Abstract
In the past decade, fairness in public procurement expert selection has attracted research attention. This paper proposes an immune evolutionary algorithm (IEA) with a punishment mechanism for expert selection, in which an ordered weighted aggregation (OWA) operator is applied to adjust the score weights to reduce expert evaluation committee abuse discretion and Grubbs method is employed to test the outliers. The results from a real-life public procurement case demonstrated that the abnormal experts could be effectively suppressed during the selection process and that the proposed method performed better than either the random selection algorithm or IEA, neither of which considers a punishment mechanism. Therefore, the proposed method, which applied the abnormal data detected in the scoring process to the expert selection process with a punishment mechanism, was proven to be effective in solving public procurement problems that may have doubtful or abnormal experts.
Highlights
Public procurement is used in the public sector for small items such as office desks and paper and large items or projects such as electricity, telecommunications, airports, railways, and other infrastructure projects
As bidding evaluation has a direct impact on the results and is a key step in the bidding process [4], choosing the appropriate public procurement experts is vital to ensuring quality
To ensure public procurement transparency and fairness, in this paper, we propose an immune evolutionary algorithm (IEA) with a punishment mechanism for expert selection, apply an ordered weighted aggregation (OWA) operator to adjust the weights of the final scores to reduce expert evaluation committee abuse discretion, and employ Grubbs method to test the outliers. e expert selection method applies any detected abnormal scoring process data to the expert selection process and introduces a punishment mechanism to identify the abnormal experts
Summary
Public procurement is used in the public sector for small items such as office desks and paper and large items or projects such as electricity, telecommunications, airports, railways, and other infrastructure projects. While it is difficult to determine whether kickbacks are being received, examining the public procurement scoring process could identify abnormal scoring and reduce or inhibit the probability of abnormal experts affecting the final supplier’s selection. To ensure public procurement transparency and fairness, in this paper, we propose an immune evolutionary algorithm (IEA) with a punishment mechanism for expert selection, apply an ordered weighted aggregation (OWA) operator to adjust the weights of the final scores to reduce expert evaluation committee abuse discretion, and employ Grubbs method to test the outliers.
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