Open Access

ARTICLE

A Compromise Programming to Task Assignment Problem in Software Development Project

Ngo Tung Son^1,2,*, Jafreezal Jaafar¹, Izzatdin Abdul Aziz¹, Bui Ngoc Anh², Hoang Duc Binh², Muhammad Umar Aftab³

1 Department of Computer and Information Sciences, Universiti Teknologi Petronas, Seri Iskandar, 32610, Malaysia
2 Department of Information and Communication Technology, FPT University, Hanoi, 100000, Vietnam
3 Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot, 35400, Pakistan.

* Corresponding Author: Ngo Tung Son. Email:

(This article belongs to the Special Issue: AI for Wearable Sensing--Smartphone / Smartwatch User Identification / Authentication)

Computers, Materials & Continua 2021, 69(3), 3429-3444. https://doi.org/10.32604/cmc.2021.017710

Received 08 February 2021; Accepted 29 April 2021; Issue published 24 August 2021

Abstract

The scheduling process that aims to assign tasks to members is a difficult job in project management. It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process. This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically. The generated schedule directs the project to be completed with the shortest critical path, at the minimum cost, while maintaining its quality. There are several real-world business constraints related to human resources, the similarity of the tasks added to the optimization model, and the literature’s traditional rules. To support the decision-maker to evaluate different decision strategies, we use compromise programming to transform multi-objective optimization (MOP) into a single-objective problem. We designed a genetic algorithm scheme to solve the transformed problem. The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’ fitness function. The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives. These are achieved through a combination of non-preference and preference approaches. The experimental results show that the proposed method worked well on the tested dataset.

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