Search

This paper studies a multi-attribute reverse auction in which one manufacturer/buyer purchases multi-unit identical components/goods from a group of capacity-constrained suppliers considering the procurement cost and delivery time. The unknown bidding preference and the discrete cost structure are particularly investigated. Constructing a bi-level distributed decision-making model, a novel iterative multi-attribute reverse auction mechanism embedding negotiation is proposed to improve the procurement efficiency under the decision-making framework. Specifically, in the upper level, the buyer determines the optimal allocation by solving the winner determination problem. To induce suppliers to adjust their delivery times, three guiding strategies are proposed, i.e., the guiding strategy based on the deviation of delivery time (GDD), the guiding strategy based on the deviation of objective function value (GDF), and the guiding strategy randomly based on the deviation of objective function value (GRDF). In the lower level, suppliers adopt the concession strategies for determining the bid price and delivery time in response to the buyer's feedback. The numerical experiments illustrate the effectiveness and applicability of the proposed mechanism by comparing it with the centralized model. When the buyer places higher importance on the procurement cost than on the delivery time, the GRDF achieves the best negotiation outcome; otherwise, the GDF is the buyer's best option. Also, the proposed mechanism is robust to the variance of suppliers' decision parameters and could be a useful procurement tool for the buyer.

We consider the problem of ranking the top m designs out of k alternatives. Using the optimal computing budget allocation framework, we formulate this problem as that of maximizing the probability of correctly ranking the top m designs subject to the constraint of a fixed limited simulation budget. We derive the convergence rate of the false ranking probability based on the large deviation theory. The asymptotically optimal allocation rule is obtained by maximizing this convergence rate function. To implement the simulation budget allocation rule, we suggest a heuristic sequential algorithm. Numerical experiments are conducted to compare the effectiveness of the proposed simulation budget allocation rule. The numerical results indicate that the proposed asymptotically optimal allocation rule performs the best comparing with other allocation rules.