2 research outputs found
Development of network location in the state of Uncertainty (ROBUST State)
No full textThis research investigates issues relating to facilities location whichcovers network design under the conditions of uncertainty and robuststate. In this direction a model is developed in which lack of certaintyis taken into consideration regarding parameters such as demand andvarious costs. Unlike the classical methods that the structure ofnetwork is predefined and is predetermined, the facilities locationmakes decisions with respect to the structure of the network.The discussed issue in many real and actual applications such as roadsnetwork, communication systems and etc does exist and locating thefacilities and designing the main network simultaneously areconsidered as important factors; therefore redesigning andoptimization of models which look for simultaneous solutions seemessential. There have been various strategies in the literature ofuncertainty optimization. Two of the most important strategies are the“Probabilistic Optimization” and “Robust Optimization”.This article employs the robust optimization to resolve the uncertaintyand the modelization arguments. Moreover using random samples, thedeveloped model is validated and for further mathematical analysis isutilize
Developing network location model in uncertainty mode (robust mode)
No full textIn this research, facility location problem - network design under uncertainty robust mode will be discussed. In this regard a model will be developed, so that the uncertainty in parameters such as demand and problem’s various costs be considered. Facility location- network design, unlike classical facility location models, which are assumed that network structure is pre-defined and specified- will also decide on the structure of the network. This has been in many actual applications such as road network, communication systems and etc and finding facility location and main network designing simultaneously has deemed important and the need for simultaneous design and optimization models to meet the mentioned items is felt. Different approaches have been developed in the uncertainty optimization literature. Amongst them, robust and stochastic optimization are well-known. In this research robust optimization approach to deal with uncertainty and problem modeling have been used. In addition, by using generated random samples, the proposed model has been tested and computational analysis is presented for various parameters. 10.13084/2175-8018.v05n09a0
