A genetic algorithm heuristic approach to general outsourcing capacitated production planning problems

Abstract

This paper addresses a dynamic capacitated production planning (CPP) problem with consideration of outsourcing. Specifically, the outsourcing problem considered in this paper has the following features: (1) all demands are met by production or outsourcing without postponement or backlog, (2) production, inventory, and outsourcing levels all have a limit, and (3) the cost functions are considered arbitrarily and time-varying. These features come together, leading to a so-called general outsourcing CPP problem. In our previous work, an algorithm with pseudo-polynomial time complexity was developed, which includes a formation of a feasible solution region and then a search procedure using dynamic programming techniques. Due to the computational complexity with such an approach, only small and medium problems can be solved in a practical sense. In this paper, we present a genetic algorithm (GA) approach to the same problem. The novelty of this GA approach is that the idea of the feasible solution region is used as a heuristic to guide the searching process. We present a computational experiment to show the effectiveness of the proposed approach.

Keywords:

• Capacitated production planning
• Outsourcing
• Inventory
• Genetic algorithms

Acknowledgements

This research is partially supported by the National Science Foundation of China (Grant No. 70571077 and 70431003) and the Natural Sciences and Engineering Research Council of Canada (Grant No. JIIRP312747-04).