In this paper, resource-constrained project scheduling problem (RCPSP) is discussed with an objective of minimizing the makespan of a project. Due to its universality, it has a variety of applications as in manufacturing, production planning, project management and elsewhere. It is a well known computationally complex problem, thus warrants the application of heuristics techniques or AI based optimization tools to achieve optimal or near optimal solution in real time. In this research, the artificial immune system (AIS) approach is proposed to solve the aforementioned problem. It exploits the beauty of learning and memory acquisition to ensure the convergence with faster rate. During extensive computational experiment, it is found that the performance of the AIS algorithm on a well known data set of resource-constrained project scheduling problem is superior as compared to GA, fuzzy-GA, LFT, GRU, SIO, MINSLK, RSM, RAN, and MJP based approaches.
Project scheduling Precedence constraint Resource constraint Artificial immune system Hypermutation
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Sprecher A (1994) Resource-constrained project scheduling: exact methods for the multi-mode case. Number 409 in lecture notes in economics and mathematical systems. Springer, Berlin Heidelberg NewYorkGoogle Scholar