A hybrid genetic algorithm for agile supply chain scheduling optimization

The time and quantity constraints of each demand and the productivity and available scheduling periods of each supplier increase the complexity of Agile Supply Chain Scheduling(ASCS) problem. In order to resolve the ASCS optimization, this paper designs a Hybrid Genetic Algorithm(HGA) by combining common GA with greedy algorithm, which takes the sum cost of inventory and transportation of the supply chain as fitness function, period codes with the information of corporations and its producing part and its available scheduling periods as genetic codes, linear order crossover and inversion mutation as crossover operator and mutation operator separately, and uses greedy algorithm to help decoding and calculating fitness values to assure HGA that can achieve the problem´s optimal solution rapidly and steadily. Finally, a scheduling example verifies the practicality and effectiveness of the algorithm.