The Experts below are selected from a list of 171 Experts worldwide ranked by ideXlab platform
Behnam Tootooni - One of the best experts on this subject based on the ideXlab platform.
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a humanitarian logistics model for disaster Relief Operation considering network failure and standard Relief time a case study on san francisco district
Transportation Research Part E-logistics and Transportation Review, 2015Co-Authors: Morteza Ahmadi, Abbas Seifi, Behnam TootooniAbstract:We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard Relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.
Uttam Kumar Bera - One of the best experts on this subject based on the ideXlab platform.
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uncertain demand estimation with optimization of time and cost using facebook disaster map in emergency Relief Operation
Applied Soft Computing, 2020Co-Authors: Deepshikha Sarma, Amrit Das, Uttam Kumar BeraAbstract:Abstract When a disaster disrupts a society within a moment without a little warning, the living people of those areas face the deprivation due to demolition. Facebook, one of the most popular social media plays a vital role in response to the victims. The authority of Facebook has launched safety check feature to know about the requirement after the disruption of disaster and based on the information, a intensity factor is defined for requirement of Relief products in this research work. Estimating the amount of Relief products through intensity measure, our research work has introduced a mathematical model for initiation of humanitarian logistic Operation plan. The research has focused on two objective functions through the mathematical model which are minimization of total cost and response time. The model with two objective functions is converted to a equivalent compromise model with neutrosophic compromise programming approach. Deterministic and non-deterministic both algorithms are implemented in the solution process of the compromise mathematical model. In deterministic approach, mathematical model is varified with different methods to obtain compromise results. For non-deterministic approach, a genetic algorithm is proposed to solve the model. The model is experienced with a numerical example and hereby statistical investigation is performed considering different dimension varying the parameters of the model.
Morteza Ahmadi - One of the best experts on this subject based on the ideXlab platform.
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a humanitarian logistics model for disaster Relief Operation considering network failure and standard Relief time a case study on san francisco district
Transportation Research Part E-logistics and Transportation Review, 2015Co-Authors: Morteza Ahmadi, Abbas Seifi, Behnam TootooniAbstract:We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard Relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.
Abbas Seifi - One of the best experts on this subject based on the ideXlab platform.
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a humanitarian logistics model for disaster Relief Operation considering network failure and standard Relief time a case study on san francisco district
Transportation Research Part E-logistics and Transportation Review, 2015Co-Authors: Morteza Ahmadi, Abbas Seifi, Behnam TootooniAbstract:We propose a multi-depot location-routing model considering network failure, multiple uses of vehicles, and standard Relief time. The model determines the locations of local depots and routing for last mile distribution after an earthquake. The model is extended to a two-stage stochastic program with random travel time to ascertain the locations of distribution centers. Small instances have been solved to optimality in GAMS. A variable neighborhood search algorithm is devised to solve the deterministic model. Computational results of our case study show that the unsatisfied demands can be significantly reduced at the cost of higher number of local depots and vehicles.
Deepshikha Sarma - One of the best experts on this subject based on the ideXlab platform.
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uncertain demand estimation with optimization of time and cost using facebook disaster map in emergency Relief Operation
Applied Soft Computing, 2020Co-Authors: Deepshikha Sarma, Amrit Das, Uttam Kumar BeraAbstract:Abstract When a disaster disrupts a society within a moment without a little warning, the living people of those areas face the deprivation due to demolition. Facebook, one of the most popular social media plays a vital role in response to the victims. The authority of Facebook has launched safety check feature to know about the requirement after the disruption of disaster and based on the information, a intensity factor is defined for requirement of Relief products in this research work. Estimating the amount of Relief products through intensity measure, our research work has introduced a mathematical model for initiation of humanitarian logistic Operation plan. The research has focused on two objective functions through the mathematical model which are minimization of total cost and response time. The model with two objective functions is converted to a equivalent compromise model with neutrosophic compromise programming approach. Deterministic and non-deterministic both algorithms are implemented in the solution process of the compromise mathematical model. In deterministic approach, mathematical model is varified with different methods to obtain compromise results. For non-deterministic approach, a genetic algorithm is proposed to solve the model. The model is experienced with a numerical example and hereby statistical investigation is performed considering different dimension varying the parameters of the model.