B2B multi-attribute e-procurement: an artificial immune system based goal programming approach.

Shukla, M.; Chan, F.T.S.; Tiwari, M.K.; Shankar, R.; Choy, K.L.

Abstract

This paper presents an artificial immune system (AIS) based goal programming approach for a multi-attribute e-procurement system. Current trends reveal that procurers are now concerned with various attributes of supplier selection, rather than negotiating only on cost. The scenario considered in this paper pertains to the procurement of an homogenous item in a large quantity. In these circumstances, procurers are forced to incorporate multi-attribute bids, dynamics pricing, related business requirements and multiple criteria in bid evaluation. The prime objective is to decide on the supplier and the quantity to be procured from the selected supplier. The problem considered here is NP hard, even without taking into account business constraints, and it becomes computationally prohibitive with an increase in the number of bids and the number of attribute values. In order to solve this problem with minimal computational time and effort, an evolutionary algorithm AIS with a goal programming technique is adopted. The working of the AIS based goal programming approach is evaluated by implementing it for a few simulated problem instances of changing complexity. The effectiveness of the proposed approach is established by a comparative study with other established evolutionary approaches such as a genetic algorithm and a simulated annealing algorithm.

Citation

Shukla, M., Chan, F., Tiwari, M., Shankar, R., & Choy, K. (2011). B2B multi-attribute e-procurement: an artificial immune system based goal programming approach. International Journal of Production Research, 49(2), 321-341. https://doi.org/10.1080/00207540902922802

• Journal Article Type: Article
• Publication Date: 2011-01
• Deposit Date: Dec 1, 2014
• Journal: International Journal of Production Research
• Print ISSN: 0020-7543
• Electronic ISSN: 1366-588X
• Publisher: Taylor and Francis Group
• Peer Reviewed: Peer Reviewed
• Volume: 49
• Issue: 2
• Pages: 321-341
• DOI: https://doi.org/10.1080/00207540902922802
• Public URL: https://durham-repository.worktribe.com/output/1419111