A decision support system for configuring spare parts supply chains considering different manufacturing technologies

Cantini, Alessandra; Peron, Mirco; Carlo, Filippo De; Sgarbossa, Fabio

doi:10.1080/00207543.2022.2041757

Cited by 52 publications

(22 citation statements)

References 82 publications

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“…The keywords are excluded because they are not consistently reported throughout the articles. A preprocessing of the corpus was crucial for achieving a better quality analysis of the results (Cantini et al., 2022; Denny & Spirling, 2017; Simonetto et al., 2021; Uysal & Gunal, 2014). It included: transformation to lower case, removal of accents, detection of html tags to parse out text only, and removal of urls; tokenization to break the text into smaller components; normalization for stemming and lemmatization to words; filtering to remove a selection of stop‐words. …”

Section: Methodsmentioning

confidence: 99%

Risk assessment for handling hazardous substances within the European industry: Available methodologies and research streams

et al. 2022

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After the Seveso disaster occurred more than 40 years ago, there has been an increasing awareness of the potential impacts that similar accident events can occur in a wide range of process establishments, where the handling and production of hazardous substances pose a real threat to society and the environment. In these industrial sites denominated "Seveso sites," the urgent need for an effective strategy emerged markedly to handle hazardous activities and to ensure safe conditions. Since then, the main challenging research issues have focused on how to prevent such accident events and how to mitigate their consequences leading to the development of many risk assessment methodologies. In recent years, researchers and practitioners have tried to provide useful overviews of the existing risk assessment methodologies proposing several reviews. However, these reviews are not exhaustive because they are either dated or focus only on one specific topic (e.g., liquefied natural gas, domino effect, etc.). This work aims to overcome the limitations of the current reviews by providing an up-to-date and comprehensive overview of the risk assessment methodologies for handling hazardous substances within the European industry. In particular, we have focused on the current techniques for hazards and accident scenarios identification, as well as probability and consequence analyses for both onshore and offshore installations. Thus, we have identified the research streams that have characterized the activities of researchers and practitioners over the years, and we have then presented and discussed the different risk assessment methodologies available concerning the research stream that they belong to.

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Section: Methodsmentioning

confidence: 99%

Risk assessment for handling hazardous substances within the European industry: Available methodologies and research streams

et al. 2022

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“…Ben-Tal [21] pointed out that small perturbations can lead to large errors, and can even lead to no feasible solution for the model. Robust optimization is now widely used in many fields [22][23][24], such as the consensus field [25][26][27][28][29][30], supply chain management [31,32], the energy field [33], etc.…”

Section: The Evolution Of the Data-driven Robust Dea Modelmentioning

confidence: 99%

“…It can be seen that it is necessary to consider robust optimization when the environment is uncertain. Robust optimization is now widely used in many fields [22][23][24], such as the consensus field [25][26][27][28][29][30], supply chain management [31,32], the energy field [33], etc. Some scholars have combined robust optimization and DEA [34].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

et al. 2022

Sustainability

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To combat global warming, China proposed the “dual carbon” policy in 2020. In this context, it becomes crucial to improve carbon emissions efficiency. Currently, some scholars have utilized data envelopment analysis (DEA) to study carbon emissions efficiency. However, uncertainty about climate and government economic policy is ignored. This paper establishes a robust DEA model to reduce uncertainty and improve robustness. First, robust optimization theory is combined with DEA to establish the robust DEA model. Second, considering three uncertainty sets (box set, ellipsoid set, and polyhedron set), a robust DEA model for different situations is considered. Finally, to address the problem of over-conservatism in robust optimization, this paper applies the data-driven robust DEA model to further analyze the carbon emissions efficiency of China. The results of the data-driven robust DEA model suggest that the government should focus on coordinated regional development, promote the transformation and upgrading of the energy structure, innovate in green technology, and advocate for people to live a green and low-carbon lifestyle.

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“…Uncertainty in suppliers' product supply is a common phenomenon in supply chain management, and, to a certain extent, it can cause losses to firms [2]. A well-configured supply chain (SC) can reduce costs and increase the competitiveness of a firm [3]. Therefore, to reduce the risk of raw material supply chain breakage, downstream manufacturers often avoid adopting the raw material procurement strategy of "putting eggs in one basket" but sign long-term minimum supply purchase agreements with leading suppliers and also purchase raw materials through various channels.…”

Section: Introductionmentioning

confidence: 99%

Multi-Oligopoly Sequential Pricing Mechanisms and Their Game Analysis in Raw Material Supply Chains

2022

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The sequential pricing game model is an approach that can be effectively used to solve the problem with multi-oligopoly pricing mechanisms in raw material supply chains. However, the existing sequential pricing mechanism does not fully consider constraints such as the purchase volume of downstream firms and the change information of each parameter, which leads to the pricing mechanism being detached from the real market. According to the concept of the sequential pricing game model being used among multi-oligopolies under constraints, we constructed the constrained sequential pricing game model by incorporating the parameters related to the product demand function, marginal production cost, dominant coefficient, following coefficient, and agreed minimum purchase volume as constraints, and the model was converted into a nonlinear bilevel programming model to facilitate model solving. Furthermore, we provided the analytical solution formulas for six special cases, thus making the model more similar to the real market. In addition, the effects of the agreed minimum purchase volume and the dominant and following coefficients on the equilibrium quoted prices and profits of the firms were analyzed. The results of the numerical simulation show that the constrained sequential pricing game model is more effective than the unconstrained sequential pricing game model in solving the problem with the multi-oligopoly pricing mechanism, which means that it can be used to establish a better pricing mechanism and provide a more reasonable and scientific basis for market operation and policymakers in solving practical problems.

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A decision support system for configuring spare parts supply chains considering different manufacturing technologies

Cited by 52 publications

References 82 publications

Risk assessment for handling hazardous substances within the European industry: Available methodologies and research streams

Risk assessment for handling hazardous substances within the European industry: Available methodologies and research streams

Data-Driven Robust Data Envelopment Analysis for Evaluating the Carbon Emissions Efficiency of Provinces in China

Multi-Oligopoly Sequential Pricing Mechanisms and Their Game Analysis in Raw Material Supply Chains

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