A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

Bozorgi-Amiri, Ali; Jabalameli, Mohammad Saeed; Al-E-Hashem, S. M. Mirzapour

doi:10.1007/s00291-011-0268-x

Cited by 323 publications

(176 citation statements)

References 52 publications

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“…In order to normalize the generated data, the sumation of the π s is justified to 1 for all scenarios. It is noted that the procedure of generating of these parameters is according to Bozorgi-Amiri et al [63] and Shishebori and YousefiBabadi [37]. Table 1 reports the performance of the CPLEX with that the proposed SGbLR algorithm.…”

Section: -1 Experimental Designmentioning

confidence: 99%

“…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%

“…The weighted the ε-constraint, the goal programming (GP), the Pareto optimal, and the LP-norm method are the most popular methods in the literature for treating multi-objective optimization problems [63][64][65][66][67] . In multi-objective problems, since the objective functions have several conflicts with each other, obtaining an optimal solution, which can optimize all of the objective functions altogether, is very difficult (or sometimes impossible) in most of the times.…”

Section: Fuzzy Goal Programmingmentioning

confidence: 99%

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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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Section: -1 Experimental Designmentioning

confidence: 99%

Section: Robust Optimization Approachmentioning

confidence: 99%

Section: Fuzzy Goal Programmingmentioning

confidence: 99%

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

2017

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“…They considered inventory relocating problem in an uncertain demand situation after a disruption. Bozorgi-Amiri et al (2011a, 2011b investigated uncertainty in many parameters of a relief operation like demand, supply and operational costs associated with it. Location of relief centers and allocation of affected area to these centers can be determined under situation described in their model.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrated decision making model for urban disaster management: A multi-objective genetic algorithm approach

Esmaeili¹,

Barzinpour²

2013

10.5267/j.ijiec

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In recent decays, there has been an extensive improvement in technology and knowledge; hence, human societies have started to fortify their urban environment against the natural disasters in order to diminish the context of vulnerability. Local administrators as well as government officials are thinking about new options for disaster management programs within their territories. Planning to set up local disaster management facilities and stock pre-positioning of relief items can keep an urban area prepared for a natural disaster. In this paper, based on a realworld case study for a municipal district in Tehran, a multi-objective mathematical model is developed for the location-distribution problem. The proposed model considers the role of demand in an urban area, which might be affected by neighbor wards. Integrating decisionmaking process for a disaster helps to improve a better relief operation during response phase of disaster management cycle. In the proposed approach, a proactive damage estimation method is used to estimate demands for the district based on worst-case scenario of earthquake in Tehran. Since such model is designed for an entire urban district, it is considered to be a large-scale mixed integer problem and hence, a genetic algorithm is developed to solve the model.

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“…Important parameters such as the demand rate of the damaged areas and also supply, the cost and time of transfer of goods in times of disaster because of incomplete or unavailable data required are inaccurate. As a result, addressing uncertainties can help to have a right strategic, tactical and operational decision [11].…”

Section: Introductionmentioning

confidence: 99%

Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty

et al. 2017

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In this study, the response phase of the management of natural disasters is investigated. One of the important issues in this phase is determining the distribution areas and timely distribution of relief to affected areas in which transportation routing is of a critical matter. In the event of disasters, especially flood and earthquake, terrestrial transportation is not that much easy due to the damage to many infrastructures. For this reason, we propose that delivering relief from the distribution areas to disaster stricken places should be done simultaneously by terrestrial as well as aerial transportation modes to increase route reliability and reduce travel time. In this study, for relief allocation after earthquake, we offer a mixed-integer nonlinear open location-routing model in uncertainty condition. This model includes several contradictory objectives and variety of factors such as travel time, total costs, and reliability. In order to solve this model, a hybrid solution by combining robust optimization and fuzzy multiobjective programming has been used. The performance and effectiveness of the offered model and solution approach has been investigated through a case study on the earthquake in East Azerbaijan, Iran. Our computational results show the solution we have offered for real problems has been effective.

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A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty

Cited by 323 publications

References 52 publications

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

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

Integrated decision making model for urban disaster management: A multi-objective genetic algorithm approach

Multi-objective open location-routing model for relief distribution networks with split delivery and multi-mode transportation under uncertainty

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