Control and Backbone Identification for the Resilient Recovery of a Supply Network Utilizing Outer Synchronization

Liang Geng,Renbin Xiao

doi:10.3390/app10010313

Abstract

The control and measurement for resilient recovery is important for a supply network facing disruption. Outer synchronization is useful for the supply network to recover to its scheduled state. In this paper, a dynamic model for a supply network is established, and measurement with memory of resilient recovery is proposed based on outer synchronization. An impulsive controller is designed to improve the control effectiveness. Afterwards, an algorithm is adopted to identify the resilient recovery backbone. Based on these factors, an efficient resilient recovery method considering cost is applied in the case study. This study improves the measurement and control of the supply network’s resilient recovery through outer synchronization, and is easily integrated with practical problems to make better control decisions.

Highlights

With the development of economic globalization, the increasing vulnerability of supply networks (SN) has drawn substantial attention to risk management [1,2]
Network resilience can be defined in two fundamental dimensions: (i) vulnerability, or the lack of ability of a network to withstand disruptive events and maintain its maximum possible level of performance in the immediate aftermath of disruptions; and (ii) recoverability, or the ability of the network to return to a desired level of performance within a recovery time horizon [8]
The resilience capacity of a system is defined as a function of three capacities: (i) absorptive capacity, or the extent to which a network is able to absorb shocks from disruptive events; (ii) adaptive capacity, or the extent to which a system can quickly adapt after a disruption by temporary means; and (iii) restorative capacity, or the extent to which the system can recover from a disruption or be reconstructed in the long-term [9]

Summary

Introduction

With the development of economic globalization, the increasing vulnerability of supply networks (SN) has drawn substantial attention to risk management [1,2]. A resilient SN is able to cope with (or react to) unexpected disruptions and recover quickly to the planned predisaster state [7], which results in the evolution of new practices to construct resilient SNs. Network resilience can be defined in two fundamental dimensions: (i) vulnerability, or the lack of ability of a network to withstand disruptive events and maintain its maximum possible level of performance in the immediate aftermath of disruptions; and (ii) recoverability, or the ability of the network to return to a desired level of performance within a recovery time horizon [8]. The resilience capacity of a system is defined as a function of three capacities: (i) absorptive capacity, or the extent to which a network is able to absorb shocks from disruptive events; (ii) adaptive capacity, or the extent to which a system can quickly adapt after a disruption by temporary means; and (iii) restorative capacity, or the extent to which the system can recover from a disruption or be reconstructed in the long-term [9]

Objectives

Methods

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Conclusion