An outer–inner fuzzy cellular automata algorithm for dynamic uncertainty multi-project scheduling problem

• Published in: Soft computing (Berlin, Germany) Vol. 19; no. 8; pp. 2111 - 2132
• Main Authors: Hu, Wenbin, Wang, Huan, Peng, Chao, Wang, Huang, Liang, Huangle, Du, Bo
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2015; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Cellular automata; Computational Intelligence; Control; Engineering; Lean manufacturing; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Methods; Optimization; Project management; Resource scheduling; Robotics; Scheduling; Uncertainty; Multi-project scheduling; Outer–inner; Uncertainty; Cellular automata; Fuzzy cell
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-014-1395-5

Current research on the resource-constrained multi-project scheduling problem (RCMPSP) mainly focuses on the constrained resources or other single uncertainty factor that cannot satisfy the practical need of enterprise management. This paper addresses the dynamic uncertain multi-project scheduling problem (DUMPSP), which alleviates the above problem of RCMPSP by synthesizing the analysis of uncertainty, sensitivity, and lean management of resources and durations. An outer–inner uncertainty fuzzy cellular automata algorithm (OIUFCA) is proposed to solve DUMPSP. The problem is divided into several parts: (1) fuzzy cell is employed to describe the project cell and process cell in DUMPSP; (2) a fuzzy outer cellular automata (FOCA) model is defined to solve the project scheduling of DUMPSP, and a fuzzy inner cellular automata (FICA) model is constructed to solve the process scheduling; (3) multi-level feedback strategy (MFS) is further proposed to deal with the multi-level dual-stage optimization problem of DUMPSP. Besides, FOCA and FICA model execute co-evolution under the control of MFS. Extensive experiments are carried out to evaluate the performances of OIUFCA with other state-of-the-art algorithms. It is revealed that OIUFCA achieves a better performance in success rate and convergence speed in solving DUMPSP, and OIUFCA has a good solution under different conditions of uncertainty, sensibility, and lean management.