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A platform for research: civil engineering, architecture and urbanism
Hybrid Bayesian Fuzzy-Game Model for Improving the Negotiation Effectiveness of Construction Material Procurement
Price negotiation is commonly required to reach a final contractual agreement during the procurement of construction material. However, uncertain and limited supplier information as well as complex correlations among various factors affects supplier behaviors, making learning a supplier’s negotiation strategy and deciding the appropriate offer price difficult for contractors. Therefore, a multistrategy Bayesian fuzzy-game model (MBFGM) that can be applied in forecasting a supplier’s negotiation strategy was developed in this study. A validation analysis revealed that incorporating limited objective data and previous knowledge from experts into the Bayesian learning process can facilitate effectively determining causal relationships in a network as well as improving the accuracy of a learned model. By using the proposed model, contractors can effectively foresee the relationship between its alternative offer prices (OPs) and a supplier’s future bidding strategies. An experiment in which construction practitioners participated revealed that contractors can benefit by applying the forecasting ability of the model to increase the success rate and profit, reduce the time spent in unnecessary negotiation, and improve negotiation efficiency in the construction material procurement process.
Hybrid Bayesian Fuzzy-Game Model for Improving the Negotiation Effectiveness of Construction Material Procurement
Price negotiation is commonly required to reach a final contractual agreement during the procurement of construction material. However, uncertain and limited supplier information as well as complex correlations among various factors affects supplier behaviors, making learning a supplier’s negotiation strategy and deciding the appropriate offer price difficult for contractors. Therefore, a multistrategy Bayesian fuzzy-game model (MBFGM) that can be applied in forecasting a supplier’s negotiation strategy was developed in this study. A validation analysis revealed that incorporating limited objective data and previous knowledge from experts into the Bayesian learning process can facilitate effectively determining causal relationships in a network as well as improving the accuracy of a learned model. By using the proposed model, contractors can effectively foresee the relationship between its alternative offer prices (OPs) and a supplier’s future bidding strategies. An experiment in which construction practitioners participated revealed that contractors can benefit by applying the forecasting ability of the model to increase the success rate and profit, reduce the time spent in unnecessary negotiation, and improve negotiation efficiency in the construction material procurement process.
Hybrid Bayesian Fuzzy-Game Model for Improving the Negotiation Effectiveness of Construction Material Procurement
Leu, Sou-Sen (author) / Son, Pham Vu Hong (author) / Nhung, Pham Thi Hong (author)
2014-09-04
Article (Journal)
Electronic Resource
Unknown
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