Swarm intelligence applied in green logistics: A literature review. (January 2015)

• Record Type: Journal Article
• Authors: Zhang, Shuzhu; Lee, C.K.M.; Chan, H.K.; Choy, K.L.; Wu, Zhang
• Is Part Of: Engineering applications of artificial intelligence. Volume 37(2015:Jan.)
• Page Start: 154
• Page End: 169
• Publication Date: 2015-01
• Subjects: Swarm intelligence -- Green logistics -- Reverse logistics -- Closed-loop supply chain -- Literature review -- Optimization; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2014.09.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Green logistics (GL) is gaining increasing attention among academic researchers and industrial practitioners, due to the escalating deterioration of the environment. Various green activities and operations aiming at improving the performance of GL have been applied synthetically, and most of the activities can be modeled as combinatorial optimization (CO) problems. Exact approaches tend to be incapable of solving the CO problems, especially with the increasing complexity. Thus, meta-heuristic approaches are widely adopted, which can generate a satisfactory solution within an acceptable time. Swarm intelligence (SI) is an innovative branch of meta-heuristics derived from imitating the behavioral pattern of natural insects. The distributed control mechanism and simple interactive rules can manage the swarm of insects effectively and efficiently. There are some pilot studies in applying SI into GL, which indicates that the integration of GL and SI could be a promising choice and of great potential. This research reviews the application of SI in GL through a comprehensive and extensive investigation and analysis of extant literature, which includes 115 publications in the last twenty years. The integration of GL and SI is analyzed from the perspective of both the problem context and the methodology. The categories of GL and SI are classified systematically. The CO problems of GL are further studied with SI algorithms, and innovative and universal guidance for algorithm customization in resolving CO problems emerges as well. Further potential research issues and opportunities of GL and SI are also identified in this research.