A Study of House Salespeople Decision Models for House-Purchasing Recommendation and Customer Allocation

• Main Authors: Yan-Guang Dong, 董延光
• Other Authors: Jiunn-Min Lee
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/90713003409049871800

碩士 === 輔仁大學 === 資訊管理學系 === 93 === Generally speaking, the brokerage of the real estate is a service industry of intensive labor, and thus effectively planning and making use of the resource for providing higher service quality can reduce the resource wasted and bring the competition ability. Current websites of the real-estate information just provide each buyer with static information of every registered house, and also collect the required data and demand information from house buyers. Therefore, after the needs and preferences of each house buyer are presented on the related website, the problem of “how the house broker systematically recommends several suitable houses to each buyer, and assigns the most ideal person to each buyer for the brokerage service” is proposed and solved in this study, so as to promote the benefit and management performance from the service of the house brokerage. This study first applies analytic hierarchy process (AHP) to quantify the implicit needs of each buyer, search the house objects satisfying the explicit needs from AHP in the database, and give the objective score to each selected object. Then, the objective score combines the preferences of each house buyer to develop a multi-objective decision model of house-purchasing recommendation and customer allocation. (i.e., namely considering the objectives such as success probability, expected profit, expected service time, etc. of the combination of both house-buyer recommendation and buyer-broker matching) Further, based on the multi-objective integer linear programming (ILP) of the above decision model and evolutionary prototyping method, a web-based prototype system is developed and can offer ‘what-if analysis’ function to perform several managerial analyses. Finally, the results of analysis can be used to explain the management implications of the above problem and taken for references of future decision making.