Mojtaba Hajian Heidary
Abstract
During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. ...
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During the last decade, many researchers have been attracted to study the role of uncertainties in their supply chain designs. Two important uncertainties of a supply chain are demand uncertainty and supply disruption. The basic concept of the proposed model of this paper is based on the newsvendor problem. The model consists of many retailers and many suppliers as two types of autonomous agents that interact with each other considering demand and supply uncertainties. To cope with the uncertainties, retailers have three choices: a forward contract, an option contract, and purchasing from the spot market. Retailers maybe risk sensitive or risk neutral. A new simulation optimization approach is developed to find the best behavior of a risk sensitive retailer in contrast with the other risk neutral retailers during the multiple contract periods. In this model two objectives are defined to find the best behavior of the risk sensitive retailer: the maximization of the profit and the service level. In order to optimize the agent based simulation, an NSGA-II approach is used. The proposed simulation based NSGA-II is further developed in two directions: the one is different realization numbers of the uncertain parameters, and the other is preference points. Under the different preference points and different number of realizations, Pareto optimal solutions are discovered by the collaboration of the agents. Results of the numerical studies showed that adopting more risk averse policies during the contract periods will result in a larger service level and smaller profit rather than adopting more risk taking policies.
Mansooreh Iravani; Reza Bashirzadeh; M. J. Tarokh
Abstract
This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability ...
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This paper introduces a Travel Demand Management (TDM) model in order to decrease the transportation externalities by affecting on passengers’travel choices. Thus, a bi-objective bi-modal optimization model for road pricing is developed aiming to enhance environmental and social sustainability by considering to minimize the air pollution and maximize the social welfare as its objectives. This model determines optimal prices (bus fare and car toll) and optimal bus frequency simultaneously in an integrated model. The model is based on discrete choice theory and consideres the modes’ utility functions in its formulation. The proposed model is solved by two meta-heuristic methods (Non-dominated Sorting Genetic Algorithm-II (NSGA-II), Multi-Objectives Harmony Search (MOHS)) and the numerical results of a case study in Tehran are presented. The main managerial insights resulted from this case study is that its results support the idea of “free public transportation” or subsidizing the public transport as an effective way to decrease the transport related air pollution
Sajed Rastbin; Mehrdad Gholami Shahbandi; Pouya Soudmand
Abstract
Fast growth of motorized transportation infrastructures in the cities is a consequence of the urbanization process. Despite the undeniable benefits of the developments, some unwelcome social-environmental damages have been occurred. On top of the list, the movements of the pedestrians and their participation ...
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Fast growth of motorized transportation infrastructures in the cities is a consequence of the urbanization process. Despite the undeniable benefits of the developments, some unwelcome social-environmental damages have been occurred. On top of the list, the movements of the pedestrians and their participation in social activities have dramatically reduced as a result of the vehicles dominancy. Pedestrianization and walking-friendly schemes are the key answer to preserve the valuable element of the urban lifestyle. This need motivated the researchers to study and propose mathematical methods to model the dynamics and behavior of the pedestrians in response to their surroundings. However, most of the models in the literature are suitable for limited small-size area and cannot be applied for a large scale urban zone. In this paper, a fuzzy macroscopic pedestrian assignment model is proposed which is applicable for a large scale network and useful for urban master plans as a decision making framework. In addition, a bi-level mixed integer programming model is presented to optimize the pedestrian walking network via selecting some projects on the network, considering the behavior of the pedestrians. Finally, the problem is solved for a large scale pedestrian network in the city of Tehran. The results show the efficiency of the algorithm where spending half of the maximum possible cost has led to a welfare gain of 82.6 percent. The problem was efficiently solved within 12.5 days which is fairly acceptable for the strategic planning of such a large scale network. The numerical results verify the necessity of the model for urban master plan horizon.