The increase in human resource cost puts forward higher requirements for the optimization of home appliance manufacturing processes. This paper studied an integrated human resource optimization problem considering the human resource selection, learning effect, skills degradation effect, and parallel production lines. There are multiple different manufacturing tasks with different normal processing times. Human resources have different abilities and costs. The actual processing time of a task is determined by its normal processing time, position, and ability of the human resource. The objective is to minimize production time and the labor cost. To solve the studied problem, we first consider the case where the human resources have been selected and assigned to the production lines. Then, some structural properties are proposed and a heuristic is developed to arrange tasks on every single production line. Also, we derive a lower bound for the problem. Since the investigated problem is NP-hard, a Variable Neighborhood Search is designed to solve the problem in a reasonable time. Finally, computational experiments are conducted and the experimental results validate the performance of the proposed methods.
Firefly algorithm (FA) is a swarm-intelligence-based, meta-heuristic algorithm and has been widely applied since its establishment in 2009. In this paper, a modified FA based on light intensity difference (LFA) is proposed. The light intensity of a firefly is determined by the landscape of the objective function in FA. The modifications are established in consideration of the variation trend of light intensity differences. As the light intensity differences vary with movements of fireflies, the parameter settings could be adjusted pertinently and self-adaptively at any moment for different problems. The applications to numeric experiments show that, LFA is well adaptive and efficient for different problems, and can make a trade-off between global exploration and local exploitation so as to decrease the risk of premature convergence effectively. 相似文献
In this paper, we present closed-form expressions, wherever possible, or devise algorithms otherwise, to determine the expectation
and variance of a given schedule on a single machine. We consider a variety of completion time and due date-based objectives.
The randomness in the scheduling process is due to variable processing times with known means and variances of jobs and, in
some cases, a known underlying processing time distribution. The results that we present in this paper can enable evaluation
of a schedule in terms of both the expectation and variance of a performance measure considered, and thereby, aid in obtaining
a stable schedule. Additionally, the expressions and algorithms that are presented, can be incorporated in existing scheduling
algorithms in order to determine expectation-variance efficient schedules. 相似文献