Adaptive Hyper-heuristics Genetic Algorithm for Solving Stochastic Job-shop Problem
Stochastic job-shop scheduling problem(SJSSP) is a kind of stochastic programming problem which transformed from job-shop scheduling problem(JSSP).The current methods to solve SJSSP ignored characteristics of SJSSP,w hich lead to large solution times and inefficient solution.Aiming at the problem,Adaptive Hyper-Heuristics genetic algorithms(AHHGA) is proposed combing w ith characteristics of SJSSP to solve SJSSP w ith the objective to minimize the expected value of makespan.The outer loop of the proposed algorithms is to determine heuristics rules on each scenario in scenario set.The inner loop is that w ithin the hyper-heuristic framew ork,a genetic algorithm is employed on the high level and heuristics rules on each scenario in scenario set are used for constructing scheduling timetables are w ork on the low level.Thus,the proposed algorithm ensure to find a better solution in a limit search scope w ith respect to characteristics of SJSSP.FT benchmark-based problems w here the processing times are subjected to independent normal distributions are solved effectively by AHHGA.The experiment results achieved by AHHGA are compared w ith quantum-inspired genetic algorithm(QGA) and standard genetic algorithm(GA) and a novel competitive co-evolutionary quantum genetic algorithm(CCQGA),w hich show s that AHHGA has better feasibility and effectiveness.