Hub location problem as a variant of the facility location problem is considered in many applications where people, commodities or information movement should occur between an origindestination
pair of nodes. In this talk, a p-robust hub location problem is extended in a risky environment where augmented chance constraint into a min-max regret form is employed to consider network risk as an objective function. The model considers risk factors to design the robust hub network. A Monte-Carlo
simulation based algorithm namely Sample Average Approximation scheme is applied to select a set of efficient scenarios. Then the problem is solved using a novel relax-and-decomposition heuristic based on coupling an accelerated Benders decomposition with Lagrangian relaxation method. A modification of well-known CAB data set is used with different levels of parameters uncertainty. The results demonstrate the capability of the proposed model to design a robust network. Also, the accuracy of the sample average approximation method is verified. Finally, results of the proposed algorithm for different instances were compared to other solution approaches which confirm the efficacy of the proposed solution method.
About the speaker: Mahdi Bashiri is a Professor of Industrial Engineering, Faculty of Engineering at Shahed University. He received a BS in Industrial Engineering from Iran University of Science and Technology (IUST) in 1999, and MS, and PhD from Tarbiat Modares University of Iran in 2001 and 2005, respectively. He is recipient of the 2013 young national top scientist award from Academy of Sciences of the Islamic Republic of Iran. He has 5 years of experience to work in Industry while he has been an academic faculty member since 2005. Mahdi has had a visiting at London
School of Economics and Political Sciences (LSE) in 2004/5 and at Fatih University of Istanbul in 2012. He is now a research visiting professor at RMIT for a period of six months. He is currently a chief editor of Journal of Quality Engineering and Production Optimization published by the Shahed University. His research interests are Facilities planning, Heuristic and Metaheuristic algorithms and finally Stochastic Optimization.
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