A Hybrid Cellular Genetic Algorithm for Multi-objective Crew Scheduling Problem

Abstract

Abstract. Crew scheduling is one of the important problems of the airline industry. This problem aims to cover a number of flights by crew members, such that all the flights are covered. In a robust scheduling the assignment should be so that the total cost, delays, and unbalanced utilization are minimized. As the problem is NP-hard and the objectives are in conflict with each other, a multiobjective meta-heuristic called CellDE, which is a hybrid cellular genetic algorithm, is implemented as the optimization method. The proposed algorithm provides the decision maker with a set of non-dominated or Pareto-optimal solutions, and enables them to choose the best one according to their preferences. A set of problems of different sizes is generated and solved using the proposed algorithm. Evaluating the performance of the proposed algorithm, three metrics are suggested, and the diversity and the convergence of the achieved Pareto front are appraised. Finally a comparison is made between CellDE and PAES, another meta-heuristic algorithm. The results show the superiority of CellDE.

Solving the Job-Shop Scheduling Problem Based on Cellular Genetic Algorithm

“…As a result, cGA draws the attention of many scholars. In recent years, cGA has been successfully applied in the NP-hard problems, such as VRP, SAT problem [4].In addition, cGA is applied in many other different fields, such as logic, information, scheduling, theory problems [5][6].LI etc. use cGA to optimize the area covering of wireless sensor networks [7].NI etc.…”