Internet of Things in Disaster Logistics Productivity

Introduction

Human beings who are prepared to establish a colony in space and who have achieved dizzying technological developments are incapable of facing natural disaster. Because natural disasters are events whose time, severity and impact are not certain. Although it is also the human beings most affected by disasters are own, now the majority of disasters stem from the human beings’ own actions. Nowadays, the number of human-induced disasters have increased in a way which challenge natural disasters. Climate changes, wars, epidemics, migrations are almost normalized human-induced disasters that we are constantly experiencing. Although the number, type, severity and impact of disasters increase, the productivity and effect of disaster prevention techniques and activities also increases.

Disaster in its most general definition accepted by the United Nations; all kinds of natural, technological or people-oriented events which cause physical, economic and social losses for people, affect society by stopping or interrupting normal life and can not cope with local opportunities (Kadıoğlu, 2008). Logistic activities are a sine qua non of disaster management and they are strategic issues. Successful logistics management is needed to reduce disaster risks, manage effective and efficient operations.

Disaster related studies are increasing in the literature. However, it is not possible to say that studies on disaster logistics are increasing at the same speed. There are many studies about humanitarian logistics and humanitarian aid logistics warehouse location selection within the scope of disaster logistics and some of the most striking of these belong to authors like Barbarosoğlu et al. (2002), Özdamar et al. (2004), Kongsomsaksakul et al. (2005), Ke-jun et al. (2006), Van Wassenhove (2006), Sheu (2007), Mitsotakis and Kassaras (2010), Mete and Zabinsky (2010), Tatham et al. (2010), Afshar and Haghani (2012), Caunhye et al. (2012), Holguín-Veras et al. (2012), Özdamar and Demir (2012), Duran et al. (2013), Jabbarzadeh et al. (2014), Hadiguna et al. (2014), Mulyono and Ishida (2014), Najafi et al. (2014), Rezaei-Malek and Tavakkoli-Moghaddam (2014), Ahmadi et al. (2015), Bayram et al. (2015), Garrido et al. (2015), Özdamar and Ertem (2015), Salman and Yücel (2015), Alem et al. (2016), Bastian et al. (2016), Fereiduni and Hamzehee (2016), Marcelin et al. (2016), Ozkapici et al. (2016), Rezaei-Malek et al. (2016), Tofighi et. al. (2016), Baskaya et al. (2017), Boonmee et al. (2017), Vahdani et al. (2018), Loree and Aros-Vera (2018), Maharjan and Hanaoka (2018), Rodríguez-Espíndola et al. (2018), Tavana et al. (2018), Garrido and Aguirre (2019). As can be seen from literature mentioned above, the articles on disaster logistics have been published after 2002. Most of the articles consist of model proposals that provide solutions to disaster logistics problems. Barbarosoğlu et al. (2002) developed a mathematical model for the planning of helicopter missions during the disaster relief operation. Özdamar et al. (2004) proposed a planning model which be integrated into the disaster logistics decision support system. Kongsomsaksakul et al. (2005) proposed a model to solve the distribution and assignment problem using the genetic algorithm. Ke-jun et al. (2006) proposed a model for eliminating troubles in disaster logistics. Afshar and Haghani (2012) developed an integrated and comprehensive model for the problems of disaster logistics operations. Hadiguna et al. (2014) developed a web-based decision support system model that evaluates the use of public facilities as evacuation centers for disaster victims. Loree and Aros-Vera (2018) developed a mathematical model for locating distribution points and inventory allocation in Post Disaster Humanitarian Aid Logistics. Rodríguez-Espíndola et al. (2018) proposed a model based on the combination of multi-objective optimization and geographic information systems. Garrido and Aguirre (2019) proposed large scale a stochastic mixed-integer programming model which can be solved by the Approximate Sampling Approach. The models in the articles have been established by means of integrated methods to solve increasingly complex problems.