Chaos-based improved immune algorithm (CBIIA) for resource-constrained project scheduling problems

Authors:
Shanshan Wu;Hung-Da Wan;Sanjay Kumar Shukla;Beizhi Li
Affiliations:
Center for Advanced Manufacturing, Department of Mechanical Engineering, Donghua University, Shanghai, China and Centre for Advanced Manufacturing and Lean Systems, Department of Mechanical Engine ...;Centre for Advanced Manufacturing and Lean Systems, Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA;Centre for Advanced Manufacturing and Lean Systems, Department of Mechanical Engineering, University of Texas at San Antonio, One UTSA Circle, San Antonio, TX 78249, USA;Center for Advanced Manufacturing, Department of Mechanical Engineering, Donghua University, Shanghai, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

This paper introduces a novel meta-heuristic, the chaos-based improved immune algorithm (CBIIA), for solving resource-constrained project scheduling problems (RCPSP). In RCPSP the activities of a project have to be scheduled with the objective of minimizing total makespan subject to both temporal and resource constraints. The proposed CBIIA is based on the traits of an artificial immune system, chaotic generator and parallel mutation. CBIIA is different from the traditional immune algorithm in its initialization and hypermutation mechanism. Initialization in CBIIA is done by using chaotic generator (Logistic, Tent, and Sinusoidal) instead of conventional random number generator (RNG). The hypermutation is performed by parallel mutation (PM) operator rather than point mutation. Parallel mutation comprises two mutation strategies viz. Gaussian and Cauchy. Gaussian strategy is utilized for small step mutation and Cauchy strategy is for large step mutation. In order to demonstrate the efficacy of the proposed algorithm, Patterson's test suites are worked out. This study aims at developing an alternative and more efficient optimization methodology and opening the application of variants of artificial immune system for solving the RCPSP.