Articles written in Sadhana

    • A P-hub median network design problem with preventive reliability approach for before and after hub failure


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      Hubs are vital elements of communication and transportation networks and play an important role in interchanging the flows of information/passenger/goods. For this purpose, designing a highly reliable hub network is very critical, because inefficiency of even a single hub across the network tends to reduce theefficiency of the whole network in transferring the flow appropriately. In this research, a bi-objective mathematical model was designed to study the situations before and after hub failure. Considering reliability, the first objective was to maximize the flow through the network, and the second objective was to prevent wasting the flow due to a possible hub failure. The lexicographic method was used to solve this multi-objective problem with dependent objectives. This method represents an appropriate solution for problems whose objective functionsare of different priorities or depend on one another. Various cases of different sizes were used to evaluate the model in terms of reliability. Since the hub location problem is an NP-Hard problem of commonly large dimensions, a hybrid meta-heuristic algorithm called ‘‘memetic algorithm’’ was used to have it solved. Thealgorithm was a combination of genetic algorithm with simulated annealing algorithm, where simulated annealing algorithm was used for local neighborhood search. Findings indicated that, consideration of the backup hub tends to enhance route reliability, thereby increasing the flow through the network, as compared tothe case with no backup hub.

    • Bi-level multi-objective model for existing link capacity expansion problem across urban transportation network considering travel time reliability: presenting dynamic particle swarm algorithm


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      Urban transportation network design and traffic control problems fall within the scope of infrastructural engineering sciences which become increasingly more important in ever-growing societies of today. In highly populated old cities where establishing new links are facing many human-related, social, economic, andpolitical problems, a workaround for addressing traffic problems is to expand the capacity of existing links, so as to not only control the traffic, but also reduce the urban environmental pollutions caused by vehicles stuck in traffic and decrease the time wasted in traffic to accelerate routines of the society. In the present research, an urban transportation network design model is presented with the aim of enhancing travel time reliability by expanding the capacity of existing network links at minimum possible cost. A significant assumption taken inthe present study is that demands in normal condition and peak traffic hours are treated separately, so as to prevent possible problems by congestion management. In the present study, the uncertainty associated with demand for travel, travel time, and the flow passing through different links are taken into consideration. Travel time reliability calculations are carried out assuming that the demand for travel and travel time follow lognormal distributions. In order to solve this bi-level model, particle swarm optimization algorithm was used. Incorporationof the inertial coefficients dynamics, personal learning, and communal learning into the algorithm contributes to the convergence of this algorithm for solving the bi-level model

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