A hybrid metaheuristic and simulation approach towards green project scheduling

Abstract

This research tackles the environmental concern of greenhouse gas emissions in the execution of projects, with a focus on multi-site projects where the transportation of resources is a major source of emissions. Despite growing consciousness among consumers and stakeholders about sustainability, the domain of project scheduling has often overlooked the environmental impact. This paper seeks to bridge this oversight by exploring how to reduce greenhouse gas emissions during both project activities and resource transportation. A novel approach is proposed, combining a simulation model with an improved non-dominated sorted genetic algorithm. The simulation model incorporates the stochastic nature of emission rates and costs. This method is further refined with innovative techniques such as magnet-based crossover and mode reassignment. The former is a genetic algorithm operation inspired by magnetic attraction, which allows for a more diverse and effective exploration of solutions by aligning similar ’genes’ from parent solutions. The latter is a strategy for reallocating resources during project execution to optimize efficiency and reduce emissions. The efficacy of the proposed method is validated through testing on 2810 scenarios from established benchmark libraries, 100 additional scenarios adhering to the conventional multi-site problems, and a case study. The Best-Worst Method (BWM) is applied for identifying the best solution. The findings indicate substantial enhancements compared to traditional methods with a 12.7% decrease in project duration, 11.4% in costs, and a remarkable 13.6% reduction in greenhouse gas emissions.

Publication DOI: https://doi.org/10.1007/s10479-024-06291-z
Divisions: College of Business and Social Sciences > Aston Business School > Operations & Information Management
College of Business and Social Sciences > Aston Business School
Aston University (General)
Additional Information: Copyright © Crown, 2024. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/
Uncontrolled Keywords: Green project scheduling,Multi-mode,Multi-site,Resource-constrained,Simulation-based optimization,General Decision Sciences,Management Science and Operations Research
Publication ISSN: 1572-9338
Data Access Statement: The supplementary data files for this study have been published on Mendeley Data [https://data.mendeley.com/datasets/p4vpyg5dnt/1].
Last Modified: 27 Nov 2024 17:42
Date Deposited: 07 Nov 2024 09:53
Full Text Link:
Related URLs: https://link.sp ... 479-024-06291-z (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2024-11-06
Published Online Date: 2024-11-06
Accepted Date: 2024-09-11
Authors: Rabet, Rahmat
Sajadi, Seyed Mojtaba (ORCID Profile 0000-0002-2139-2053)
Tootoonchy, Mahshid

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