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A Risk-Based Location-Allocation Approach for Weapon Logistics

Department of Management Information Systems, Adana Science and Technology University, 01250 Adana, Turkey
Department of Industrial Engineering, Gaziantep University, 27310 Gaziantep, Turkey
Author to whom correspondence should be addressed.
Logistics 2018, 2(2), 9;
Received: 7 March 2018 / Revised: 27 April 2018 / Accepted: 28 April 2018 / Published: 2 May 2018
PDF [7152 KB, uploaded 2 May 2018]


Governments have vital missions, such as securing their nation from many internal or external risks/threats. Thus, they prepare themselves against different scenarios. The most common scenario for all countries is “facing attacks from other countries”. However, training for these scenarios is not possible because military exercises are too expensive. The contribution of this paper is a scientific approach proposed for such a scenario. A mathematical model is developed to allocate different weapon types to a set of candidate locations (demand nodes, the military installations that need weapons) while minimizing total transportation costs, setup costs, and allocation risk. The risk arises from allocating the weapons to other military units as backups during a conflict. The risk increases when one military unit allocates their weapons to another unit during attacks. The mathematical model is tested on a case study problem of Turkish Land Forces. This case study is solved in 14 min, and the optimal total transportation and setup costs are determined. Since it is very important to make quick decisions during an attack, this scientific approach and computational time can be useful for military decision makers. Additionally, the results of this study can guarantee that any attack can be handled with the minimum cost and risk. View Full-Text
Keywords: location-allocation; risk; weapon logistics; homeland defense location-allocation; risk; weapon logistics; homeland defense

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Çetinkaya, C.; Haffar, S. A Risk-Based Location-Allocation Approach for Weapon Logistics. Logistics 2018, 2, 9.

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