Available-to-promise based bidding decision by fuzzy mathematical programming and genetic algorithm

The present study attempts to integrate bidding decisions with order promising and production planning to enhance supplier profitability and service level. This study formulates the bid price and production plan as a mixed integer programming model with fuzzy constraints. The fuzzy constraints represent the decision-maker’s subjective judgment regarding the customer’s price tolerance. The proposed model combines the advanced available-to-promise (AATP) concept to find optimum resource allocation and enable accurate estimations of production costs and delivery dates. The proposed solution procedure determines the optimum bid price by striking a compromise between profitability and the possibility to win the contract. This study develops a genetic algorithm to solve this problem, and provides computer simulated experiments to evaluate the performance of the proposed approach.

► We formulate a model for suppliers to integrate bidding decisions with production planning. ► The model combines the advanced available-to-promise concept to find optimum resource allocation. ► We develop a genetic algorithm to solve the problem and conduct experiments to evaluate its performance. ► Results show the algorithm can obtain satisfactory solutions and efficiently solve large problems.