Integrated lot-sizing and scheduling for parallel mixed-model automotive production lines with transportation resource constraints

The automotive industry is undergoing rapid transformation, accompanied by intensifying market competition. As a result, component manufacturers face mounting pressure to improve production efficiency and control costs, particularly in high-mix, low-volume manufacturing environments. This paper investigates the integrated lot-sizing and scheduling problem in a mixed-flow parallel production system for automotive components, with a particular constraint related to the availability and management of transportation resources during production. The connection between lot-size scheduling and transportation resource allocation is crucial to the overall efficiency of the system. Therefore, a novel mixed integer programming model is presented for the multi-objective problem to minimize the makespan, total tardiness, total setup cost, and inventory levels. The min–max normalization approach is applied to normalize the objectives with different dimensions. A hybrid sparrow search algorithm (HSSA) is proposed with multi-strategy fusion, i.e., integrated mutation strategy, particle swarm velocity position update strategy, Gaussian Cauchy perturbation strategy, and multi-point crossover strategy. Two instances with varying scales have been designed to assess the performance of the proposed algorithm in the context of a practical automotive components production environment. The parameters of the HSSA for different sizes of problems are tuned by Taguchi method. Performance evaluation of the proposed HSSA is carried out by conducting extensive experiments compared to three well-established algorithms. The results demonstrate the effectiveness and superiority of the proposed HSSA in solving the simultaneous lot-sizing and scheduling problem with transportation resource constraints.