Optimal supply chain resilience with consideration of failure propagation and repair logistics

Goldbeck, Nils; Angeloudis, Panagiotis; Ochieng, Washington

doi:10.1016/j.tre.2019.101830

Cited by 75 publications

(30 citation statements)

References 57 publications

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“…The ability of the supply chain to identify the existence of possible disruptions and develop actions to avoid or diminish their possible effects. 16 6.89% [12,26,29,31,33,37,46,131,136,157,165,169,170,172,218,225] Knowledge Management…”

Section: Disruptive Environment Awarenessmentioning

confidence: 99%

A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain

2020

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The challenges of global economies foster supply chains to have to increase their processes of collaboration and dependence between their nodes, generating an increase in the level of vulnerability to possible impacts and interruptions in their operations that may affect their sustainability. This has developed an emerging area of interest in supply chain management, considering resilience management as a strategic capability of companies, and causing an increase in this area of research. Additionally, supply chains should deal with the three dimensions of sustainability (economic, environmental, and social dimensions) by incorporating the three types of objectives in their strategy. Thus, there is a need to integrate both resilience and sustainability in supply chain management to increase competitiveness. In this paper, a systematic literature review is undertaken to analyze resilience management and its connection to increase supply chain sustainability. In the review, 232 articles published from 2000 to February 2020 in peer-reviewed journals in the Scopus and ScienceDirect databases are analyzed, classified, and synthesized. With the results, this paper develops a conceptual framework that integrates the fundamental elements for analyzing, measuring, and managing resilience to increase sustainability in the supply chain. Finally, conclusions, limitations, and future research lines are exposed.

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Section: Disruptive Environment Awarenessmentioning

confidence: 99%

A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain

2020

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“…For example, the phased‐mission behavior has recently been modeled using an extended object‐oriented Petri net considering failed task reexecution 30 and universal generating functions considering performance sharing and transmission loss 31 in the physical domain. The failure propagation behavior has recently been examined for critical infrastructure systems, 32 supply chains, 33 and series‐parallel systems 34 . The common‐cause failure behavior has been investigated using Bayesian networks 35 and a survival signature‐based method for satellite systems 36 .…”

Section: Illustrative Examplementioning

confidence: 99%

A behavior‐driven reliability modeling method for complex smart systems

Xing

Zhao

Xiang

et al. 2021

Quality & Reliability Eng

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With recent advancements in Internet technologies and wireless communications, wireless sensor network (WSN)‐based smart systems are gaining an enormous increase in use in various applications (eg, healthcare, smart home, smart manufacturing, smart power grids, and smart transportation). Due to the mission‐critical or safety‐critical nature of smart system applications, it is imperative that a smart system be reliable during its mission time. However, reliability analysis and design of smart systems are still open challenging research problems due to complicated dependencies existing in the WSN domain or the physical domain monitored by the WSN, and due to dependencies crossing the two domains. This paper proposes a new behavior‐driven reliability modeling methodology for accurate and efficient reliability analysis of complex WSN‐based smart systems, contributing toward their robust designs and operations. The suggested method can address effects of dependent behaviors affecting different domains of the smart system in a combinatorial manner. It also enables the use of efficient single‐domain reliability methods to retain their efficiency. A case study of a smart home system is performed to demonstrate the application of the proposed method as well as its advantages in handling nonexponential time‐to‐failure distributions and in analyzing smart systems with complicated intradomain and cross‐domain dependencies.

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“…For instance, Jabbarzadeh focused on facility location and allocation decisions and proposed a practical optimization model for multiple postdisaster periods [32]. Furthermore, Goldbeck et al took the relations between resource allocation and the recovery speed into account, which are not considered in most models, and proposed a novel multistage stochastic programming model that optimizes predisruption investment decisions with postdisruption dynamic adjustment mechanisms for supply chain operations and the allocation of repair resources [33], and Zhou et al noted that postdisaster resource distribution is an important part of emergency resource scheduling, so a multiobjective optimization model for multiperiod dynamic emergency resource scheduling (ERS) problems was established, and a novel framework involving a multiobjective evolutionary algorithm based on decomposition (MOEA/D) was used to solve this model [34]. These studies made great efforts to establish resource allocation in multiple periods; however, the resources' type and coverage are not considered in these model, so Iloglu et al concentrated on the coverage in the recovery of physically damaged infrastructure and the allocation of resources after a disaster and built a maximum multiple coverage and network recovery model for the recovery and restoration of infrastructure systems after disasters [16].…”

Section: Resource Allocationmentioning

confidence: 99%

An Equilibrium Service Capability Recovery Model for a Port-Hinterland Service Network With Delay or Disruption Risk

2021

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This paper aims to propose a comprehensive equilibrium service capability recovery model to effectively recover the service capability of a port-hinterland service network with delay or disruption risk under limited resources. We first analyze the risk factors and generation mechanism of a port-hinterland network and then introduce a differential evolution algorithm and project pursuit method to determine the risk resistance level of service nodes and regions in a port-hinterland service network for resource allocation and service capability recovery based on the selected indicators. An equilibrium utility model is used to control the equilibrium of the service capability recovery within nodes and regions in the porthinterland service network, and the multinomial logit model is used to calculate the service path choice probability. Finally, we build this model, taking the port-hinterland service network of three provinces in Northeast China as examples to validate the model, which is solved with an elite genetic algorithm, and then perform comparisons of four scenarios. The results show that (1) external factors more easily induce delay or disruption risk than do internal factors; (2) the strategy considering equilibrium recovery is better than that without considering equilibrium recovery; (3) the strategy considering both equilibrium among the regions and inside the regions is better than that considering only region equilibrium or internal equilibrium; and (4) the strategy considering region equilibrium is better than that considering internal equilibrium.

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Optimal supply chain resilience with consideration of failure propagation and repair logistics

Cited by 75 publications

References 57 publications

A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain

A Conceptual Framework to Manage Resilience and Increase Sustainability in the Supply Chain

A behavior‐driven reliability modeling method for complex smart systems

An Equilibrium Service Capability Recovery Model for a Port-Hinterland Service Network With Delay or Disruption Risk

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