Solving a new multi-objective resource constrained project scheduling problem by SAICA and compare it with DE method

Nowadays the Resource Constrained Project Scheduling Problem (RCPSP) has triggered a substantially significant issue among scheduling problems. The purpose of RCPSP is minimizing the duration of the projects due to both limited available resources and precedence constraints. Indeed, it attempts to consume the total resources by finding the best duration for each activity. This paper proposes a new multi-objective mathematical model for multi-mode RCPSP with interruption to minimize the completion time of the project, maximize the Net Present Value (NPV) of the project, and minimize the allocating workforce’s costs to perform required skills of all activities. To solve the proposed model, an efficient method based on Me measure is used to cope with the uncertainties, and TH method is utilized to convert the multi-objective method into the single one. Furthermore, this paper presents a novel hybrid meta-heuristic algorithm based on Imperialist Competitive Algorithms (ICA) named Self-Adaptive Imperialist Competitive Algorithm (SAICA) to solve the mathematical model which has never been used to solve this type of problems before. Also, to evaluate the proposed method, its performance is investigated against some meta-heuristic algorithms: Differential Evolution (DE) and Imperialist Competitive Algorithm (ICA). Then, a numerical example, two case studies and a real case study have been carried out to embody both validity and efficiency of the presented approach. The obtained results embody that the proposed SAICA is more effective and practical in comparison with DE, ICA, and BCO in decreasing the project duration and also, the considerable effect on solutions confirms the quality of the proposed method.