A new model to mitigating random disruption risks of facility and transportation in supply chain network design

Azad, Nader; Davoudpour, Hamid; Saharidis, Georgios K. D.; Shiripour, Morteza

doi:10.1007/s00170-013-5404-0

Cited by 60 publications

(29 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Constraints (11)(12) calculate the amount of budget for outsourcing facility's functions when disruptions occurred at manufacturers and distribution centers, respectively. Constraints (13)(14) enforce the amount of protection resources invested in all facilities to be less than or equal to the total protection budget. Constraint (15) shows the non-negativity restriction on decision variables while constraint (16) ensures the binary nature of decision variables.…”

Section: Model Formulationmentioning

confidence: 99%

A Bi-Objective Programming Model for Reliable Supply Chain Network Design under Facility Disruption

Hatefi

Moshashaee²,

Mahdavi³

2019

IJIE

View full text Add to dashboard Cite

Section: Model Formulationmentioning

confidence: 99%

A Bi-Objective Programming Model for Reliable Supply Chain Network Design under Facility Disruption

Hatefi

Moshashaee²,

Mahdavi³

2019

IJIE

View full text Add to dashboard Cite

“…Another main decision that needs to be made to devise a strategy is to define what decisions must be made a priori, and which ones can be made later, once the set of available facilities is known, in order to adapt the a priori solution to the actual situation. In this context, several alternatives can be considered depending on the flexibility of the supply chain, ranging from the high flexibility of Gade and Pohl [2009], Aydin and Murat [2013], or Qin et al [2013], where all assignments can be freely modified in the case of disruptions, to the rigid situation of Azad et al [2014], where customers only have one assignment for regular operating mode, and one assignment for emergencies.…”

Section: Notation and Modeling Assumptionsmentioning

confidence: 99%

“…Another work addressing capacitated facilities is Azad et al [2014]. Here, the authors consider quite a general model where disruptions can occur both, in the facilities and in the transportation network.…”

Section: Introductionmentioning

confidence: 99%

Introducing capacities in the location of unreliable facilities

Albareda-Sambola

Landete

Monge

et al. 2017

European Journal of Operational Research

View full text Add to dashboard Cite

The goal of this paper is to introduce facility capacities into the Reliability Fixed-Charge Location Problem in a sensible way. To this end, we develop and compare different models, which represent a tradeoff between the extreme models currently available in the literature, where a priori assignments are either fixed, or can be fully modified after failures occur. In a series of computational experiments we analyze the obtained solutions and study the price of introducing capacity constraints according to the alternative models both, in terms of computational burden and of solution cost.

show abstract

“…A firm suffering disruptions may only lose a fraction instead of the whole of its production capacity [18]. For each disruption, the start time, duration and the loss fraction of production capacity (without loss of generality, it is called as loss) are randomly distributed.…”

Section: Bmentioning

confidence: 99%

A model to evaluate risk propagation considering effect of dynamic risk information sharing and multi-sourcing in supply chain networks

Ponnambalam

et al. 2015

2015 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

View full text Add to dashboard Cite

The cost-optimal trend in global competitive environment has led to lean, complex and consequently vulnerable supply chains. This vulnerability is high due to the presence of increasing dependency in lean supply chain networks. Hence, it is essential for companies to regulate the supply chain network structure and information sharing strategies so as to be more resilient to disruptions/risks. In order to achieve this objective, it is imminent to analyze as to how best to quantify and examine the robustness of their supply chain networks under disruptions.Here, we propose an analytical model to assist companies to evaluate dynamic risk propagation and quantify likely disruption loss in supply chain networks with the consideration of the effect of multiple sourcing and information sharing on risk propagation. Mean loss and Value at risk (VaR) is employed to quantify the potential loss due to disruptions.

show abstract

A new model to mitigating random disruption risks of facility and transportation in supply chain network design

Cited by 60 publications

References 36 publications

A Bi-Objective Programming Model for Reliable Supply Chain Network Design under Facility Disruption

A Bi-Objective Programming Model for Reliable Supply Chain Network Design under Facility Disruption

Introducing capacities in the location of unreliable facilities

A model to evaluate risk propagation considering effect of dynamic risk information sharing and multi-sourcing in supply chain networks

Contact Info

Product

Resources

About