Information Technology and Management

, Volume 18, Issue 1, pp 41–53 | Cite as

An adaptive genetic algorithm for demand-driven and resource-constrained project scheduling in aircraft assembly

  • Siqing Shan
  • Zhongjun Hu
  • Zhilian Liu
  • Jihong Shi
  • Li Wang
  • Zhuming Bi


Scheduling of aircraft assembling activities is proven as a non-deterministic polynomial-time hard problem; which is also known as a typical resource-constrained project scheduling problem (RCPSP). Not saying the scheduling of the complex assemblies of an aircraft, even for a simple product requiring a limited number of assembling operations, it is difficult or even infeasible to obtain the best solution for its RCPSP. To obtain a high quality solution in a short time frame, resource constraints are treated as the objective function of an RCPSP, and an adaptive genetic algorithm (GA) is proposed to solve demand-driven scheduling problems of aircraft assembly. In contrast to other GA-based heuristic algorithms, the proposed algorithm is innovative in sense that: (1) it executes a procedure with two crossovers and three mutations; (2) its fitness function is demand-driven. In the formulation of RCPSP for aircraft assembly, the optimizing criteria are the utilizations of working time, space, and operators. To validate the effectiveness of the proposed algorithm, two encoding approaches have been tested with the real data of demand.


Demand-driven NP-hard problems Genetic algorithm Resource-constrained Project scheduling Resource-constrained project scheduling problem (RCPSP) Aircraft assembly 



This research work would not have been possible without the support of the National Natural Science Foundation of China (71332003, 71471008, 91224007, and 71301011) and Beijing Natural Science Foundation (9142012). This research work has also been supported by the Aircraft Manufacturing Advanced Technology project in the Shanghai Aircraft Manufacturing Company. Besides, we thank the anonymous reviewers for insightful comments that helped us improve the quality of the paper.


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Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Siqing Shan
    • 1
  • Zhongjun Hu
    • 1
  • Zhilian Liu
    • 1
  • Jihong Shi
    • 1
  • Li Wang
    • 1
  • Zhuming Bi
    • 2
  1. 1.Department of Information System, School of Economics and ManagementBeihang UniversityBeijingChina
  2. 2.Department of EngineeringIndiana University Purdue University Fort WayneFort WayneUSA

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