A PDDE-Based Order Scheduling Optimization with Raw Material Uncertainty

Abstract

An adaptive order scheduling system is of great importance for the successful implementation of production planning in dynamic make-to-order production environment where a variety of unexpected disruptions is usually inevitable. This paper investigated the scheduling problem with uncertain arrival of raw materials and limited production capacity. A Pareto discrete differential evolution (PDDE) approach is proposed to generate the approximate optimum scheduling solution with stochastic arrival of raw materials. The PDDE algorithm adopts Pareto selection strategy to improve adaptability of the PDDE algorithm upon evolving towards the global optimal solution and integrates the stochastic simulation model and utility function into the fitness evaluation of the individuals. The experimental results demonstrate that the proposed PDDE optimization model outperforms the industrial practice and has self-adaptation and fitness capacity to responsively self-adjust upon the uncertain arrival of raw material.