuttles is larger than 2 or the transaction arrival rate is larger than 24 per hour.

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“…e classical methods, which cover operational programming [12], optimization algorithm [13], system simulation [14,15], and simulation-based optimization [16], have not made a fundamental breakthrough in solving the above problems; moreover, they are increasingly struggling to cope with the myriad new problems that are constantly emerging in the logistics industry.…”

Section: Introductionmentioning

confidence: 99%

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

2021

Discrete Dynamics in Nature and Society

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The synergy of computational logistics and deep learning provides a new methodology and solution to the operational decisions of container terminal handling systems (CTHS) at the strategic, tactical, and executive levels. Above all, the container terminal logistics tactical operational complexity is discussed by computational logistics, and the liner handling volume (LHV) has important influences on a series of terminal scheduling decision problems. Subsequently, a feature-extraction-based lightweight convolutional and recurrent neural network adaptive computing model (FEB-LCR-ACM) is presented initially to predict the LHV by the fusion of multiple deep learning algorithms and mechanisms, especially for the specific feature extraction package of tsfresh. Consequently, the container-terminal-oriented logistics service scheduling decision support design paradigm is put forward tentatively by FEB-LCR-ACM. Finally, a typical large-scale container terminal of China is chosen to implement, execute, and evaluate the FEB-LCR-ACM based on the terminal running log around the indicator of LHV. In the case of severe vibration of LHV between 2 twenty-foot equivalent units (TEUs) and 4215 TEUs, while forecasting the LHV of 300 liners by the log of five years, the forecasting error within 100 TEUs almost accounts for 80%. When predicting the operation of 350 ships by the log of six years, the forecasting deviation within 100 TEUs reaches up to nearly 90%. The abovementioned two deep learning experimental performances with FEB-LCR-ACM are so far ahead of the forecasting results by the classical machine learning algorithm that is similar to Gaussian support vector machine. Consequently, the FEB-LCR-ACM achieves sufficiently good performance for the LHV prediction with a lightweight deep learning architecture based on the typical small datasets, and then it is supposed to overcome the operational nonlinearity, dynamics, coupling, and complexity of CTHS partially.

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Stochastic modeling of parallel process flows in intra-logistics systems: Applications in container terminals and compact storage systems

Cited by 19 publications

References 35 publications

Digital Technique-Enabled Container Logistics Supply Chain Sustainability Achievement

Digital Technique-Enabled Container Logistics Supply Chain Sustainability Achievement

Modeling and performance analysis of shuttle-based compact storage systems under parallel processing policy

A Feature-Extraction-Based Lightweight Convolutional and Recurrent Neural Networks Adaptive Computing Model for Container Terminal Liner Handling Volume Forecasting

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