multiple items supplier selection, economic lot-sizing, and order allocation under quantity discount: a genetic algorithm approach

The task at hand involves selecting the most suitable supplier(s), determining the optimal lot size, and allocating the total order quantities among the suppliers based on various selection criteria. however, this can become more complex when taking into account quantity discount offers and transportation selection decisions in the selection and order allocation process. to address this challenge, this paper proposes an integrated approach that combines the analytic hierarchy process (ahp) with a multi-objective mixed integer nonlinear program. the approach is designed for a multi-item, capacitated multi-supplier scenario, where the goal is to select suppliers, determine lot sizes, and allocate orders while taking into account unit quantity discounts and intermodal freight costs. the proposed approach aims to minimize costs and the percentage of rejected items, while maximizing the purchasing value. to solve this problem, an efficient genetic algorithm with problem-specific operators is utilized.