INTEGRATION OF SCOR AND FUZZY AHP FOR LOCATION SELECTION OF EDIBLE WHITE COPRA AGRO-INDUSTRY

The selection of agro-industry location is essential in establishing, growing, and relocating agro-industrial systems for all forms of product development. In this regard, white copra has displayed a great economic potential due to export demand. To response this challenge, Indragiri Hilir Regency in Riau Province might become the most promising area since the location is lack of agroindustrial activity. Such condition leads to a excessive supply of coconut, which is in turn, causing the low price. This work aimed determine the best location for developing agroindustry for edible white copra based on multiple criteria. According to the findings, this research successfully created a new integration SCOR with Fuzzy AHP based on a multiple-criteria approach. At the first and second level, each option has equal rate of importance for each attribute and metric, while at the third level, corresponding to the highest importance, is the adaptability for increased shipping, procurement cost, days for coconut inventory, days for edible white copra stock. The fourth level, also corresponding to the highest importance, includes standard conformity, transportation facility, and the percentage of orders with the correct content. Based on the analysis, the locations showing the highest to the lowest importance were Tembilahan Hulu (0.194), Tempuling (0.152), Batang Tuaka (0.160), Kempas (0.118), Kuala Indragiri (0.100), Tembilahan (0.100), Teluk Belengkong (0.087), Pelangiran (0.080), and Enok (0.072). This research is expected to increase the development of edible white copra agroindustry in the Regency of Indragiri Hilir. Keywords: integration, SCOR, FUZZY AHP, multicriteria, edible white copr

Integrating GIS with f-AHP for locating a single facility

Location selection problems stand out as popular research topics. Due to the popularity, different solution approaches emerged in the literature. Multi-criteria Decision Making (MCDM) techniques are examples of the solution approaches and they are frequently used because of their ordering capability in ranking the decision alternatives and success in representing decision makers’ experiences. On the other hand, Geographic Information Systems (GIS) is able to perform different spatial data analysis and provide geographic material. To reach a better decision, integration of experts’ opinions and certain geographic information derived from GIS is necessary. Within this context, in this paper, integration of the GIS abilities with Fuzzy Analytic Hierarchy Process (F-AHP) is discussed with two different integration methodologies for locating single facility. A hypothetical case study is provided to determine a location problem, which focuses on logistics activities. The results have shown that both proposed methodologies are able to order location alternatives in multiple criteria environments.

Sistema de información geográfica para el monitoreo de aves en el Bosque lluvioso de Madre de Dios en la empresa GI Consultores

El objetivo de la investigación fue diseñar un sistema de información geográfico para el monitoreo de aves en el Perú, bajo el cual se empleó un diseño metodológico, mediante los indicadores de distribución, amenazas, así como los indicadores de diversidad. Tratándose de ver que esta herramienta puede plasmar adecuadamente el monitoreo y llegar ser empleado como herramienta para la creación de planes de conservación. Bajo la misma plataforma se puede trabajar más indicadores de diversidad o inclusive sobre la data ya modelada crear nuevos indicadores para la toma de decisiones. Así como recomendaciones para mejorar los planes de conservación entre ellas la necesidad de un modelo más amplio para la granularidad de datos, y la inclusión de nuevos datos como los avistamientos para la generación de rutas de aves que el GIS puede tratar. Por otro bajo la creación de una base de datos relacional permite tener una data limpia y disponible para los usuarios de esta área que es necesaria para el estudio de datos y la creación de nuevas investigacione