Dr. S. (Shadi) Sharif Azadeh
Work in Progress
- M. Pacheco, S. Sharif Azadeh, M. Bierlaire & B. Gendron (2019). Integrating supply and demand within the framework of mixed integer optimization problems. European Journal of Operational Research.
T. Robenek, S. Sharif Azadeh & Y. Maknoon (2018). Train timetable design under elastic passenger demand. Transportation Research. Part B, Methodological, 111, 19-38.
T. Robenek, S. Sharif Azadeh, Y. Maknoon & M. Bierlaire (2017). Hybrid cyclicity: combining the benefits of cyclic and non-cyclic timetables. Transportation Research. Part C, Emerging Technologies, 75, 228-257.
T. Robenek, S. Sharif Azadeh, Y. Maknoon, J. Chen Jianghang & M. Bierlaire (2016). Passenger Centric Train Timetabling Problem. Transportation Research. Part B, Methodological, 89, 107-126.
S. Sharif Azadeh, P. Marcotte & G. Savard (2015). A non-parametric method to demand forecasting in revenue management systems. Computers and Operations Research, 63, 23-31.
S. Sharif Azadeh, M. Hosseinalifam & G. Savard (2014). The impact of customer behavior models on revenue management systems. Computational Management Science, 1, 1-11.
S. Sharif Azadeh, P. Marcotte & G. Savard (2014). A taxonomy of demand uncensoring methods in revenue management. Journal of Revenue and Pricing Management, 13, 440-456.
S. Sharif Azadeh & R. Labib (2013). Railway demand forecasting in revenue management using neural networks. International Journal of Revenue Management.
S. Sharif Azadeh. Demand Forecasting in Revenue Management Systems. Polytechnique Montreal Prom./coprom.: P. Marcotte.
PhD Track (1)
Side positions (3)
PhD Vacancy (1)
We propose this research aligned with one of the main targets of the United Nations in the 2030
agenda for sustainable development. One of these main goals aims at strengthening the means
of implementation and revitalization of the global partnership for sustainable development. This
objective encourages multi-stakeholder partnerships that mobilize and share knowledge,
expertise, technology and financial resources to support the achievement of the sustainable
development goals in all countries. The Netherlands in general and Rotterdam in particular are perfectly positioned for the handling of large volumes of cargo. On the sea side, the port’s strategic location in North-west Europe, its unrivalled depth and the large-scale container handling facilities definitely give Rotterdam an edge
over the competition. Decisive, innovative companies have already been optimally utilizing these advantages for decades. For the hinterland transport of deep-sea cargo throughout Europe, the comprehensive networks of rivers and railway lines constitute major trump cards as well, with a huge capacity for sustainable transport.
Use of real time data in transport planning with fixed connections (like trains, shortsea and ferries) is limited. Despite the presence of such data in large scale provided by ferry and train companies, the online planning still remains a complex task to tackle in practice. Online planning using real -time data is even more complex in the context of synchromodal transport for freight. Recently, several research projects have introduced decision-support algorithms and heuristics to solve logistics problems synchromodal logistics systems. However, the existing algorithms are not real time. As a result, they do not consider the benefit of self-learning mechanisms in case of disturbances.
This research project aims at two main targets:
1) develop a real-time synchromodal planning algorithm capable of solving real case problems,
2) integrate self-learning mechanism to introduce recovery strategies in case of small scale
Office: Tinbergen Building H11-27
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3062 PA Rotterdam
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