DocumentCode :
2709536
Title :
A starting-time-based approach to production scheduling with Particle Swarm Optimization
Author :
Grobler, Jacomine ; Engelbrecht, Andries P. ; Joubert, Johan W. ; Kok, Schalk
Author_Institution :
Ind. Eng., Pretoria Univ.
fYear :
2007
fDate :
1-5 April 2007
Firstpage :
121
Lastpage :
128
Abstract :
This paper provides a generic formulation for the complex scheduling problems of Optimatix, a South African company specializing in supply chain optimization. To address the complex requirements of the proposed problem, various additional constraints were added to the classical job shop scheduling problem. These include production downtime, scheduled maintenance, machine breakdowns, sequence-dependent set-up times, release dates and multiple predecessors per job. Differentiation between primary resources (machines) and auxiliary resources (labour, tools and jigs) were also achieved. Furthermore, this paper applies particle swarm optimization (PSO), a stochastic population based optimization technique originating from the study of social behavior of birds and fish, to the proposed problem. Apart from the significance of the paper in that the proposed problem has not been addressed before, the benefit of an improved production schedule can be generalized to include cost reduction, customer satisfaction, improved profitability and overall competitive advantage
Keywords :
job shop scheduling; particle swarm optimisation; Optimatix; complex scheduling; cost reduction; customer satisfaction; job shop scheduling; machine breakdown; particle swarm optimization; production downtime; production scheduling; profitability; scheduled maintenance; sequence-dependent set-up times; starting-time-based approach; stochastic population based optimization; Birds; Electric breakdown; Fixtures; Job production systems; Job shop scheduling; Marine animals; Particle production; Particle swarm optimization; Stochastic processes; Supply chains;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Scheduling, 2007. SCIS '07. IEEE Symposium on
Conference_Location :
Honolulu, HI
Print_ISBN :
1-4244-0704-4
Type :
conf
DOI :
10.1109/SCIS.2007.367679
Filename :
4218606
Link To Document :
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