A Novel Multi-Objective Nonlinear Discrete Binary Gaining-Sharing Knowledge-Based Optimization Algorithm: Optimum Scheduling of Flights for Residual Stranded Citizens Due to COVID-19

ABSTRACT

GSK algorithm is based on the concept of how humans acquire and share knowledge through their lifespan. Discrete binary version of GSK named novel binary gaining sharing knowledge-based optimization algorithm (DBGSK) depends on mainly two binary stages binary junior gaining sharing stage and binary senior gaining sharing stage with knowledge factor 1. These two stages enable BGSK for exploring and exploitation of the search space efficiently and effectively to solve problems in binary space. One of these practical applications is to optimally schedule the flights for residual stranded citizens due to COVID-19. The problem is defined for a decision maker who wants to schedule a multiple stepped trip for a subset of candidate airports to return the maximum number of residuals of stranded citizens remaining in listed airports while comprising the minimization of the total travelled distances for a carrying airplane. A nonlinear binary mathematical programming model for the problem is introduced with a real application case study. The case study is solved using DBGSK.