Recovery strategies from major supply disruptions in single and multiple sourcing networks

Azad, Nader; Hassini, Elkafi

doi:10.1016/j.ejor.2018.11.044

Cited by 37 publications

(17 citation statements)

References 88 publications

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“…Similar to this work, considering a committed and a responsive pricing strategy under which the retailer adjusts the sales price before and after the supply state is realised, Wang and Yu (2020) investigate whether a supply-side contingent sourcing strategy should be jointly adopted with a demand-side pricing strategy to mitigate supply disruption. Considering dynamic pricing as a recovery lever to manage demand during supply disruptions, and under the assumption that demand can be satisfied via a partially disrupted production facility or outsourcing, Azad and Hassini (2019) develop a portfolio of recovery strategies by a programming model and a Benders decomposition algorithm. The strategies incorporate pricing, transportation rerouting, and outsourcing.…”

Section: Combination Strategiesmentioning

confidence: 99%

Dynamic compensation and contingent sourcing strategies for supply disruption

Minner

2020

International Journal of Production Research

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Section: Combination Strategiesmentioning

confidence: 99%

Dynamic compensation and contingent sourcing strategies for supply disruption

Minner

2020

International Journal of Production Research

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“…Equation (18) indicates that the allocated resources at nodes in the hinterland area and for intermediate carriers and ports in region m are not greater than the total resources allocated to region m . Equation (19) demonstrates that the recovered service capability of each node in the PHSN is no greater than the initial service capability. Equation (20) guarantees that the recovery proportion of the service capability is less than 1.…”

Section: ) Model Constructionmentioning

confidence: 99%

“…However, research on postevent response has focused on the reorganization of the existing service capability to best optimize the entire network, and the effects of node service capability recovery and the service choice probability on service path choice have been ignored. A few studies have explored service capability recovery in PHSNs, mainly in the supply chain management and emergency resource allocation fields [16,17], in which the recovery time stage and recovery efficiency are central problems [18][19][20]. However, the relevant studies have neglected the risk resistance and recovery equilibrium of nodes or regions in PHSNs, which has led to unbalanced recovery resource allocation and reduced recovery efficiency.…”

Section: Introductionmentioning

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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“…Nevertheless, there are expectations for disruption recovery methods. Since disruptions refer to catastrophic events that are difficult to predict and control, it seems impossible to eliminate them no matter how many precautionary actions have been implemented [46]. In such cases, appropriate recovery strategies are more suitable for tackling these unpredictable disruption risks and making the supply network more resilient [47].…”

Section: Related Workmentioning

confidence: 99%

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

2021

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With the increasing reliance on global sourcing and the growth in the likelihood of disruptive incidents, today’s supply networks are more prone to unexpected natural and man-made disruptive events. In order to alleviate the losses caused by these disruptive events, when a large-scale event disrupts multiple suppliers simultaneously, a single or several critical suppliers should be selected from the disrupted ones to assist them to recover their production as soon as possible. The selection of these recovery suppliers is of great importance in the recovery process of the entire supply network. Thus, this paper proposes a recovery supplier selection method from the view of the supply network structure. Firstly, a tripartite graph-based supply model is proposed to depict a two-stage supply network, which consists of multiple manufacturers and suppliers as well as the diverse product supply-demand interdependence connecting them. To measure the impacts caused by supplier disruptions and to evaluate the effectiveness of recovery supplier decisions, two supply network performance metrics reflecting product supply availability are also given. Then, the recovery supplier selection problem is described as a combinatorial optimization problem. To solve this problem, a heuristic algorithm, with enhanced variable neighborhood search (EVNS) is designed based on the general framework of a variable neighborhood search. Finally, experiments based on a real-world supply network are conducted. The experimental results indicate that the proposed method is applicable and effective.

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Recovery strategies from major supply disruptions in single and multiple sourcing networks

Cited by 37 publications

References 88 publications

Dynamic compensation and contingent sourcing strategies for supply disruption

Dynamic compensation and contingent sourcing strategies for supply disruption

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

Enhanced Variable Neighborhood Search-Based Recovery Supplier Selection for Post-Disruption Supply Networks

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