An Intelligent Model for Solving Manpower Scheduling Problems
This addresses resource management challenges in human resource coordination for organizations, though it appears incremental as it builds on existing scheduling solutions.
The paper tackles the manpower scheduling problem by transforming it into a multi-constraint combinatorial optimization problem and using an improved multi-dimensional evolution algorithm, achieving up to 25.7% efficiency and 17% accuracy gains compared to existing methods, with a new algorithm providing at least 28.91% time efficiency improvement.
The manpower scheduling problem is a critical research field in the resource management area. Based on the existing studies on scheduling problem solutions, this paper transforms the manpower scheduling problem into a combinational optimization problem under multi-constraint conditions from a new perspective. It also uses logical paradigms to build a mathematical model for problem solution and an improved multi-dimensional evolution algorithm for solving the model. Moreover, the constraints discussed in this paper basically cover all the requirements of human resource coordination in modern society and are supported by our experiment results. In the discussion part, we compare our model with other heuristic algorithms or linear programming methods and prove that the model proposed in this paper makes a 25.7% increase in efficiency and a 17% increase in accuracy at most. In addition, to the numerical solution of the manpower scheduling problem, this paper also studies the algorithm for scheduling task list generation and the method of displaying scheduling results. As a result, we not only provide various modifications for the basic algorithm to solve different condition problems but also propose a new algorithm that increases at least 28.91% in time efficiency by comparing with different baseline models.