Robust supply chain networks design and ambiguous risk preferences

Yu, Guodong; Li, Fēi; Yang, Yong

doi:10.1080/00207543.2016.1232499

Cited by 29 publications

(19 citation statements)

References 34 publications

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“…In the last decade, reasons for disruptions in SCs have been extensively investigated. Numerous studies including (but not limited to) the works of Hendricks and Singhal (2005); Kleindorfer and Saad (2005); Tomlin (2006); Craighead et al (2007); Blackhurst et al (2011); Bode et al (2011); Nair and Vidal (2011), Simangunsong et al (2012), Pettit et al (2013); Tang et al (2014); Ivanov et al (2014a,b), Ambulklar et al 2015 (2017), Khalili et al (2017), Yu et al (2017) revealed basic reasons for the disruptions and their impact on SC execution and performance.…”

Section: Literature Selectionmentioning

confidence: 99%

Literature review on disruption recovery in the supply chain

Ivanov

Dolgui

Sokolov

et al. 2017

International Journal of Production Research

545

318

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HAL is a multidisciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Section: Literature Selectionmentioning

confidence: 99%

Literature review on disruption recovery in the supply chain

Ivanov

Dolgui

Sokolov

et al. 2017

International Journal of Production Research

545

318

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“…Along with the study by Yoon, Talluri, et al (2018), Sawik's studies suggested models that integrate supplier selection and risk mitigation strategy selection. An alternative approach was taken in the study by Yu, Li, and Yang (2017) that applies robust stochastic optimisation to SC design. Based on the regret minimisation method, their findings allow the balancing of the conservativeness of a pure robust optimisation model and the optimism of risk-neutrality.…”

Section: Research Focusmentioning

confidence: 99%

“…buffer capacities, backup suppliers, or risk mitigation inventory) at the pre-disruption stage, resilience deals with the system's ability to sustain or restore its functionality and performance following a significant change in the system and environment conditions (Aven 2017). SC resilience encompasses both proactive and reactive stages (Bhamra, Dani, and Burnard 2011;Jüttner and Maklan 2011;Spiegler, Naim, and Wikner 2012;Pettit, Croxton, and Fiksel 2013;Brandon-Jones et al 2014;Ambulkar, Blackhurst, and Grawe 2015;Tukamuhabwa et al 2015;Chowdhury and Quaddus 2017;Yu, Li, and Yang 2017). As such, an integration of pro-and reactive decisions is important for increasing SC resilience by utilising the synergetic effects between mitigation and contingency policies (Sheffi 2005;Tomlin 2006;Melnyk et al 2014;Ivanov et al 2016;Geng and Xiao 2017;Ivanov and Rozhkov 2017;Rezapour, Farahani, and Pourakbar 2017;Ivanov 2018a;Ivanov, Dolgui, and Sokolov 2018).…”

Section: Introductionmentioning

confidence: 99%

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

Ivanov

Dolgui

2018

International Journal of Production Research

255

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This study suggests a new approach to supply chain (SC) disruption risk management where SC behaviour is less dependent on the certainty of our knowledge about the environment and its changes. The unpredictability of the occurrence of disruption and its magnitude suggests that designing SCs with a low need for 'certainty' may be as important, if not more so, than predetermined disruption control strategies. In this setting, this study calls for the development of a new perspective in SC disruption management, i.e. low-certainty-need (LCN) SCs. A number of principles and concepts is derived in recent, relevant literature to structure the characteristics of the LCN framework and its management. Structural variety, process flexibility, and parametrical redundancy are identified as key LCN SC characteristics that ensure efficient disruption resistance as well as recovery resource allocation. Two efficiency capabilities of the LCN SC are shown, i.e. low need for uncertainty consideration in planning decisions and low need for recovery coordination efforts based on a combination of lean and resilient elements. The results allow the identification of an LCN SC framework, concepts and technologies for its implementation as well as missing themes and new research questions which contribute to a better understanding of SC disruption risks. Special focus is directed on the digital technology usage in the LCN framework implementation.

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“…et al [8] developed a multistage inexact stochastic robust model for regional energy system management in Jining City, China. However, the robust approach is usually criticized by its overly conservative results, which may lead to excessive investments [23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Before proposing our risk-averse model, we briefly review the concept of risk measurement. Coherent risk measures have been considered extensively in the literature (for more details, see [25]). Mapping ρ : L → R ∪ {+∞} is coherent if it satisfies the following properties for random variables Z 1 and Z 2 .…”

Section: Risk-averse Modelmentioning

confidence: 99%

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

Zhao

Xia

2019

Energies

Self Cite

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A groundswell of opinion in utilizing environmentally friendly energy technologies has been put forth worldwide. In this paper, we consider an energy generation plant distribution and allocation problem under uncertainty to get the utmost out of available developments, as well as to control costs and greenhouse emissions. Different clean and traditional energy technologies are considered in this paper. In particular, we present a risk-averse stochastic mixed-integer linear programming (MILP) model to minimize the total expected costs and control the risk of CO2 emissions exceeding a certain budget. We employ the conditional value-at-risk (CVaR) model to represent risk preference and risk constraint of emissions. We prove that our risk-averse model can be equivalent to the traditional risk-neutral model under certain conditions. Moreover, we suggest that the risk-averse model can provide solutions generating less CO2 than traditional models. To handle the computational difficulty in uncertain scenarios, we propose a Lagrange primal-dual learning algorithm to solve the model. We show that the algorithm allows the probability distribution of uncertainty to be unknown, and that desirable approximation can be achieved by utilizing historical data. Finally, an experiment is presented to demonstrate the performance of our method. The risk-averse model encourages the expansion of clean energy plants over traditional models for the reduction CO2 emissions.

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Robust supply chain networks design and ambiguous risk preferences

Cited by 29 publications

References 34 publications

Literature review on disruption recovery in the supply chain

Literature review on disruption recovery in the supply chain

Low-Certainty-Need (LCN) supply chains: a new perspective in managing disruption risks and resilience

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

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