The accidental and unpredictable nature of disasters such as earthquake brings about some plans to deal with critical problems in order to reduce the dangers at the time of their occurrence. Effective distribution of ...
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The accidental and unpredictable nature of disasters such as earthquake brings about some plans to deal with critical problems in order to reduce the dangers at the time of their occurrence. Effective distribution of relief goods and supplies plays an important role in the rescue operation after an earthquake. Therefore, a two-phase, multi-objective mixed integer, multi-period and multi-commodity mathematical modeling in the three-level relief chain was offered, in which, locating of the distribution centers and warehouses with various levels of capacity, related decisions to the stored goods in the warehouses and established distribution centers were considered in the first phase, and considering the limited hard time windows, in the second phase, operational programming was performed for vehicle routing and distribution of goods to the affected areas, so minimizing the total cost and travel time also increased the reliability of the route. In addition to the features considered in this model, in special cases, it is possible that each critical area receives service more than once;to consider split delivery assumption in the problem, a different model will be presented for this purpose. Since some parameters are uncertain during the crisis, in order to let the model approach the reality, using a robust optimization approach, the model was developed in an uncertain condition. Two meta-heuristic algorithms of NSGAII and MOPSO were used to solve the given problem, in which the accuracy of the mathematical model and the proposed algorithms efficiency were assessed through numerical examples. the results of algorithms were presented for 35 various problems.
The increasing damage caused by disasters is a major challenge for disaster management authorities, especially in instances where simultaneous disasters affect different geographical areas. The uncertainty and chaotic...
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The increasing damage caused by disasters is a major challenge for disaster management authorities, especially in instances where simultaneous disasters affect different geographical areas. The uncertainty and chaotic conditions caused by these situations combined with the inherent complexity of collaboration between multiple stakeholders complicates delivering support for disaster victims. Decisions related to facility location, procurement, stock prepositioning and relief distribution are essential to ensure the provision of relief for these victims. There is a need to provide analytical models that can support integrated decision-making in settings with uncertainty caused by simultaneous disasters. However, there are no formulations tackling these decisions combining multiple suppliers, multiple agencies, and simultaneous disasters. This article introduces a novel bi-objective two-stage stochastic formulation for disaster preparedness and immediate response considering the interaction of multiple stakeholders in uncertain environments caused by the occurrence of simultaneous disasters. At the first stage, decisions related to the selection of suppliers, critical facilities, agencies involved, and pre-disaster procurement are defined. Resource allocation, relief distribution and procurement of extra resources after the events are decided at the second stage. The model was tested on data from the situation caused by simultaneous hurricanes and storms in Mexico during September of 2013. The case is contrasted with instances planning for disasters independently. The results show how planning for multiple disasters can help understand the real boundaries of the disaster response system, the benefits of integrated decision-making, the impact of deploying only the agencies required, and the criticality of considering human resources in disaster planning.
In this paper, it is presented an approach to match technically and economically the reliability of electricity distribution networks through an optimization methodology consisting of the mathematical model and metahe...
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In this paper, it is presented an approach to match technically and economically the reliability of electricity distribution networks through an optimization methodology consisting of the mathematical model and metaheuristic solution technique to obtain an optimized plan for efficient management of maintenance tasks. The problem of maintenance tasks is formulated as a mixed dynamic nonlinear multi-objective optimization model, in which costs to perform maintenance tasks on equipment and/or components that make up the electric distribution network are minimized, while they have their reliability maximized. The constraints of this model are the individual and group electricity supply interruption duration and frequency indices, and availability of financial and human resources. The solution of this problem is obtained through a specialized non-dominated sorting genetic algorithm multi-objective metaheuristic, which provides a set of non-dominated solutions very close to the optimal Pareto frontier. Each solution at this frontier represents a maintenance plan able to assist the making decision of the operators in distribution companies for managing the maintenance crews.
In this paper, an adaptive algorithm is designed for dynamic risk management in petroleum project investment based on a variable precision rough set (VPRS) model. In risk management, at each stage of decision-making, ...
