The Effect of GA Redundancy on the Design of Reverse Logistics Network

Abstract

Genetic algorithm's approach has been one of the most important optimization techniques though the related problems of selection pressure still exist. Diversity is the key issue. For this reason, we explored the implication of redundancy in both genetic algorithm's overrepresentation and multi-explanation. Our research result supports the view of crosscompetition but not diversity loss when considering uniform coding redundancy with expressive elitism and the tolerance of infeasible solutions. On the other side, we developed new decoding procedure based on the inborn searchability of the small world. In designing the network of the reverse logistics having multiple lower-dimensional constraints, with the help of multiple overlapped fitness functions, the performance of genetic algorithms can truly be improved.

The Dark and Bright Sides of Complexity: A Dual Perspective on Supply Network Resilience

“…When a firm uses multiple carriers (i.e., has more arcs in its supply base), it can shift the movement of products swiftly across carriers (Tang 2006). Hence, the availability of more carriers enhances a firm’s flexibility to utilize its logistics network flexibly to avoid delays and swiftly recover from disruptions (Wing et al 1991; Chi et al 2007). Also, more complex logistics networks provide a greater inflow of information that may be used to address discreet disruption events (Nilsson 2006; Scholten et al 2014).…”