Performance comparison of some evolutionary algorithms on job shop scheduling problems

International Conference on Advances in Materials and Manufacturing Applications 2016

Mishra, S.K.^1 ; Rao, C.S.P.^2

CEO, Bhramos, New Delhi, India^1

Department of Mechanical Engineering, National Institute of Technology, Warangal

Telangana

506004, India^2

关键词: Bacterial foraging optimization;    Evolutionary method;    Invasive weed optimization;    Job shop scheduling problems;    Music based harmony searches;    Particles swarm optimizations;    Performance comparison;    State space search;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/149/1/012041/pdf
DOI  :  10.1088/1757-899X/149/1/012041

来源: IOP

【 摘 要 】

Job Shop Scheduling as a state space search problem belonging to NP-hard category due to its complexity and combinational explosion of states. Several naturally inspire evolutionary methods have been developed to solve Job Shop Scheduling Problems. In this paper the evolutionary methods namely Particles Swarm Optimization, Artificial Intelligence, Invasive Weed Optimization, Bacterial Foraging Optimization, Music Based Harmony Search Algorithms are applied and find tuned to model and solve Job Shop Scheduling Problems. To compare about 250 Bench Mark instances have been used to evaluate the performance of these algorithms. The capabilities of each these algorithms in solving Job Shop Scheduling Problems are outlined.