Simultaneous order scheduling and mixed-model sequencing in assemble-to-order production environment: a multi-objective hybrid artificial bee colony algorithm

In today’s competitive manufacturing market, effective production planning and scheduling are crucial to streamline production and increase profit. Successful production planning can achieve efficient capacity utilization and fulfill customer demand in a timely manner. For assemble-to-order companies, the assembly production planning is mainly driven by customer orders. In literature, the master production schedule which assigns production orders of individual models to production intervals is generally treated independently from the product sequencing, which might lead to local optimization for the final assembly schedule. In this paper, both order scheduling and mixed-model sequencing are taken into account simultaneously to formulate the final assembly schedule. Three objectives are concurrently considered including, maximizing net profit earned from orders, reducing sequence-dependent setup time between different models and leveling material usage. A novel multi-objective hybrid artificial bee colony (MHABC) algorithm combined with some steps of genetic algorithm and the Pareto optimality is developed to solve the current problem. Experiments are conducted and performance of the proposed MHABC algorithm is examined with the improved strength Pareto evolutionary algorithm (SPEA2). The results indicate that the proposed MHABC performs better as compared to the SPEA2 and gives better Pareto optimal solutions. Finally, a practical case problem from an engineering machinery company is solved with the proposed approach for simultaneous order scheduling and mixed-model sequencing.