Multi criteria decision making methods for location selection of distribution centers

In recent years, major challenges such as, increase in inflexible consumer demands and to improve the competitive advantage, it has become necessary for various industrial organizations all over the world to focus on strategies that will help them achieve cost reduction, continual quality improvement, increased customer satisfaction and on time delivery performance. As a result, selection of the most suitable and optimal facility location for a new organization or expansion of an existing location is one of the most important strategic issues, required to fulfill all of these above mentioned objectives. In order to sustain in the global competitive market of 21st century, many industrial organizations have begun to concentrate on the proper selection of the plant site or best facility location. The best location is that which results in higher economic benefits through increased productivity and good distribution network. When a choice is to be made from among several alternative facility locations, it is necessary to compare their performance characteristics in a decisive way. As the facility location selection problem involves multiple conflicting criteria and a finite set of potential candidate alternatives, different multi-criteria decision-making (MCDM) methods can be effectively applied to solve such type of problem. In this paper, four well known MCDM methods have been applied on a facility location selection problem and their relative ranking performances are compared. Because of disagreement in the ranks obtained by the four different MCDM methods a final ranking method based on REGIME has been proposed by the authors to facilitate the decision making process

Deep Reinforcement Learning for Autonomous Inspection System

The objective of the group is to investigate the application of Reinforced Deep Learning to autonomously complete damage inspection on buildings after natural disasters. By using Reinforced Deep Learning with Convolutional Neural Networks performing image processing, the drone will be trained to navigate itself towards buildings and perform building inspection without scanning the whole structure. The algorithms for Reinforced Deep Learning can be used to drive drones autonomously and perform inspections currently done by humans. The stability of the structure of buildings are unknown when these inspections are performed. This application would greatly reduce the risk of individuals involved in inspecting the building structures and provide greater insight into the stability of these structures