Multi-objective demand fulfillment problem for solar cell industry

Abstract With the growth of green energy market, there are many companies with vertical integration and outsourcing process. Their business models in the solar cell industry cause different influence on the business objectives including revenue, profit, and gross margin. The sales managers are hard to generate fast and reliable ATP (Available-to-promise) which covered all business objectives. Under the various products types with limited quantity, the loading of factory is uncertain and the quantity of order becomes huger. This study formulates multi-objective demand fulfillment problem in solar cell industry and applies a multi-objective genetic algorithm (moGA) to optimize the ATP. The model trade-offs objectives with by Pareto approach without sacrificing other benefits. The multi-population strategy used for stability of solutions. Results of empirical study show that the proposed moGA can improve gross margin while maintaining the desired revenue and profit. With effective solutions and reasonable computing time, the decision support system with proposed moGA has become a regular communication bridge between sales and production departments in the case company.