Evaluating environmental strategies in a textile printing and dyeing enterprise by an agent-based simulation model

Abstract

To improve the capabilities of saving energy and reducing pollutant emission of textile printing and dyeing (PD) industry, this article presents a novel agent-based simulation model for assessing the impacts of environmental strategies on a PD enterprise. Two typical PD enterprises in China are simulated with different modelling granularities: one is at a module level, while the other is at an enterprise level. The module-level simulation model depicts detailed production processes in a PD enterprise and evaluates five candidate strategies on their capabilities of improving energy usage and waste emission. The enterprise-level simulation model views a PD enterprise as an agent and assesses three tax strategies for waste discharge. The simulation results show that the proposed general model could be a valuable tool to explore potential solutions to saving energy and reducing waste emission in PD enterprises, after being calibrated to a real case.

Keywords:

• environmental strategy evaluation
• agent-based model
• textile industry
• complex adaptive system
• energy saving
• emission reduction

Acknowledgments

This study was supported in part by the Key Project of the National Nature Science Foundation of China (no. 61134009), the National Nature Science Foundation of China (nos 60975059 and 20906011), Specialised Research Fund for the Doctoral Program of Higher Education from Ministry of Education of China (no. 20090075110002), Project of the Shanghai Committee of Science and Technology (nos 11XD1400100, 10JC1400200 and 10DZ0506500) and China Postdoctoral Science Foundation (no. 20090460608). The authors wish to thank three anonymous reviewers for their constructive and valuable comments, which were very helpful in strengthening the presentation of this article.

Notes

Note

1. Currently, executing a replication of simulation experiment takes nearly an hour. The running time is expected to be longer with the increase of system complexity. So, we initially did an experiment and compared results from different replication sizes (20, 50, 100 and 200) under assessed scenarios for a shorter simulation period, and found that the averaged results were similar across these different replication sizes. Therefore, we settled for 20 as the replication size of choice to save time.