Babak Badri-Koohi
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Babak Badri-Koohi has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Haitham Seada
Michigan State University
1 shared publication - M. Asghari
Grado Department of Industrial and Systems Engineering, Virginia Tech, USA
1 shared publication - Reza Tavakkoli-Moghaddam
School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
1 shared publication - Siqian Shen
Department of Industrial and Operations Engineering University of Michigan 48109 Ann Arbor MI
1 shared publication - Xian Yu
Department of Integrated Systems Engineering, The Ohio State University, Columbus, United States
1 shared publication
Latest Publications
- 21 October 2022
- Published by Elsevier in Computers & Operations Research
- Vol. 150, 106045
Time window optimization for attended home service delivery under multiple sources of uncertainties
- 21 October 2022
- Published by Elsevier in Computers & Operations Research
- Vol. 150, 106045
Time window optimization for attended home service delivery under multiple sources of uncertainties
- Open Access
- 16 February 2019
- Published by Engineering, Technology & Applied Science Research in Engineering, Technology & Applied Science Research
- Vol. 9 (1) , 3715-3720
Optimizing Number and Locations of Alternative-Fuel Stations Using a Multi-Criteria Approach
The transition to alternative fuels is obligatory due to the finite amount of available fossil fuels and their rising prices. However, the transition cannot be done unless enough infrastructure exists. A very important infrastructure is the fueling station. As establishing alternative-fuel stations is expensive, the problem of finding the optimal number and locations of initial alternative-fuel stations emerges and it is investigated in this paper. A mixed-integer linear programming (MILP) formulation is proposed to minimize the costs using net present value (NPV) technique. The proposed formulation considers the criteria of the two most common models in the literature for such a problem, namely P-median model and flow refueling location model (FRLM). A decision support system is developed for the users to be able to control the parameter values and run different scenarios. For case study purposes, the method is used to find the optimal number and locations of the alternative-fuel stations in the city of Chicago. Some data wrangling techniques are used to overcome the inability of the method to solve very large-scale problems.
- 16 February 2019
- Published by Engineering, Technology & Applied Science Research in Engineering, Technology & Applied Science Research
- Vol. 9 (1) , 3715-3720
Open AccessOptimizing Number and Locations of Alternative-Fuel Stations Using a Multi-Criteria Approach
The transition to alternative fuels is obligatory due to the finite amount of available fossil fuels and their rising prices. However, the transition cannot be done unless enough infrastructure exists. A very important infrastructure is the fueling station. As establishing alternative-fuel stations is expensive, the problem of finding the optimal number and locations of initial alternative-fuel stations emerges and it is investigated in this paper. A mixed-integer linear programming (MILP) formulation is proposed to minimize the costs using net present value (NPV) technique. The proposed formulation considers the criteria of the two most common models in the literature for such a problem, namely P-median model and flow refueling location model (FRLM). A decision support system is developed for the users to be able to control the parameter values and run different scenarios. For case study purposes, the method is used to find the optimal number and locations of the alternative-fuel stations in the city of Chicago. Some data wrangling techniques are used to overcome the inability of the method to solve very large-scale problems.