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Abstract
When planning production in a centralized decision-making environment using data envelopment analysis (DEA), previous researches usually plan for units by selecting best-practice points within the entire production possibility set or adhering to their original abilities so that potentials may not be fully explored. In practice, there often exist factors that influence units’ production abilities. Difficulties may occur when improving inefficient units’ performances or they can only be improved in a limited room. This paper takes these influencing factors into account to avoid new plans beyond units’ abilities or not fully exploring their potentials. Depending on performance variability, two DEA-based production planning approaches are proposed to optimize the total resource utilization assuming demand changes in the next production season can be forecasted. When performances are improvable, units are grouped according to the influencing factors they face. Simple numerical examples and a real world data set are used to illustrate the proposed approaches.
Acknowledgements
This work was supported by the National Natural Science Foundation of China (70971137).