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In this paper, an adaptive algorithm is designed for dynamic risk management in petroleum project investment based on a variable precision rough set (VPRS) model. In risk management, at each stage of decision-making, experts are invited to identify risk indices and support the decision-maker in evaluating the risk exposure (RE) of individual projects. The VPRS model is used to mine risk rules and determine the significance of risk indices from RE decision tables. Considering that there are multiple risks involved in any petroleum project investment, we use multi-objective programming to obtain the optimal selection of projects with minimum RE, where the significance of risk indices is assigned to each of the corresponding multi-objective functions as a weight. Moreover, we develop a risk ranking model to measure the degree of risk for individual projects in a portfolio. Finally, a numerical example based on a Chinese petroleum company's investments in overseas projects is presented to illustrate the proposed approach, and then conclusions are drawn. (C) 2010 Elsevier Inc. All rights reserved.
This article develops a systems dynamics and multi-objective programming model (SDMOP) for planning a regional circular economy. Various risk analyses are conducted using the technique of sensitivity analysis. This SD...
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This article develops a systems dynamics and multi-objective programming model (SDMOP) for planning a regional circular economy. Various risk analyses are conducted using the technique of sensitivity analysis. This SDMOP model includes two modules: the MOP module used to derive optimized parameters as inputs to the systems dynamics model, and the systems dynamics module used to plan the regional circular economy. We demonstrate the application of this SDMOP model to a problem of planning the circular economy of a county in the Sichuan Province of China.
This work presents an integrated method for the optimisation of a regional wood-energy supply network. The model is based on a scalar system that comprises a demand point (district heating plants (DHP)) and bio-energy...
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This work presents an integrated method for the optimisation of a regional wood-energy supply network. The model is based on a scalar system that comprises a demand point (district heating plants (DHP)) and bio-energy sources (supply basin (SB)), each of which is related to a biomass terminal. The objective of optimisation is based on both technical-logistics and environmental parameters. An SB is defined by the anisotropic weighted Voronoi tessellation methodology. The parameters are then aggregated to a multiobjective analysis that includes the optimisation of variables and compromise programming approach. Results permit the identification of the best supply chain organisation and the determination of the agroforest energy districts where rural policy and intervention could be applied. The model was tested in the province of Florence (central Italy) to depict efficient scenarios for the fuelling of DHPs.
In this article, a new exact method is proposed to solve a problem, say , of maximizing a linear fractional function over the integer efficient set of multi-objective integer linear programming problem (MOILP). The me...
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In this article, a new exact method is proposed to solve a problem, say , of maximizing a linear fractional function over the integer efficient set of multi-objective integer linear programming problem (MOILP). The method is developed through the branch and cut technique and the continuous linear fractional programming, to come up with an integer optimal solution for problem without having to explicitly list all efficient solutions of problem (MOILP). The branching process is strengthened by an efficient cut as well as an efficiency test so that a large number of non-efficient feasible solutions can be avoided. Illustrative example and an experimental study are reported to show the merit of this new approach.
This paper proposes an optimization model of supply chain resilience strategy for large passenger aircraft. A quality function deployment (QFD) framework is conducted to analyze the resilience of the large passenger a...
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This paper proposes an optimization model of supply chain resilience strategy for large passenger aircraft. A quality function deployment (QFD) framework is conducted to analyze the resilience of the large passenger aircraft supply chain, and the key parameters are characterized based on the probabilistic linguistic term. Then based on the output of the QFD framework an optimization model of the resilience strategy considering the stochastic disturbance faced by the supply chain is constructed. Taking the supply chain for large aircraft cockpit control display module as an example to illustrate the application steps and feasibility of the model, the results demonstrate that change of supply chain management responsibilities, implementing hierarchical management of suppliers, seeking coordinated implementation of inventory management mode, and improving the pre-risk identification system, play prominent roles in enhancing supply chain resilience, and the combination of different strategies can indeed enhance the supply chain resilience under the budget constraint.
The letter formalizes the bandwidth allocation process over space communication systems as a multi-objective programming (MOP) problem and proposes an allocation called "Minimum Distance" algorithm. The algo...
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The letter formalizes the bandwidth allocation process over space communication systems as a multi-objective programming (MOP) problem and proposes an allocation called "Minimum Distance" algorithm. The algorithm assigns the bandwidth so to approach the ideal situation where each station has the overall channel bandwidth available, as close as possible. The performance evaluation is carried out analytically by varying the fading level of the space channel.
We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive" model and the more flexible "history-adaptive" one. We point out severa...
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We compare two different models for multicriterion routing in stochastic time-dependent networks: the classic "time-adaptive" model and the more flexible "history-adaptive" one. We point out several properties of the sets of efficient solutions found under the two models. We also devise a method for finding supported history-adaptive solutions. (C) 2009 Elsevier B.V. All rights reserved.
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