6. Conclusions and further work
Because of the deterioration of environment and the consumption of the finite and diminishing energy sources, green logistics is gaining increasing attention substantially. Economic performance is no longer the only objective in logistics; two other aspects, environmental and societal performance, are becoming more important than ever before for the purpose of sustainable development. The operational level activities in logistics are the fundamental for any upper management strategies, thus the modeling and implementing of green activities are of great importance. In this research, the green activities are classified into different dimensions and categories considering their inherent features. Swarm intelligent algorithms of meta-heuristics are adopted to handle the abstracted mathematical models of green activities. The literature review reveals the integration of green logistics and swarm intelligence. Moreover, an innovative and universal guidance is provided for the implementation of swarm intelligent algorithms when solving green logistics problems.
The contribution of this research can be described by the following aspects. First, a comprehensive and extensive literature review regarding the integration of green logistics and swarm intelligence is provided, which can help to understand the current research status in both areas and enrich the knowledge base of this interdisciplinary study. The classification scheme of operational-level activities in green logistics provides an intuitive description that helps to identify potentially fruitful research areas. Some green logistics categories have been accumulated a number of experienced studies, which others are still at the starting stage. Readers could identify their interested areas referring to the results of this literature review. Apart from the green logistics, this research also presents a straightforward description of the application of swarm intelligent algorithms. For instance, the ACO algorithm accounts for more 50% comparing with the other two swam intelligence technqiues, which may attribute to the historical accumulation and its intrinsic features. Referring to the applications of these three typical examples, more swarm intelligent algorithms could be further adapted. In addition, considering the various swarm intelligent algorithms and their variants, we analyzed their inner driving force for progression from a balancing perspective, through fully investigating their intrinsic features. The diversification and intensification effect of each operator in swarm intelligence algorithms are considered. It leads to another potential contribution which is algorithm customization for the specific problems.
Due to the predetermined conditions of literature collection, there may be other useful resources which are not covered in this research. Only the operational-level activities in green logistics are considered in this paper, however the application of swarm intelligence in the strategic or tactical level management of green logistics could be another research direction. In the proposed algorithm framework, only a limited number of swarm intelligent algorithms are analyzed, the other meta-heuristic algorithms, e.g. tabu search, simulated annealing, etc. may also be analyzed following the same procedure, and could contribute to a more comprehensive algorithm framework.