Open AccessA NEW HYBRID HEURISTIC TECHNIQUE FOR SOLVING JOB-SHOP SCHEDULING PROBLEM
Author(s) -
Cheng-Fa Tsai,
FengCheng Lin
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.3.3.310
Subject(s) - computer science , job shop scheduling , initialization , mathematical optimization , flow shop scheduling , rate monotonic scheduling , dynamic priority scheduling , fair share scheduling , job shop , job scheduler , scheduling (production processes) , two level scheduling , distributed computing , mathematics , schedule , cloud computing , programming language , operating system
This paper proposes a new and efficient hybrid heuristic scheme for solving job-shop scheduling problems (JSP). A new and efficient population initialization and local search concept, based on genetic algorithms, is introduced to search the solution space and to determine the global minimum solution to the JSP problem. Simulated results imply that the proposed novel JSP method (called the PLGA algorithm) outperforms several currently used approaches. This investigation also considers a real-life job-shop scheduling system design, which optimizes the performance of the job-shop scheduling system subject to a required service level. Simulation results demonstrate that the proposed method is very efficient and potentially useful in solving job-shop scheduling problems.