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Improved genetic algorithm for magnetic material two-stage multi-product production scheduling: A case study

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5 Author(s)
Yefeng Liu ; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang,110004, China ; Tianyou Chai ; S. Joe Qin ; Quanke Pan
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In this paper an improved genetic algorithm (GA) was present for magnetic material two-stage, multi-product, production scheduling problem (TMPS) with parallel machines. TMPS was changed into molding-stage's multi-product production scheduling problem (MMPS) and the scheduling model was set up for the first time. A set of random solutions were explored first, better feasible solutions were obtained by GA. To shorten the solving time and improve solution accuracy, an improved GA was proposed. We improved GA's crossover operator, adopted heuristic greedy 3PM crossover operator (HG3PMCO) to reduce GA's computational time. Through contrast of computational results of MILP, general GA and improved GA, the improved GA has demonstrated its effectiveness and reliability in solving the molding sintering production scheduling problems and the MILP model set up for the first time is reasonable. At last, the improved genetic algorithm was used in molding stage and sintering stage TMPS of magnetic material.

Published in:

2012 IEEE 51st IEEE Conference on Decision and Control (CDC)

Date of Conference:

10-13 Dec. 2012