Researcher:
Shahraki, Narges

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PhD Student

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Narges

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Shahraki

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Shahraki, Narges

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2014 - 20164

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Researcher Of Publications: search.filters.isAuthorOfPublication.63eceb43-3786-45cd-bfc9-ed2bf8489f39

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    Optimal locations of electric public charging stations using real world vehicle travel patterns
    (Pergamon-Elsevier Science Ltd, 2015) Cai, Hua; Xu, Ming; N/A; Department of Industrial Engineering; Shahraki, Narges; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956
    We propose an optimization model based on vehicle travel patterns to capture public charging demand and select the locations of public charging stations to maximize the amount of vehicle-miles-traveled (VMT) being electrified. The formulated model is applied to Beijing, China as a case study using vehicle trajectory data of 11,880 taxis over a period of three weeks. The mathematical problem is formulated in GAMS modeling environment and Cplex optimizer is used to find the optimal solutions. Formulating mathematical model properly, input data transformation, and Cplex option adjustment are considered for accommodating large-scale data. We show that, compared to the 40 existing public charging stations, the 40 optimal ones selected by the model can increase electrified fleet VMT by 59% and 88% for slow and fast charging, respectively. Charging demand for the taxi fleet concentrates in the inner city. When the total number of charging stations increase, the locations of the optimal stations expand outward from the inner city. While more charging stations increase the electrified fleet VMT, the marginal gain diminishes quicldy regardless of charging speed.
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    Developing a new model for dynamic vendor managed inventory with considering time value of money
    (Inderscience Publishers, 2015) Pourghannnad, Behrooz; Kazemi, Abolfazl; Chiniforooshan, Payam; Azizmohammadi, Mahdi; N/A; Shahraki, Narges; PhD Student; Graduate School of Sciences and Engineering; N/A
    This paper investigates a new dynamic two-echelon single-vendor multiple-buyer supply chain model under vendor managed inventory assumptions, which named dynamic vendor managed inventory, named DVMI, model. The DVMI model is formulated in order to find out the optimal sales quantity for each buyer at each period by maximising the profit of supply chain. All parameters are considered deterministic and delays in the payments are permissible. In addition, the relationship between sales quantity and sales price is assumed to be linearly. Implementation of proposed DVMI model is analysed with a case study that carried out at an Iranian dairy products company. The DVMI model is solved by using both LINGO and MATLAB genetic algorithm and direct search toolbox. Furthermore, sensitivity of model respect to the number of selected buyers in each period parameter is analysed.
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    Analysis of interaction among land use, transportation network and air pollution using stochastic nonlinear programming
    (Springer, 2014) N/A; Department of Industrial Engineering; Shahraki, Narges; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956
    This paper presents two novel models for land use and transportation to address the development of different functional zones in urban areas by considering the design of an efficient transportation network and reducing air pollution. Objective functions of the first model are maximizing utility function and maximizing reliability index. the utility is formulated as a function of travel cost and zonal attractiveness. Reliability index is defined as the probability that flow in each link of the network is less than the design capacity. Maximizing this probability is equivalent to minimizing congestion in the network. in addition, maximizing utility and minimizing carbon monoxide emission in the network are considered as objective functions in the second model. the formulated models are nonlinear and stochastic. We implement the epsilon-constraint method for solving these bi-objective optimization problems. We analyze the models and solution characteristics of some examples. in addition, we evaluate the relation between computing time and complexity of the model. in this study, for the first time in the open literature, stochastic bi-objective optimization models are formulated to analyze interaction among land use, transportation network and air pollution. We also extract and summarize some useful insights on the relationship among land use, transportation network and environmental impact associated with them.
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    Urban logistics: multi-modal transportation network design accounting for stochastic passenger demand and freight logistics
    (Springer, 2016) N/A; Department of Industrial Engineering; Shahraki, Narges; Türkay, Metin; PhD Student; Faculty Member; Department of Industrial Engineering; Graduate School of Sciences and Engineering; College of Engineering; N/A; 24956
    In this chapter, we present a bi-level optimization model by considering multiple transportation modes, stochastic passenger travel demand and freight logistics. Passenger travel demand can follow a general probability distribution where its mean and variance are function of the population in the origin and destination areas. The problem is formulated as a bi-level optimization problem. In the lower level, transportation design problem is formulated to minimize traveler costs and in the upper level we consider minimizing carbon monoxide emission and minimizing probability of traffic congestion. The two-stage model is formulated as a single stage model by considering optimality condition of lower level problem as a set of constraints in the upper level model. The formulated single stage model is a Mixed-Integer Non-linear Programming (MINLP) problem. In this chapter, a stochastic multi-modal, bi-level optimization model is presented for passenger and freight transportation problem in urban regions.