A dynamic model for facility location in the design of complex supply chains

Thanh, Phuong Nga; Bostel, Nathalie; Péton, Olivier

doi:10.1016/j.ijpe.2007.10.017

Cited by 139 publications

(77 citation statements)

References 20 publications

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“…Outsourced products are directly delivered to customers. The capacity of external facility is assumed to be infinite (Cortinhal et al, 2015;Thanh et al, 2011Thanh et al, , 2010Thanh et al, ,2008Hinojosa et al, 2008).Capacity expansion at undisrupted facilities is not permitted during disruption onset period td 1 as it is not practically feasible to implement structural changes in short term immediately after the disruption (Melo etal., 2006 andIvanov et al, 2016).…”

Section: Problem Description and Model Formulationmentioning

confidence: 99%

“…Each facility per period can add no more than and no less than1/4 and 1/12 of its initial capacity (Thanh et al, 2008). The minimum capacity utilization for a facility to be opened is assumed as 20% (Badri et al, 2013).…”

Section: Numerical Examplementioning

confidence: 99%

“…It is difficult to justify the investments made in proactive disruption management from supply chain manager's perspective (Ghadge et al, 2015 andDani, 2008).Reconfiguring the supply chain network (SCN) by managing with the available resources and restoring to normal working condition is a reactive disruption mitigation strategy (Blackhurst et al, 2005;Tang, 2006 andBaymout, 2014). Supply chain network reconfiguration (SCNR) under disruption includes strategies such as shifting the production to undisrupted facilities, capacity expansion at selected facilities, opening new facilities, inventory allocation, transportation rerouting and outsourcing unmet demand (Wilhelm et al, 2013;Thanh et al, 2008 andMelo et al, 2006). Recent reviews on facility location dynamics suggest that network reconfiguration under facility disruption risk as a high scope problem (Seyedhosseini et al, 2016 andArabani &Farahani, 2012).Past SCNR models deal mainly with operational risks such as demand, supply, commodity price fluctuation, exchange rate and import tariff risks (Cortinhal et al, 2015;Pimentel et al, 2013;Melo et al, 2012, 2006and Thanh et al, 2011.…”

Section: Introductionmentioning

confidence: 99%

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Multi Objective Supply Chain Network Reconfiguration Under Disruptions

Rajagopal¹,

Venkatesan²,

Agrawal³

2017

World Conference on Supply Chain Management

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Disruption in today's complex and global supply chain network (SCN) results in a negative impact on business performance. Companies try to manage disruptions by shifting the production/sourcing to undisrupted facilities, expanding the capacity at selected facilities, re-routing transportation and outsourcing the unmet demands. Models and methods to find a cost effective SCN reconfiguration to deal with disruption need attention. In this work, a SCN reconfiguration in a dynamic planning horizon under facility disruption is modelled mathematically with the objectives of minimizing the expected total cost and delivery time. The augmented ε-constraint method is proposed as a solution approach to obtain a set of Pareto optimal solutions. Numerical illustration with disruption scenarios is presented and our results show that facility with minimum distance to most customers serve majority of total customer demand. During disruption onset period, unmet demands are shifted to least utilised facility. During recovery periods, the facility with minimum distance to most customers is expanded to satisfy unmet demands. More capacity expansion occurred in minimum cost solution than in minimum time solution as more number of facilities are opened in latter than in former. The capacity utilization of facilities in minimum cost solutions are higher compared to that of minimum time solution.

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Section: Problem Description and Model Formulationmentioning

confidence: 99%

Section: Numerical Examplementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Multi Objective Supply Chain Network Reconfiguration Under Disruptions

Rajagopal¹,

Venkatesan²,

Agrawal³

2017

World Conference on Supply Chain Management

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“…The use of optimization models for routing and planning in the semiconductor industry has been discussed using linear programming and mixed integer programming as well as stochastic programs (Chouinard et al, 2008;Azaron et al, 2008;Thanh et al, 2008;Huh et al, 2006;Barahona et al, 2005;Hood et al, 2003;Swaminathan, 2000). For example, Thanh et al (2008) recently presented a deterministic mixed integer linear program for designing a production distribution system.…”

Section: Literature Reviewmentioning

confidence: 99%

“…For example, Thanh et al (2008) recently presented a deterministic mixed integer linear program for designing a production distribution system. On the other hand, Chouinard et al (2008) evaluates the impacts of uncertainty related to recovery, processing and demand volumes at the same time in logistics network design with the integration of reverse logistics into supply chains.…”

Section: Literature Reviewmentioning

confidence: 99%

Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations

Rastogi

Fowler

Carlyle

et al. 2011

International Journal of Production Economics

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a b s t r a c tA semiconductor supply network involves many expensive steps, which have to be executed to serve global markets. The complexity of global capacity planning combined with the large capital expenditures to increase factory capacity makes it important to incorporate optimization methodologies for cost reduction and long-term planning. The typical view of a semiconductor supply network consists of layers for wafer fab, sort, assembly, test and demand centers. We present a two-stage stochastic integer-programming formulation to model a semiconductor supply network. The model makes strategic capacity decisions, (i.e., build factories or outsource) while accounting for the uncertainties in demand for multiple products. We use the model not only to analyze how variability in demand affects the make/buy decisions but also to investigate how the correlation between demands of different products affects these strategic decisions. Finally, we demonstrate the value of incorporating demand uncertainty into a decision-making scheme.

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Multistage stochastic programming for the closed‐loop supply chain planning with mobile modules under uncertainty

Zhang

et al. 2023

AIChE Journal

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Traditional supply chains usually follow fixed facility designs which coincide with the strategic nature of supply chain management (SCM). However, as the global market turns more volatile, the concept of mobile modularization has been adopted by increasingly more industrial practitioners. In mobile modular networks, modular units can be installed or removed at a particular site to expand or reduce the capacity of a facility, or relocated to other sites to tackle market volatility. In this work, we formulate a mixed‐integer linear programming (MILP) model for the closed‐loop supply chain network planning with modular distribution and collection facilities. To further deal with uncertain customer demands and recovery rates, we extend our model to a multistage stochastic programming model and efficiently solve it using a tailored stochastic dynamic dual integer programming (SDDiP) with Magnanti‐Wong enhanced cuts. Computational experiments show that the added Magnanti‐Wong cuts in the proposed algorithm can effectively close the gap between upper and lower bounds, and the benefit of mobile modules is evident when the temporal and spatial variability of customer demand is high.

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A dynamic model for facility location in the design of complex supply chains

Cited by 139 publications

References 20 publications

Multi Objective Supply Chain Network Reconfiguration Under Disruptions

Multi Objective Supply Chain Network Reconfiguration Under Disruptions

Supply network capacity planning for semiconductor manufacturing with uncertain demand and correlation in demand considerations

Multistage stochastic programming for the closed‐loop supply chain planning with mobile modules under uncertainty

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