Order Acceptance and Scheduling Model Based on Improved Genetic Algorithm

Abstract

As the manufacturing industry’s industrial structure continues to evolve, many enterprises have shifted from traditional make-to-store modes (MTS) to make-to-order modes (MTO). This paper focuses on MTO manufacturing enterprises’ order acceptance and scheduling problem, namely, how to exclude low-margin orders under capacity constraints and schedule them. We establish a hypothetical model with the goal of maximizing corporate profits, taking into account production capacity constraints and delivery time constraints. To solve the model, we propose a heuristic solution method based on genetic algorithms. The feasibility of the method is verified through randomly generated examples. The research results demonstrate that our proposed solution method can quickly solve large-scale order decision-making problems and has practical significance as a guiding tool.