@article {1448,
	title = {The r-interdiction selective multi-depot vehicle routing problem},
	journal = {International Transactions in Operational Research},
	volume = {27},
	year = {2020},
	pages = {835-866},
	abstract = {<p>Abstract The protection of critical facilities has been attracting increasing attention in the past two decades. Critical facilities involve physical assets such as bridges, railways, power plants, hospitals, and transportation hubs among others. In this study we introduce a bilevel optimization problem for the determination of the most critical depots in a vehicle routing context. The problem is modeled as an attacker{\textendash}defender game (Stackelberg game) from the perspective of an adversary agent (the attacker) who aims to inflict maximum disruption on a routing network. We refer to this problem as the r-interdiction selective multi-depot vehicle routing problem (RI-SMDVRP). The attacker is the decision maker in the upper level problem (ULP) who chooses r depots to interdict with certainty. The defender is the decision maker in the lower level problem (LLP) who optimizes the vehicle routes in the wake of the attack. The defender has to satisfy all customer demand either using the remaining depots or through outsourcing to a third party logistics service provider. The ULP is solved through exhaustive enumeration, which is viable when the cardinality of interdictions does not exceed five among nine depots. For the LLP we implement a tabu search heuristic adapted to the selective multi-depot VRP. Our results are obtained on a set of RI-SMDVRP instances synthetically constructed from standard MDVRP test instances.</p>},
	keywords = {bilevel programming, interdiction, multi-depot vehicle routing problem, outsourcing, tabu search},
	doi = {https://doi.org/10.1111/itor.12669},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/itor.12669},
	author = {Sadati, Mir Ehsan Hesam and Aksen, Deniz and Necati Aras}
}
@article {1442,
	title = {Robust supply chain network design with multi-products for a company in the food sector},
	journal = {Applied Mathematical Modelling},
	volume = {60},
	year = {2018},
	pages = {526-539},
	abstract = {<p>The paper aims to solve a problem faced by a company competing in the snacks market in Turkey. In line with the growth in this market, the company needs to make important decisions over the next few years about the timing and location of a new plant, its initial capacity, the timing and amount of additional capacity to be installed at the new and existing plants, the assignment of demand points to plants and the amount of raw materials to be shipped from suppliers to the plants in each period. The objective is to minimize the total cost of various components. The problem is formulated as a multi-period supply chain network design model with multi products. The resulting mixed-integer linear programming model is solved by the commercial solver CPLEX. This model enables us to carry out all analyses requested by the company in an efficient way. After this deterministic model is solved on the basis of a 9\% annual increase in demand, it is extended to a minimax regret model to deal with uncertainty in demand quantities. The results suggest that opening the new plant in the city of {\.I}zmir is indeed a robust solution that is unaffected in different scenarios that are based on three distinct demand increase rates. Even though the location of the new plant remains unchanged with respect to scenarios, the optimal robust solution differs from the optimal solution of each scenario in terms of the capacity expansion decisions. After all obtained results had been communicated to the company managers and executives, the new plant construction was started in 2016 very close to the city that the mathematical model had determined. The new plant is expected to start operating in 2018.</p>},
	keywords = {Demand uncertainty, Facility location-allocation, Mixed-integer linear programming, Robust optimization, Supply chain network design},
	issn = {0307-904X},
	doi = {https://doi.org/10.1016/j.apm.2018.03.034},
	url = {https://www.sciencedirect.com/science/article/pii/S0307904X18301586},
	author = {Necati Aras and {\"U}mit Bilge}
}
