Multi-mode resource-constrained project scheduling problem along with material ordering under time-of-use electricity tariffs and carbon taxes

The research highlights the potential of integrating energy management and the Multi-Mode Resource-Constrained Project Scheduling Problem, which includes material ordering. It addresses the pressing concerns of escalating electricity consumption and greenhouse gas emissions by considering renewable energy resources, time-of-use (TOU) electricity tariffs, and carbon taxes. In this regard, a mixed integer linear programming model is developed. To solve the small-sized problem instances, epsilon-constraint method is used. However, since the problem under consideration is NP-hard and exact methods fail to provide solutions for large-sized instances within a reasonable timeframe, two multi-objective meta-heuristic algorithms, Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO), are used as solution methods. Comparing results obtained by NSGA-II and MOPSO demonstrates the superiority of NSGA-II in the majority of performance metrics, regardless of the problem size. Through comprehensive sensitivity analysis of key model parameters, including TOU electricity tariffs and carbon tax rates, the research determines the optimal project duration and cost. Sensitivity analyses offer insights for sustainable project planning, highlighting the role of renewables in electricity consumption. Minimizing carbon taxes and proactive resource management underscore environmental responsibility, while TOU tariffs enhance sustainable energy practices. Overall, strategically integrating renewables in certain scenarios optimizes project execution, aligning with sustainability objectives for efficient and successful outcomes.