A replenishment model for the supply-uncertainty problem

Mohebbi, Esmail

doi:10.1016/s0925-5273(03)00098-7

Cited by 58 publications

(29 citation statements)

References 29 publications

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“…They considered the lost-sales case and also discussed the backorder case, for both the discounted and long-run AC criteria. Mohebbi (2004) considered a continuous-review inventory system with compound Poisson demand, hyper exponentially distributed lead time, and lost sales where the supply process might be randomly interrupted depending on the availability of a supplier. He presented an exact formulation of the long-run AC rate function based on deriving the stationary distribution of the on-hand inventory, and provided some numerical results.…”

Section: Literature Reviewmentioning

confidence: 99%

Development of an inventory model for two suppliers with random capacity considering supply disruption

Ahmed

Sultana

Azeem

2017

IJLSM

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Supply disruption occurs for diverse reasons including transportation problem, equipment failure, raw material shortages, natural calamities etc. As a result supplier may become unavailable at random times for random time length. In addition, suppliers may also have random capacities leading to uncertain yield in orders. Again under random supplier capacities, the retailer should order from a number of suppliers in order to diversify the risk associated with shortages. The objective of this paper is to develop an inventory model for two suppliers with random capacities considering supply disruption. A modified (Q, r) model is developed to tackle the problem of future supply uncertainty in response to the demand generated by Poisson process. Two suitable optimisation algorithms are applied to search for the optimal values of the decision variables which are state dependent order quantities and reorder point to minimise the cost per unit time. A hypothetical example and its solution are then provided to have a better understanding about the demonstration of the proposed model. Finally, sensitivity analysis is also carried out to have better insights about the model developed.

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Section: Literature Reviewmentioning

confidence: 99%

Development of an inventory model for two suppliers with random capacity considering supply disruption

Ahmed

Sultana

Azeem

2017

IJLSM

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“…The term "supplier reliability" may refer to a number of attributes ranging from availability in responding to re-plenishment orders, when needed, to variability in delivery lead times as well as the quality of delivered goods. The availability of a supplier can be negatively impacted by a variety of factors including equipment breakdowns, material shortages, capacity constraints, price inflations, strikes, embargos and political crises thereby disrupting the supply process (Mohebbi 2004). Silver (1981) appears to be one of the first authors to indicate the need for models dealing with supply uncertainty.…”

Section: Supplier's Reliabilitymentioning

confidence: 99%

“…The significance of modelling the issue of supply interruption is due to the severity of its potential negative impact on the performance of supply chains in every competitive business market (Mohebbi 2004).…”

Section: Supplier's Reliabilitymentioning

confidence: 99%

“…He uses independent non-identical exponential distributions to describe the supplier's on/off periods and applies an (s, Q)-type control policy while allowing for a maximum of one order to be outstanding at any time. Mohebbi (2004) considers a similar problem but extended the earlier analysis to a larger family of lead time distributions, using a fairly general stochastic process to describe the supplier's availability.…”

Section: Supplier's Reliabilitymentioning

confidence: 99%

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A simulation optimisation approach for inventory management decision support based on incomplete information

Ramaekers

2008

4OR-Q J Oper Res

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“…There is a stream of research in the inventory literature that considers supply disruptions in the context of classical inventory models, such as the EOQ (Parlar and Berkin [72], Berk and Arreola-Risa [5], Snyder [91]), (Q, R) (Gupta [42], Parlar [71], Mohebbi [64,65]), and (s, S) (Arreola-Risa and DeCroix [1]) models. More recent models examine a range of strategies for mitigating disruptions, including dual sourcing (Tomlin [100]), demand management (Tomlin [99]), supplier reliability forecasting (Tomlin [98], Tomlin and Snyder [101]), and productmix flexibility (Tomlin and Wang [102]).…”

Section: Inventory Models With Supply Disruptionsmentioning

confidence: 99%

Planning for Disruptions in Supply Chain Networks

Snyder¹,

Scaparra²,

Daskin³

et al. 2006

Models, Methods, and Applications for Innovative Decision Making

169

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Recent events have highlighted the need for planners to consider the risk of disruptions when designing supply chain networks. Supply chain disruptions have a number of causes and may take a number of forms. Once a disruption occurs, there is very little recourse regarding supply chain infrastructure because these strategic decisions cannot be changed quickly. Therefore, it is critical to account for disruptions during the design of supply chain networks so that they perform well even after a disruption. Indeed, these systems can often be made substantially more reliable with only small additional investments in infrastructure. Planners have a range of options available to them in designing resilient supply chain networks, and their choice of approaches will depend on the financial resources available, the decision maker's risk preference, the type of network under consideration, and other factors. In this tutorial, we present a broad range of models for designing supply chains resilient to disruptions. We first categorize these models by the status of the existing network: A network may be designed from scratch, or an existing network may be modified to prevent disruptions at some facilities. We next divide each category based on the underlying optimization model (facility location or network design) and the risk measure (expected cost or worst-case cost).

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A replenishment model for the supply-uncertainty problem

Cited by 58 publications

References 29 publications

Development of an inventory model for two suppliers with random capacity considering supply disruption

Development of an inventory model for two suppliers with random capacity considering supply disruption

A simulation optimisation approach for inventory management decision support based on incomplete information

Planning for Disruptions in Supply Chain Networks

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