A Chaotic Bee Colony approach for supplier selection-order allocation with different discounting policies in a coopetitive multi-echelon supply chain
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Competitive models offer superiority in maximizing only a buyer’s profit, and do not satisfy all members in a supply chain. However, coordinative models give benefit to the whole supply chain. Research has been carried out the application of these two types of models in the supplier selection problems. In this study, we have considered coopetition in a supply chain, with the objective of selecting a supplier from a pool of suppliers and allocating optimal order quantities for the acquisition of a firm’s total requirements for a particular product. The competition in a one buyer- multiple suppliers system in the supplier selection process has been considered by applying mixed-integer nonlinear programming in first phase. On the other hand, the total cost to the whole supply chain is minimized rather than only for the buyer. Genetic Algorithm, Artificial Bee Colony, and Chaotic Bee Colony are used separately in the second phase as optimization techniques. We find that the All Units discount scheme is more preferable than the Incremental Units discount scheme. However, in the case for different values of the discount percentage and levels, or when supplier provides different type of scheme, other policies need to be explored. Finally, the proposed model is illustrated by a numerical example from the literature. A better result is found for the buyer’s cost by applying the proposed two-phase method, while the result is comparable result for the supply chain cost.
KeywordsArtificial intelligence Chaotic Bee Colony Coopetition Expert system Supply chain management
The work described in this paper was substantially supported by a grant from The Hong Kong Scholars Program Mainland–Hong Kong Joint Postdoctoral Fellows Program (Project No.: G-YZ24); and The Hong Kong Polytechnic University Research Committee for financial and technical support through an internal Grant (Project No. G-UB03).
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