Energy-efficient project scheduling with supplier selection in manufacturing projects

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.

Automated summary

This study proposes an energy-efficient resource constrained project scheduling plan with a supplier selection strategy, aiming to promote green manufacturing project scheduling. The plan is designed as a bi-objective problem with the goals of minimizing project completion time and green project indicators. A genetic algorithm-based memetic algorithm is proposed to solve this problem. Experimental results demonstrate that this algorithm outperforms other approaches in terms of solution quality and computational efficiency.