A Heuristic-Based Genetic Algorithm for Scheduling of Multiple Projects Subjected to Resource Constraints and Environmental Responsibility Commitments

During the last decades, resource-constrained project scheduling problems have been abundantly presented in extant literature. However, there are still some real-world challenges that have not been adequately considered. These challenges include environmental commitments and constraints related to the procurement of resources (as regards procurement commitment). This calls for the integration of the project planning and forward-reverse supply chain planning systems. To achieve this goal, this paper contributes to the existing literature by presenting a model that incorporates the two issues in the integrated planning system: (1) the procurement commitment objective is met through the just-in-time delivery of non-renewable resources to the project sites while considering the limited supply capacity of suppliers, and (2) the environmental commitment is satisfied by collecting and recycling the waste generated at project sites. A mixed-integer linear formulation of the problem is proposed. Since the model is NP-hard (non-deterministic polynomial time-hard), the paper develops a new heuristic-based genetic algorithm to solve the problem instances. The main parameters of the algorithm are tuned using the Taguchi method. The results show the efficiency of the algorithm in obtaining appropriate solutions in reasonable computational times. The integrated planning model that is proposed in this paper and its novel resolution method would help managers to make more responsive and efficient decisions.