Two-stage distributionally robust mixed-integer optimization model for three-level location–allocation problems under uncertain environment

Liu, Zhimin; Wu, Zhongqing; Ji, Ying; Qu, Shaojian; Raza, Hassan

doi:10.1016/j.physa.2021.125872

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…Distributionally robust optimization (DRO) is a technique that unifies the robust and stochastic optimization frameworks, making it less prone to the weaknesses of each individual approach ( Shang and You, 2018 , Jia et al, 2020 , Liu et al, 2021 ). It is therefore the appropriate framework in the context of a pandemic where there is no prior historical data for SC managers to rely on to make risk-benefit decisions.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic

Ash

Diallo

Venkatadri

et al. 2022

Computers & Industrial Engineering

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Section: Literature Reviewmentioning

confidence: 99%

Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic

Ash

Diallo

Venkatadri

et al. 2022

Computers & Industrial Engineering

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“…Liu et al. (2021) presented a two‐stage distributionally robust mixed‐integer optimization model for the three‐level location‐allocation problems under uncertain customer demand and transportation costs.…”

Section: Literature Reviewmentioning

confidence: 99%

Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity

Wang

Yang

et al. 2022

Int Trans Operational Res

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This paper considers facility location, inventory pre‐positioning and vehicle routing as strategic and operational decisions corresponding to preparedness and response phases in disaster relief logistics planning. For balancing surpluses and shortages, an effective lateral transshipment strategy is proposed to evenly distribute the relief resources between warehouses after the disaster occurs. To handle ambiguity in the probability distribution of demand, we develop a risk‐averse two‐stage distributionally robust optimization (DRO) model for the disaster relief logistics planning problem, which specifies the worst‐case mean‐conditional value‐at‐risk (CVaR) as a risk measure. For computationally tractability, we transform the robust counterpart into its equivalent linear mixed‐integer programming model under the discrepancy‐based ambiguity set centered at the nominal (empirical) distributions on the observed demand from the historical data. We verify the effectiveness of the proposed DRO model and the value of lateral transshipment strategy by an illustrative small‐scale example. The numerical results show that the proposed DRO model has advantage on avoiding over‐conservative solutions compared to the classic robust optimization model. We also illustrate the applicability of the proposed DRO model by a real‐world case study of hurricanes in the southeastern United States. The computational results demonstrate that the proposed DRO model has superior out‐of‐sample performance and can mitigate the adverse effects of Optimizers' Curse compared with the traditional stochastic programming model.

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“…Fathi et al [26] developed a two-phase approach based on queuing theory and stochastic optimization to determine the optimal number and location of distribution centers. Liu et al [27] studied the three-level optimal location problem under the condition of uncertain transportation cost and customer demand. Marianov et al [28] studied the location of retail stores considering customers' purchase of multiple commodities.…”

Section: Physical Store Locationmentioning

confidence: 99%

“…Constraint (26) ensures that if a vehicle k 1 enters a node, it also exits from the node. Formula (27) indicates that vehicle k 1 can only drive out of the physical store that has been built. Constraints (28) and (29) eliminate sub-cycles between customers and physical stores.…”

Section: Objective Functionmentioning

confidence: 99%

Location Optimization of Offline Physical Stores Based on MNL Model under BOPS Omnichannel

Wan

Zhang

Liu

et al. 2022

JTAER

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With the continuous upgrading of consumer demand and retail modes, more and more retailers are switching to an omnichannel retail mode. In order to study the location problem of offline physical stores of online retail enterprises that plan to implement the BOPS (Buy Online and Pickup in Store) omnichannel retail model, this paper considers consumers’ choice behavior under the omnichannel retail model; uses the MNL (Multinomial Logit) model to depict customers’ choice behavior between the online channel, BOPS channel, and offline physical channel; and constructs a location optimization model of offline physical stores with the goal of minimizing the enterprise’s cost. According to the characteristics of the model, an improved genetic algorithm was designed; the algorithm was improved on chromosome selection mode, crossover, and mutation rules. Finally, an example is calculated, and the physical store location scheme of a retail enterprise and the vehicle routing optimization scheme under the two-level distribution network are obtained, which verifies the effectiveness of the model and algorithm and provides a scientific reference for the physical store location decision of online retail enterprises planning to implement the BOPS omnichannel retail model. The impact of freight, return rate, and service level of physical stores on the location of offline physical stores is analyzed. The results show that in the process of expanding offline physical stores to implement the BOPS omnichannel retail model, online retail enterprises can reduce enterprise costs by improving the freight level and service level of the physical store. The higher the return rate of the online channel, the more necessary it is to expand offline physical stores, and the lower the enterprise cost. At the same time, management suggestions are put forward for the enterprise operation under the BOPS omnichannel retail mode.

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Two-stage distributionally robust mixed-integer optimization model for three-level location–allocation problems under uncertain environment

Cited by 7 publications

References 33 publications

Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic

Distributionally robust optimization of a Canadian healthcare supply chain to enhance resilience during the COVID-19 pandemic

Distributional robustness and lateral transshipment for disaster relief logistics planning under demand ambiguity

Location Optimization of Offline Physical Stores Based on MNL Model under BOPS Omnichannel

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