Research on Distributed Heterogeneous Factory Task Assignment Problem With Loading Efficiency Constraints: A Case Study

Abstract

In a distributed heterogeneous factory (DHF), an order can be assigned to multiple factories for production. Although this method reduces production costs, it can cause reduced loading efficiency and increase transportation costs. Balancing loading efficiency and total cost is a key issue that must be resolved in the task assignment problem (TAP) for DHF. This study investigates the DHFTAP with total cost objective and loading efficiency constraints. To solve this problem, three mixed integer linear programming (MILP) models are constructed based on different production strategies: Model I: split orders based on the combination of factory and warehouse; Model II: merge orders based on product types; Model III: split orders based on tasks. In the experiment, three MILP models and three evolutionary algorithms (EAs) are compared with the rule-based traditional method, and the sensitivity of minimum loading efficiency and the impact of factory load rate are analyzed. The experimental results reveal that MILP and EA can effectively solve the researched problem. Finally, valuable suggestions are provided for enterprises.

Keywords

• Distributed heterogeneous factory
• task assignment problem
• mixed integer linear programming
• evolutionary algorithm