An uncertain goal programming model for machine scheduling problem

Machine scheduling is to assign a group of jobs to a set of machines in an efficient strategy, such that some objectives such as minimizing the makespan time are satisfied under some constraints. Considering the human uncertainty in operations, this paper assumes the processing times of the jobs are uncertain variables, and proposes an uncertain goal programming model for the machine scheduling problem, in which each machine is supposed to finish all its jobs before a predetermined time under the cost constraints. A crisp equivalent model is obtained, and an intelligent algorithm is introduced to solve the equivalence based on a revised genetic algorithm. In addition, a numerical experiment is given to illustrate the efficiency of the intelligent algorithm.