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 () and Zhuming Bi ()
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Siqing Shan: Beihang University
Zhongjun Hu: Beihang University
Zhilian Liu: Beihang University
Jihong Shi: Beihang University
Li Wang: Beihang University
Zhuming Bi: Indiana University Purdue University Fort Wayne

Information Technology and Management, 2017, vol. 18, issue 1, No 3, 53 pages

Abstract: Abstract 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.

Keywords: Demand-driven; NP-hard problems; Genetic algorithm; Resource-constrained; Project scheduling; Resource-constrained project scheduling problem (RCPSP); Aircraft assembly (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10799-015-0223-7

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