A novel hybrid method for constructing resilient microalgae supply chain: Integration of n-1 contingency analysis with stochastic modelling

Lo, Shirleen Lee Yuen; How, Bing Shen; Teng, Sin Yong; Lim, Juin Yau; Loy, Adrian Chun Minh; Lam, Hon Loong; Sunarso, Jaka

doi:10.1016/j.jclepro.2023.137939

Cited by 4 publications

(1 citation statement)

References 21 publications

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“…In [28], the authors investigated the health of an engine through experimental observation using an artificial neural network (ANN). An ANN can also be used for early fault detection [29,30]. Another interesting publication showed how to use deep learning with LPC and wavelet algorithms for driving fault diagnosis [31].…”

Section: State Of the Artmentioning

confidence: 99%

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Kuźnar,

Lorenc

2023

Applied Sciences

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In the field of transport, and more precisely in supply chains, if any of the vehicle components are damaged, it may cause delays in the delivery of goods. Eliminating undesirable damage to the means of transport through the possibility of predicting technical conditions and a state of failure may increase the reliability of the entire supply chain. From the aspect of sustainability, the issue of reducing the number of failures also makes it possible to reduce supply chain disturbances, to reduce costs associated with delays, and to reduce the materials needed for the repair of the means of transport, since, in this case, the costs only relate to the replaced elements before their damage. Thus, it is impossible for more serious damage to occur. Often, failure of one item causes damage to others, which generates unnecessary costs and increases the amount of waste due to the number of damaged items. This article provides an author’s method of technical condition prediction; by applying the method, it would be possible to develop recommended maintenance activities for key elements related to the safety and reliability of transport. The combination of at least two artificial intelligence methods allows us to achieve very good prediction results thanks to the possibility of individual adjustments of weights between the methods used. Such predictive maintenance methods can be successfully used to ensure sustainable development in supply chains.

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Section: State Of the Artmentioning

confidence: 99%

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Kuźnar,

Lorenc

2023

Applied Sciences

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Towards facing uncertainties in biofuel supply chain networks: a systematic literature review

Habibi,

Chakrabortty,

Abbasi

2023

Environ Sci Pollut Res

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Biofuel supply chains (BSCs) face diverse uncertainties that pose serious challenges. This has led to an expanding body of research focused on studying these challenges. Hence, there is a growing need for a comprehensive review that summarizes the current studies, identifies their limitations, and provides essential advancements to support scholars in the field. To overcome these limitations, this research aims to provide insights into managing uncertainties in BSCs. The review utilizes the Systematic Reviews and Meta-Analyses (PRISMA) method, identifying 205 papers for analysis. This study encompasses three key tasks: first, it analyses the general information of the shortlisted papers. Second, it discusses existing methodologies and their limitations in addressing uncertainties. Lastly, it identifies critical research gaps and potential future directions. One notable gap involves the underutilization of machine learning techniques, which show potential for risk identification, resilient planning, demand prediction, and parameter estimations in BSCs but have received limited attention. Another area for investigation is the potential of agent-based simulation, which can contribute to analysing resilient policies, evaluating resilience, predicting parameters, and assessing the impact of emerging technologies on BSC resilience in the twenty-first century. Additionally, the study identifies the omission of various realistic assumptions, such as backward flow, lateral transshipments, and ripple effects in BSC. This study highlights the complexity of managing uncertainties in BSCs and emphasizes the need for further research and attention. It contributes to policymakers’ understanding of uncertain sources and suitable approaches while inspiring researchers to address limitations and generate breakthrough ideas in managing BSC uncertainties.

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Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

Song,

Yeo,

Teng

et al. 2024

Ind. Eng. Chem. Res.

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A bilevel optimization model is developed to synthesize an optimal waste oil recycling supply chain design in economic and environmental aspects while considering its overall resiliency in addressing major disruptions. In the upper level, optimal re-refineries selection is determined based on the cost and emissions, while the “n-1” contingency is incorporated in the subsequent level to identify the production capacity required for each re-refinery to ensure the functionality of supply chains under disruption. Two regional case studies, comprising the East Malaysia and West Malaysia, are presented to evaluate the effectiveness of the “n-1” contingency strategy. The economic viability of applying “n-1” is different for both cases. Considering a one-month disruption across the asset lifespan, the model anticipates a positive economic gain of 15.79% (in East Malaysia) and 85.71% (in West Malaysia). Nevertheless, applying “n-1” contingency demonstrates better environmental performances in both cases as compared to cases without “n-1”.

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A novel hybrid method for constructing resilient microalgae supply chain: Integration of n-1 contingency analysis with stochastic modelling

Cited by 4 publications

References 21 publications

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

A Hybrid Method for Technical Condition Prediction Based on AI as an Element for Reducing Supply Chain Disruptions

Towards facing uncertainties in biofuel supply chain networks: a systematic literature review

Bilevel Optimization for Capacity Contingency Debottlenecking Considering Trade-Offs within the Waste Oil Supply Chain

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