從雙贏策略以粒子群聚演算法探討營建協商總體效用最佳化

Abstract

Negotiation is a common required procedure in the procurement of construction materials between contractors and suppliers to reach the final contractual agreement. However in current practice, a traditional negotiation often end in suboptimal final results, therefore “leaving money on the table”. Previous research has been conducted by using Genetic algorithm to find the best joint payoff of the parties, and web-based development to improve the negotiation efficiency. This research presents two alternate optimization algorithms, namely PSO and BBPSO in the effort to obtain better mutually beneficial agreements. This study shows an improvement of the best joint payoff resulted, compared with previous related study using GA as its optimization. Moreover, the negotiation optimizations using PSO and BBPSO also reach better speed of convergence, from 12th generation to 58th generation (PSO), and from 7th generation to 21st generation (BBPSO) in four simulated scenarios. In addition, two alternate approach based on win-win perspective in counter to the contractors’ dissatisfaction of the results are also discussed. It shown by sacrificing small amount of joint payoff from 0% to 4.3% (PSO) and from 0% to 5.6% (BBPSO), the difference between the two payoffs can be minimized from 75% to 100% (PSO) and from 0% to 99.8% (BBPSO).

Negotiation is a common required procedure in the procurement of construction materials between contractors and suppliers to reach the final contractual agreement. However in current practice, a traditional negotiation often end in suboptimal final results, therefore “leaving money on the table”. Previous research has been conducted by using Genetic algorithm to find the best joint payoff of the parties, and web-based development to improve the negotiation efficiency. This research presents two alternate optimization algorithms, namely PSO and BBPSO in the effort to obtain better mutually beneficial agreements. This study shows an improvement of the best joint payoff resulted, compared with previous related study using GA as its optimization. Moreover, the negotiation optimizations using PSO and BBPSO also reach better speed of convergence, from 12th generation to 58th generation (PSO), and from 7th generation to 21st generation (BBPSO) in four simulated scenarios. In addition, two alternate approach based on win-win perspective in counter to the contractors’ dissatisfaction of the results are also discussed. It shown by sacrificing small amount of joint payoff from 0% to 4.3% (PSO) and from 0% to 5.6% (BBPSO), the difference between the two payoffs can be minimized from 75% to 100% (PSO) and from 0% to 99.8% (BBPSO).