Research Project: Transport Clusters Development and Implementation Measures of a Six- Region Strategic Joint Action Plan for Knowledge-based Regional Innovation.
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Contributors
Funders
ID
EC.00037
Authors
Türkay, Metin
Faculty Member
Publications
Sustainability in supply chain management: aggregate planning from sustainability perspective
(Public Library of Science, 2016) Arslan, Mehmet Can; Saraçoğlu, Öztürk; Türkay, Metin; Saraçoğlu, O.; Arslan, M.C.; Department of Industrial Engineering; Yes; College of Engineering
Supply chain management that considers the flow of raw materials, products and information has become a focal issue in modern manufacturing and service systems. Supply chain management requires effective use of assets and information that has far reaching implications beyond satisfaction of customer demand, flow of goods, services or capital. Aggregate planning, a fundamental decision model in supply chain management, refers to the determination of production, inventory, capacity and labor usage levels in the medium term. Traditionally standard mathematical programming formulation is used to devise the aggregate plan so as to minimize the total cost of operations. However, this formulation is purely an economic model that does not include sustainability considerations. In this study, we revise the standard aggregate planning formulation to account for additional environmental and social criteria to incorporate triple bottom line consideration of sustainability. We show how these additional criteria can be appended to traditional cost accounting in order to address sustainability in aggregate planning. We analyze the revised models and interpret the results on a case study from real life that would be insightful for decision makers.
Analysis of interaction among land use, transportation network and air pollution using stochastic nonlinear programming
(Springer, 2014) Shahraki, Narges; Türkay, Metin; Department of Industrial Engineering; Yes; College of Engineering
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.
Optimal locations of electric public charging stations using real world vehicle travel patterns
(Elsevier, 2015) Shahraki, Narges; Türkay, Metin; Cai, Hua; Xu, Ming; Department of Industrial Engineering; Yes; College of Engineering
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.
