Energy-efficient project scheduling with supplier selection in manufacturing projects

• Published in: Expert systems with applications Vol. 193; p. 116446
• Main Authors: Rahman, Humyun Fuad, Chakrabortty, Ripon K., Elsawah, Sondoss, Ryan, Michael J.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.05.2022; Elsevier BV
• Subjects: Clean energy; Completion time; Component and supplier management; Energy consumption; Evolutionary algorithms; Genetic algorithms; Green; Manufacturing; Memetic algorithm; Multi-objective; Noise pollution; Occupational safety; Project management; Project scheduling in manufacturing; Resource scheduling; Scheduling; Sorting algorithms; Supplier; Project scheduling in manufacturing; Memetic algorithm; Supplier; Multi-objective; Green
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.116446

•Studied a green RCPSP embedded with supplier selection plan for manufacturing.•Developed a model with the objectives of minimization of makespan and green project indicators.•Proposed a GA-based MA algorithm to solve the problem.•Compare the performance of MA against NSGA-II and EP algorithms. In make-to-order or engineer-to-order systems, the overarching process of producing complex and highly customized products along with managing multiple stakeholders (including suppliers) can be considered to be a project scheduling problem. Yet, despite the need for an advanced project scheduling plan for manufacturing, there is little research in the literature considering both project scheduling and supplier selection in a manufacturing context. Due to the scarcity of resources in manufacturing, this project scheduling problem resembles the well-known resource constrained project scheduling problem (RCPSP). Additionally, with the increasing awareness of the environment and the need to minimize energy consumption and noise pollution, and continuing concern with worker safety, there is a need for innovative methods to improve green factors (energy, noise, and safety) in manufacturing projects. This paper, therefore, proposes for manufacturing projects an energy-efficient resource constrained project scheduling plan embedded with a supplier selection strategy called the green RCPSP for manufacturing (or GRCPSPM). This proposed GRCPSPM is designed as a bi-objective problem with the conjoint objectives of minimization of project completion time and green project indicators (GPI). To solve that bi-objective problem, a genetic algorithm-based memetic algorithm (MA) is proposed and experimental results show that the proposed MA outperforms the well-known non-dominated sorting genetic algorithm-II (NSGA-II) approach and evolutionary programming (EP) algorithm for a number of self-generated project scheduling instances, in terms of both solution quality and computational efficiency. The obvious outcome of this study is to support the selection of an appropriate supplier based on resource processing speeds and resource GPI, thereby ensuring green manufacturing project scheduling with minimum completion time and minimum energy usage.