Integrating machine layout, transporter allocation and worker assignment into job-shop scheduling solved by an improved non-dominated sorting genetic algorithm

To meet the everchanging production demand and close to the actual scheduling environment, a multi -objective job-shop scheduling problem (MOJSP) with multiple resource constraints is investigated. Specifically, an integrated mathematical model is constructed based on a job-shop scheduling problem (JSP) considering machine layout rearrangement, transporter allocation with capacity limitation, and worker assignment with skill variance to simultaneously minimize the exit time, labor cost, worker workload difference, and trans-portation time. To tackle the concerned problem, an improved non-dominated sorting genetic algorithm with a hybrid local search (INSGA-HLS) is designed. In the numerical simulation, a test dataset is first constructed according to the literature. Second, an orthogonal experiment is utilized to find the best combination of key parameters for INSGA-HLS. Third, the exploitation competence of the proposed hybrid local search (HLS) is verified. Then, the superiority of the designed INSGA-HLS algorithm is demonstrated by comparing it with the other intelligent algorithms. Thereafter, the influence of three considered factors on the integrated scheduling problem is illustrated: (1) flexible machine layout rearrangement may reduce the handling time, and improve production efficiency to some extent; (2) transporter capacity limitation can prolong the transport time, which extends the exit time; and (3) worker assignment with skill variance can not only improve productivity and reduce the labor cost, but also narrow the workload difference to improve employee satisfaction. Finally, an actual production line application further verifies the practicability and benefit of the model, and the managerial significance is analyzed.

Automated summary

This study investigates the multi-objective job-shop scheduling problem with multiple resource constraints and proposes an integrated mathematical model considering machine layout rearrangement, transporter allocation with capacity limitation, and worker assignment with skill variance. An improved non-dominated sorting genetic algorithm with a hybrid local search is designed to solve the problem. The numerical simulation and actual production line application demonstrate the effectiveness and practicability of the proposed model.