A combined algorithm for selection of optimal bidder(s)

Abstract

Organizations (manufacturing, service, healthcare etc.) buy goods and services for their operational needs from reliable and subject-optimized companies frequently. In fact, procurement processes follow stringent procedures to ensure that the process is fair and efficient with a minimal wastage of resources. The main challenge of all such procurement processes is the selection of optimal bidder(s) such that they satisfy the ‘best value for money’ criteria than accomplishing the ‘best optimized price’ criteria alone. In fact, choosing an optimal bidder requires, in general, consideration of both technical and financial criteria with appropriate assignment of weightage for each. In this work, an innovative approach is proposed where only an optimal set of shortlisted bidders is made to undergo quality and cost-based selection (QCBS) evaluation procedure. The proposed method combines a few significant algorithms together for effective results. These algorithms are studied and implemented in the following order: hyper sphere cluster density (HSCD) algorithm; K-means algorithm; artificial neural network (ANN) model and QCBS evaluation model. Initially, the coupling of HSCD algorithm with standard K-means is done for creating robust and reliable non-overlapping clusters. Then, the multi-layer perceptron (MLP) model of ANN is applied to determine optimal cluster from set of all non-overlapping clusters. This model is effective in reducing the laborious task of unnecessarily evaluating all the shortlisted bidders. Next, the QCBS evaluation is done to select the optimal bidder from the already chosen optimal cluster. Numerical example is considered to demonstrate the proposed procedure. Several conclusions are drawn based on which recommendations for the application of the proposed method are made.