Advanced planning for minimizing makespan with load balancing in multi-plant chain

Abstract

This paper deals with the advanced planning problem for minimizing makespan with workload balancing considering capacity constraints, precedence relations, and alternative resources with different operation times in a multi-plant chain. The problem is formulated as a multi-objective mixed integer programming (mo-MIP) model which determines the operations sequences with resource selection and schedules. In this model, a single unique solution does not exist since the objectives may be conflicting, which have to be globally minimized with respect to the two objectives. For effectively solving the alternative solutions of the advanced planning model, we develop an adaptive genetic algorithm (aGA) approach with the adaptive recombination functions and the revised adaptive weighted method. The experimental results are presented for the advanced planning problems of various sizes to describe the performance of the proposed aGA approach. The performance of the aGA approach is also compared with that of the Moon, Li and Gen (MLG) method.

Keywords:

• Advanced planning
• Supply-chain management
• Multi-plant chain
• Multi-objective mixed integer programming model
• Adaptive genetic algorithm

Acknowledgements

This work is supported in part by a fund from the Basic Research Program (grant no. R01-2002-000-00232-0) of the Korea Science & Engineering Foundation and by the University of Ulsan Research Fund of 2002.