Development of Decision Support System for Evaluating Spatial Efficiency of Regional Transport Logistics

• Main Authors: Purim Srisawat, Nopadon Kronprasert, Kriangkrai Arunotayanun
• Format: Conference Proceeding
• Published: 2018
• Subjects: Social Sciences
• Online Access: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85020214827&origin=inward; http://cmuir.cmu.ac.th/jspui/handle/6653943832/57930
• Institution: Chiang Mai University

© 2017 The Authors. Published by Elsevier B.V. Logistics infrastructure in several developing countries has been planned and developed in a haphazard way. Many decision makers normally specify its location based on their experience due to limited data source; and at the same time, disregard the spatial efficiency of the areas. The objectives of this study are (i) to develop a decision tool that helps store and analyse both primary and secondary data in a systematic manner; and (ii) to apply it for evaluating the spatial efficiency of transport logistics in a regional scale. The development of the decision support system is two-fold. One is the development of knowledge base. The study used the Multi-Criteria Decision-Making (MCDM) method to develop the decision solutions. To model the MCDM mechanism, various data and indicators related to logistics efficiency were acquired from both local and regional government and non-government organizations, study reports, and corporate studies. The study conducted a questionnaire survey from three expert groups: the academic group, the government officials, and the private companies. The study applied Fuzzy Analytical Hierarchy Process (FAHP) to designate the criteria used for the MCDM model and to determine the weights of each criterion. The second is the development of computer system. This study used the Geographic Information System (GIS) technology to analyse the MCDM model and visualize the spatial data. The proposed spatial decision-making tool embedded in a GIS platform can be a powerful tool to support decision made in case of highly complex spatial data. Such data related to efficiency evaluation can be visualized for the potential, advantages, and disadvantages of each area and can be used for strategic planning, enhancing logistics efficiency in regional areas.