An insurance risk management framework for disaster relief and supply chain disruption inventory planning

Lodree, Emmett J.; Taskin, Selda

doi:10.1057/palgrave.jors.2602377

Cited by 136 publications

(51 citation statements)

References 32 publications

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“…In particular, resilience has been used in examining responses to major supply chain disruptions and disaster relief efforts (Tomlin, 2006;Lodree Jr. and Taskin, 2007;Ratick et al, 2008;Falasca et al, 2008;Boin et al, 2010). This implies the strategic planning and positioning of supply chain parties.…”

Section: Supply Chain Resiliencementioning

confidence: 99%

“…Most existing studies have provided more qualitative insights to the problem and focus more on identifying sources of risks and on determining mitigation and contingency strategies. Quantitative researchers have focused on reducing the likelihood of the occurrence of disruptive events and/or developing means of overcoming disruptions if such events occur (Tomlin, 2006;Lodree Jr. and Taskin, 2007;Wilson, 2007;Tang and Tomlin, 2008;Mitra et al, 2009;Skipper and Hanna, 2009;Wagner and Neshat, 2010). They do not explicitly state that supply chain resilience is achieved or measured at any point in their work.…”

Section: Trade-off Between Resilience and Costmentioning

confidence: 99%

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A control engineering approach to the assessment of supply chain resilience

Spiegler

Naim

Wikner

2012

International Journal of Production Research

269

141

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There is no consensus on the supply chain management definition of resilience. To aid in evaluating the dynamic behaviour of such systems we need to establish clearly elucidated performance criteria that encapsulate the attributes of resilience. A literature review establishes the latter as readiness, responsiveness and recovery. We also identify robustness as a necessary condition that would complement resilience. We find that the Integral of the Time Absolute Error (ITAE) is an appropriate control engineering measure of resilience when it is applied to inventory levels and shipment rates. We use the ITAE to evaluate an often used benchmark model of make-to-stock supply chains consisting of three decision parameters. We use both linear and non-linear forms of the model in our evaluation. Our findings suggest that optimum solutions for resilience do not yield a system that is robust to uncertainties in lead-time. Hence supply chains will experience drastic changes in their resilience performance when lead-time changes.

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Section: Supply Chain Resiliencementioning

confidence: 99%

Section: Trade-off Between Resilience and Costmentioning

confidence: 99%

A control engineering approach to the assessment of supply chain resilience

Spiegler

Naim

Wikner

2012

International Journal of Production Research

269

141

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“…Lodree and Taskin (2008) showed in their study of inventory policy for an imminent hurricane, that the inability to forecast demand directly impacts inventory policy as it is difficult to estimate inventory requirements due to the disaster. Balcik and Beamon (2008) showed that a larger predisaster budget allowed for the storage of more relief inventory, which improved the responsiveness of the relief supply chain.…”

Section: Framework Of Factors Affecting Inventory Pre-positioningmentioning

confidence: 99%

Conceptualising Inventory Prepositioning in the Humanitarian Sector

Richardson¹,

Leeuw²,

Vis³

2010

IFIP Advances in Information and Communication Technology

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Abstract. Improved responsiveness to natural and man-made disasters is critical to saving lives and alleviating the suffering caused by such disasters. Emphasis on the design of the relief chain to reduce delivery time of relief inventory improves responsiveness. This is the essence of inventory prepositioning (IPP). IPP is yet to be clearly defined; and the main factors affecting IPP decision-making need to be further discussed in the humanitarian literature. Quantitative factors such as costs usually take precedence over qualitative factors such as collaboration, infrastructure and others that can substantially impact IPP decisions. This can lead to ineffective decision-making in humanitarian organizations. This paper provides a definition of IPP by reviewing relevant literatures; and a conceptual framework of the main factors affecting IPP decision-making specific to the humanitarian sector, developed through desk reviews and observations.

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“…However, there are considerably several studies that address different aspects of disaster relief logistics. These include location (e.g., Drezner et al [5] on casualty collection points; Doerner et al [6] on shelter/temporary housing location and/or allocation; Akkihal [7], Jia et al [8], and Balcik and Beamon [9] on location of RDCs; and Berman et al [10,11] on transfer point location), location-allocation (see for instance, Sherali et al [12], Tzeng et al [13]), inventory aspects (e.g., Beamon and Kotleba [14]; Whybark [15]; Lodree and Taskin [16]), relief distribution (e.g., Haghani and Oh [17]; Viswanath and Peeta [18]; Ozdamar et al [19]; Barbarosoglu and Arda [20]; Amiri [21]; Yi and Kumar [22]; Sheu [23]; Campbell et al [24] and Yuan and Wang [25]), evacuation process planning (see, for instance, Han et al [26]; Yi and Kumar [22]; Yi and Ozdamar [27]; Ozdamar and Yi [28]; Regnier [29]; Chiou and Lai [30]; Stepanov and Smith [31] and Saadatseresht et al [32]); and location routing (see, for instance, Yi and Ozdamar [27]; Ukkusuri and Yushimito [33]). …”

Section: Introductionmentioning

confidence: 99%

A modified particle swarm optimization for disaster relief logistics under uncertain environment

Bozorgi-Amiri

Jabalameli

Alinaghian

et al. 2011

Int J Adv Manuf Technol

133

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Relief logistics is one of the most important elements of a relief operation. This paper investigates a relief chain design problem where not only demands but also supplies and the cost of procurement and transportation are considered as the uncertain parameters. Furthermore, the model considers uncertainty for the locations where those demands can arise and the possibility that a number of the facility could be partially destroyed by the disaster. The proposed model for this study is formulated as a mixed-integer nonlinear programming to minimize the sum of the expected total cost (which includes costs of location, procurement, transportation, holding, and shortage) and the variance of the total cost. The model simultaneously determines the location of relief distribution centers and the allocation of affected area to relief distribution centers. Furthermore, an efficient solution approach based on particle swarm optimization is developed in order to solve the proposed mathematical model. At last, computational results for several instances of the problem are presented to demonstrate the feasibility and effectiveness of the proposed model and algorithm.

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An insurance risk management framework for disaster relief and supply chain disruption inventory planning

Cited by 136 publications

References 32 publications

A control engineering approach to the assessment of supply chain resilience

A control engineering approach to the assessment of supply chain resilience

Conceptualising Inventory Prepositioning in the Humanitarian Sector

A modified particle swarm optimization for disaster relief logistics under uncertain environment

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