A MOEAD-Based Approach to Solving the Staff Scheduling Problem

Abstract

Due to the impact on increase of the utilization efficiency of the staff and decrease of operating cost of enterprises, the staff scheduling problem has attracted the interests of many scholars. Actually, the staff scheduling problem can be considered to be how to assign the right staff to the right shift on the right time period based on constraints, meanwhile the objectives should be optimized. Hence, designing an algorithm to satisfy all the requirements mentioned above is challenging. First, there are prohibitive combinations of assigning the staff to shifts from a sheer numbers perspective; Next, there are potential conflicts among optimization objectives, which means objectives may not reach the optimization at the same time and the optimal schedule can not be found; Finally, rare work about the fairness of optimization objectives has been studied. The existing works usually focus on optimization objectives in total, ignoring the fairness of them. A schedule with best optimization objectives can not provide the highest fairness. Hence, we propose an approach based on multi-objective evolutionary algorithm based on decomposition (MOEAD) to solve the staff scheduling problem in the fairness aspect. A series of experiments are performed and prove that the proposed method can effectively find the schedule with fairness.

PACAM: A Pairwise-Allocated Strategy and Capability Average Matrix-Based Task Scheduling Approach for Edge Computing

“…(4) Using real-life data (daily call arrivals in 6 months of 2020) and synthetic data (capabilities of devices), we experimentally verify the effectiveness and efficiency of our proposed method. Compared with five stateof-the-art methods for task scheduling, i.e., linear programming (LP [21]), integer programming (IP [22]), MIP [16]), improved particle swarm optimization (IPSO [23]), and multiobjective evolutionary algorithm-based decomposition (MOEAD [24]), we find that PACAM is at least two orders of magnitude faster than the above methods.…”

“…In this section, we experimentally evaluate the efficiency and effectiveness of our proposed solution PACAM against the state-of-the-art methods. We implement our algorithm in Python and adopt the Python implementation of all competitors based on the following methods: (1) LP [21], (2) IP [22], (3) MIP [16], (4) IPSO [23], and ( 5) MOEAD [24]. e solver used in this article is Gurobi solver 9.1 [25].…”

“…Considering some phenomenons in nature, heuristic algorithms abstracted this similarity into their methods to address the task scheduling problem [30]. Hong et al [24] modeled their resource scheduling problem as a multiobjective problem and adopted the multiobjective evolutionary algorithm based on decomposition (MOEAD) to generate feasible solutions. In addition, some works [31,32] used the nondominated sorting genetic algorithm (NSGA) to deal with the task scheduling problem containing multiple objectives.…”