Research on multi-objective optimal scheduling considering the balance of labor workload distribution

Area of Science:

• Operations Research
• Industrial Engineering
• Manufacturing Systems

Background:

• Unbalanced employee workloads in parallel flow shop scheduling lead to inefficiencies.
• Existing scheduling models often fail to adequately address both completion time and workload balance.
• Optimizing for minimum delay and equitable work distribution is crucial for manufacturing efficiency.

Purpose of the Study:

• To propose a novel method for job standard balance to improve employee workload distribution in parallel flow shop scheduling.
• To develop a bi-objective nonlinear integer programming model minimizing both completion delay and work imbalance.
• To evaluate the effectiveness of newly designed heuristic algorithms against existing methods.

Main Methods:

• Development of a job standard balance metric to quantify employee workload.
• Formulation of a bi-objective nonlinear integer programming model.
• Design and implementation of NSGA-II-EDSP, NSGA-II-KES, and NSGA-II-QKES heuristic algorithms.
• Conducting computational experiments with varying problem sizes.

Main Results:

• The proposed job standard balance method effectively addresses workload imbalance.
• The developed bi-objective model successfully optimizes for minimum delay and work balance.
• The heuristic algorithms demonstrated superior performance compared to the standard NSGA-II.
• Significant error reductions of 14.56%, 15.16%, and 15.67% were achieved.

Conclusions:

• The proposed job standard balance method and bi-objective model offer a significant improvement for parallel flow shop scheduling.
• The developed heuristic algorithms provide efficient solutions for complex scheduling problems.
• This research contributes to more equitable and efficient workforce management in manufacturing environments.