# A New Modified Firefly Algorithm for Optimizing a Supply Chain Network Problem
## 4 Conclusions  
The search-based character of metaheuristics algorithms indicates that they might lose some of their speed when all searching parameters are needed. For experimental investigations and strategic decisions, this matter can be less of a concern. Nevertheless, for instant purposes like a real-life dynamic emergency vehicle routing problem in which every single second counted, extra moments of required time may make the approach improper to employ, even if a better solution can be found.  
This study presented a new modification for the standard FA. A mixed integer linear programming model was developed for a supply chain network problem to demonstrate the efficiency of the proposed model. The findings showed that the proposed algorithm performs more efficient compared to classical FA. A common issue in running the standard FA is it usually stuck in stagnation or fall in premature convergence (fall into the local optimum) in searching space, especially for large size problems. This issue is connected with too much exploration and exploitation and inherent in the standard FA formulation. The proposed modification in this study resolves this issue by providing a balance between the exploitation and exploration in the standard FA.  
The generalisability of these results is subject to certain limitations. For instance, the developed model was formulated as mixed integer linear programming. In order to develop the model further,  
Figure 5: Performances comparison of modified FA and FA.  
care should be taken for nonlinear models. Nonlinear behaviour of parameters and variables might also result in different findings.  
The obtained results of implementing metaheuristics approaches are very sensitive to their parameters. Consequently, a little change in their parameters may influence the quality of the obtained solutions. As a result, a fine-tuning procedure for the parameters is needed to find better solutions. However, parameter tuning has almost neglected in all studies in the area under investigation by this research and the previous studies in the area generally have found metaheuristics parameters setting using a trial and error procedure. A further study could assess the effects of parameters' setting on the performance of the proposed algorithm.  
Methodology, A.M.; software, A.M.; validation, A.R.A.R., R.A. and M.R.A.J.; formal analysis, A.M.; investigation, A.R.A.R.; resources, M.R.A.J.; data curation, A.M.; writing--original draft preparation, A.M.; writing--review and editing, A.M.; visualization, M.R.A.J.; supervision, R.A.; funding acquisition.  
This research was funded by Universiti Teknologi Malaysia (UTM) and Ministry of Higher Education (MOHE) Malaysia under Professional Development Research University (PDRU) grant Vot Q.J130000.21A2.04E15, for the project \"An optimization model of oil-palm biomass supply chain for sustainable power generation in Malaysia\" **00**Q.K130000.2601.15J23.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results.