Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala

Awah, Penn Collins; Nam, Hojung; Kim, Sihyun

doi:10.3390/jmse9070720

Cited by 12 publications

(7 citation statements)

References 27 publications

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“…One of the issues of this industry is container throughput forecasting (Shankar et al 2020 ). Container throughput can be considered one of the important key performance indicators of any port (Awah et al 2021 ). In this regard, the authors Shankar et al ( 2020 ) used a DL network to forecast container throughput using data from the Port of Singapore.…”

Section: Systematic Review Resultsmentioning

confidence: 99%

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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In today’s complex and ever-changing world, Supply Chain Management (SCM) is increasingly becoming a cornerstone to any company to reckon with in this global era for all industries. The rapidly growing interest in the application of Deep Learning (a class of machine learning algorithms) in SCM, has urged the need for an up-to-date systematic review on the research development. The main purpose of this study is to provide a comprehensive vision by reviewing a set of 43 papers about applications of Deep Learning (DL) methods to the SCM, as well as the trends, perspectives, and potential research gaps. This review uses content analysis to answer three research questions namely: 1- What SCM problems have been solved by the use of DL techniques? 2- What DL algorithms have been used to solve these problems? 3- What alternative algorithms have been used to tackle the same problems? And do DL outperform these methods and through which evaluation metrics? This review also responds to this call by developing a conceptual framework in a value-adding perspective that provides a full picture of areas on where and how DL can be applied within the SCM context. This makes it easier to identify potential applications to corporations, in addition to potential future research areas to science. It might also provide businesses a competitive advantage over their competitors by allowing them to add value to their data by analyzing it quickly and precisely.

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Section: Systematic Review Resultsmentioning

confidence: 99%

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Shavaki

Ghahnavieh

2022

Artif Intell Rev

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“…Awah et al [15] explore key port performance indicators associated with port operations with the aim of modeling the optimal engineering throughput of a port and offering valuable knowledge that helps a port optimize its bottlenecks.…”

Section: Berth Allocation Problemmentioning

confidence: 99%

Mathematical Programming Formulations for the Berth Allocation Problems in Container Seaport Terminals

Aljuaid,

Koubâa,

Ammar

et al. 2024

Logistics

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Background: Improving the performance of marine terminals is one of the major concerns of both researchers and decision-makers in the maritime transportation sector. The problem of container storage planning and the berth allocation problem (BAP) are the two mainstays of optimizing port operations. Methods: In this work, we address these two issues, proposing two mathematical models that operate sequentially and are applicable to both static and dynamic cases. The first developed model is a mixed-integer linear problem model aimed at minimizing vessel traffic time in the port. The second model developed is a multi-objective optimization model based on goal programming (GP) to minimize both container transfer time and the number of storage areas (minimizing container dispersion). Results: The robustness of the proposed models has been proven through a benchmark with tests using data from the literature and real port data, based on the IBM ILOG CPLEX 12.5 solver. Conclusions: The two developed mathematical models allowed the both minimization of the transfer time and the number of used storage areas, whatever the number of operations handling companies (OHCs) operating in the seaport and for both static and dynamic cases. We propose, as prospects for this work, the development of a heuristic model to deal with the major instances relating to the case of large ports.

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“…The longer the container stays in the yard, the more difficult it becomes to locate. As such, it increases the dwell time, resulting in a storage charge or dwell fee [6]. This means that container Dwell time is a shipping term that primarily refers to the amount of time a container spends at a facility like a terminal or port between when it's unloaded from one form of transport and moved out of the facility.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Notwithstanding, demurrage is a charge that should be eliminated at all costs to increase the benefits to the importers and the efficiency of the Douala container terminal. According to many authors, including this research paper [6], with the putting in place of this piece of research regarding the Douala container terminal, many changes will occur regarding the reduction of demurrage and efficiency of the Douala terminal.…”

Section: Literature Reviewmentioning

confidence: 99%

Strategies for Reducing Demurrage at the Douala Container Terminals (RTC Terminal) during Importation

Atud,

Njuma,

Takwa

et al. 2023

AJEBA

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COVID-19 diverse methods and procedures of different port actors are some of the reasons for increasing demurrage for containerized goods. Demurrage is the additional charges levied by the maritime carrier on the users of its equipment (container) after the container passes the normal time in the seaport, which is 11 days in Cameroon (Douala). This study aims to investigate the main causes of increasing demurrage at the Douala container terminal and recommend strategies to reduce the increasing demurrage at the port RTC terminal (Container terminal management or Regie du Terminal a Conteneurs). This study uses a target population of 700 actors selected using a stratified random techniques in different fields linked to the international logistics chain and the Douala port in particular. Moreover a total of 490 participants responded to the questionnaire distributed to them. Descriptive statistics such as table and pie chart were used to analyze the data collected during the research. Findings demonstrated that the activities of the different actors involved in the logistics chain, namely the customs administrators, customs brokers, importers, shipping line exporters, PAD (Douala Autonomous Port) and other actors such as Society General de Surveillance (SGS), Cameroon Customs Information System (CAMCIS) and RTC Terminal are the main causes of the increased demurrage at the Douala port. The study recommends, amongst others, eliminating public holidays and weekends by shipping line when counting the demurrage days at the Douala port. Also we recommend that, the demurrage days should only start when the manifest is visible to all the actors, which is not the case. Equally, the government should impose the demurrage charge on the actors, which are the principal cause of demurrage at the port. It should be noted that with the implementation of the above recommendations by the different actors, demurrage will reduce in the Douala port and many African countries.

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Short Term Forecast of Container Throughput: New Variables Application for the Port of Douala

Cited by 12 publications

References 27 publications

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Applications of deep learning into supply chain management: a systematic literature review and a framework for future research

Mathematical Programming Formulations for the Berth Allocation Problems in Container Seaport Terminals

Strategies for Reducing Demurrage at the Douala Container Terminals (RTC Terminal) during Importation

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