A robust optimization model for stochastic logistic problems

Yu, Chian Son; Li, Hanlin

doi:10.1016/s0925-5273(99)00074-2

Cited by 400 publications

(172 citation statements)

References 6 publications

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“…Since the late 1990s, this approach has been applied broadly in the area of uncertain control and optimization. Several RO approaches are presented in the literature review (e.g., Mulvey et al 1995;Ben-Tal and Nemirovski 1998, 1999Yu and Li 2000), Sim 2003, 2004;Leung et al 2007), (Bozorgi-Amiri et al 2011;Pishvaee et al 2011;Shishebori and Babadi 2015). According to the efficient beneficial properties of the Pishvaee et al (2011) approach for our problem, in this study, this robust approach is considered which is conducive to cope with the related parameter uncertainty.…”

Section: Solution Approach a Hintmentioning

confidence: 99%

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

Shishebori

Babadi

2017

J Ind Eng Int

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This study investigates the reliable multi-configuration capacitated logistics network design problem (RMCLNDP) under system disturbances, which relates to locating facilities, establishing transportation links, and also allocating their limited capacities to the customers conducive to provide their demand on the minimum expected total cost (including locating costs, link constructing costs, and also expected costs in normal and disturbance conditions). In addition, two types of risks are considered; (I) uncertain environment, (II) system disturbances. A two-level mathematical model is proposed for formulating of the mentioned problem. Also, because of the uncertain parameters of the model, an efficacious possibilistic robust optimization approach is utilized. To evaluate the model, a drug supply chain design (SCN) is studied. Finally, an extensive sensitivity analysis was done on the critical parameters. The obtained results show that the efficiency of the proposed approach is suitable and is worthwhile for analyzing the real practical problems.

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Section: Solution Approach a Hintmentioning

confidence: 99%

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

Shishebori

Babadi

2017

J Ind Eng Int

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“…According to the easy understanding of the Mulvey approach for many designers/architects and the scenario-based approach of the problem, this robust approach is considered which is conducive to cope of the related parameter uncertainty. For more detail reading about this robust optimization approach, we refer readers to Mulvey et al [55], Yu and Li [61], Leung et al, [62] , BozorgiAmiri et al [63], Shishebori and Yousefi-Babadi [37].…”

Section: Robust Optimization Approachmentioning

confidence: 99%

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

2017

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Abstract:This study considers a multi-objective combined budget constrained facility location/network design problem (FL/NDP) in which the system uncertainty is considered. The most obvious practical examples of the problem are territorial designing and locating of academies, airline networks, and medical service centers. In order to assure the network reliability versus uncertainty, an efficient robust optimization approach is applied to model the proposed problem. The formulation is minimizing the total expected costs, including, transshipment costs, facility location (FL) costs, fixed cost of road/link utilization as well as minimizing the total penalties of uncovered demand nodes. Then, in order to consider of several system uncertainty, the proposed model is changed to a fuzzy robust model by suitable approaches. An efficient Sub-gradient based Lagrangian relaxation algorithm is applied. In addition, a practical example is studied. At the following, a series of experiments, including several test problems, is designed and solved to evaluate of the performance of the algorithm. The obtained results emphasize that considering of practical factors (e.g., several uncertainties, system disruptions, and customer satisfaction) in modelling of the problem can lead to significant improvement of the system yield and subsequently more efficient utilization of the established network.

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“…Mulvey et al (1995) used a quadratic form of variance, which is nonlinear and complicated. To handle this difficulty, here we use an absolute deviation as Yu and Li (2000) proposed, which is as follows,…”

Section: Robust Optimizationmentioning

confidence: 99%

“…(13) is the second objective function of this problem, which is variance of purchase, transportation, inventory holding, shortage and ordering costs. This has been considered as absolute magnitude and it is converted to a linear form based on the available literature in this field, like what pointed in the study of Yu and Li (YU & Li 2000).…”

Section: Mathematical Formulationmentioning

confidence: 99%

A multi-objective model for locating distribution centers in a supply chain network considering risk and inventory decisions

Hamedani¹,

Jabalameli²,

Bozorgi-Amiri³

2013

10.5267/j.msl

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This paper presents a multi-objective location problem in a three level supply chain network under uncertain environment considering inventory decisions. The proposed model of this paper considers uncertainty for different parameters including procurement, transportation costs, supply, demand and the capacity of various facilities. The proposed model presents a robust optimization model, which specifies locations of distribution centers to be opened, inventory control parameters (r, Q), and allocation of supply chain components, concurrently. The resulted mixed-integer nonlinear programming minimizes the expected total cost of such a supply chain network comprising location, procurement, transportation, holding, ordering, and shortage costs. The model also minimizes the variability of the total cost of relief chain and minimizes the financial risk or the probability of not meeting a certain budget. We use the ε-constraint method, which is a multi-objective technique with implicit trade-off information given, to solve the problem and using a couple of numerical instances, we examine the performance of the proposed approach.

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A robust optimization model for stochastic logistic problems

Cited by 400 publications

References 6 publications

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

Designing a capacitated multi-configuration logistics network under disturbances and parameter uncertainty: a real-world case of a drug supply chain

A Lagrangian relaxation approach to fuzzy robust multi-objective facility location network design problem

A multi-objective model for locating distribution centers in a supply chain network considering risk and inventory decisions

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