Open AccessMulti-Objective Master Production Schedule for Balanced Production of Manufacturers
Author(s) -
C. Wang,
Yang Bing,
H. Q. Wang
Publication year - 2020
Publication title -
international journal of simulation modelling
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.603
H-Index - 24
eISSN - 1996-8566
pISSN - 1726-4529
DOI - 10.2507/ijsimm19-4-co17
Subject(s) - crossover , mathematical optimization , topsis , genetic algorithm , schedule , production (economics) , ideal solution , computer science , fitness function , transformation (genetics) , set (abstract data type) , fuzzy logic , mathematics , operations research , artificial intelligence , biochemistry , chemistry , physics , thermodynamics , gene , economics , macroeconomics , programming language , operating system
Focusing on the balanced use of production capacity in the formulation of master production schedule (MPS), this paper sets up a single-product, multi-stage, multi-objective MPS model based on balanced production. Whereas the model aims to achieve multiple objectives through nonlinear integer programming, a genetic algorithm based on automatic transformation (AT-GA) was designed to solve the model. Specifically, the chromosomes were encoded as integers to satisfy the model constraints; the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was adopted to handle the four nonlinear objectives of the model, thereby obtaining the fitness function; the fuzzy logic control (FLC) was introduced to automatically adjust the crossover and mutation parameters, and balance the global and local search abilities of the GA, enhancing the computing power of the algorithm. The experimental results show that the AT-GA can effectively solve the multi-objective MPS optimization problem under balanced production.