A multi-objective model for multi-project scheduling and multi-skilled staff assignment for IT product development considering competency evolution

Abstract

We address a multi-skill project scheduling problem for IT product development in this article. The goal is for product development managers to be able to generate an initial schedule at an early stage of development activities. Due to the complexity of the product structure and functionality, an IT product development effort is divided into multiple projects. Each project includes several tasks, and each task must be completed by an employee who has mastered a certain skill to complete it. A pool of multi-skilled employees is available, and the employees’ skill efficiencies are influenced by both learning and forgetting phenomena. Based on the real-world demands of product development managers, three objectives are simultaneously considered: skill efficiency gain, product development cycle time and costs. To solve this problem, we propose a multi-objective non-linear mixed integer programming model. The Non-dominated Sorting Genetic Algorithm II (NSGA-II)is designed to generate an approximation to the optimal Pareto front of this NP-hard multi-objective optimisation problem. The algorithm produces feasible schedules for all the development projects using the serial schedule generation scheme. We adopt penalty values and individual employee adjustments to address resource conflicts and constraint violations. A weighted ideal point method is used to select the final solution from the approximate Pareto solution set. An application case of a new electrical energy saving product implementation in a leading electrical device company in China is used to illustrate the proposed model and algorithm.

Keywords:

• multi-project scheduling
• multi-objective optimisation
• staff assignment
• multi-skilled staff
• skill evolution
• IT product development

Notes

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study is part of research work of National Natural Science Foundation of China [grant number 71331002], [grant number 71271072], [grant number 71521001], [grant number 71301040], [grant number 71601061].