Application of an evolutionary algorithm-based ensemble model to job-shop scheduling
Journal of Intelligent Manufacturing
Saeid (2017) Application of an evolutionary algorithm-based ensemble model to job-shop scheduling. Journal of Intelligent Manufacturing. ISSN 0956-5515 (In Press) Published by: Springer URL: http://dx. Abstract In this paper, a novel evolutionary algorithm is applied to tackle job-shop scheduling tasks in manufacturing environments. Specifically, a modified micro genetic algorithm (MmGA) is used as the building block to formulate an ensemble model to undertake multi-objective optimisation
... ms in job-shop scheduling. The MmGA ensemble is able to approximate the optimal solution under the Pareto optimality principle. To evaluate the effectiveness of the MmGA ensemble, a case study based on real requirements is conducted. The results positively indicate the effectiveness of the MmGA ensemble in undertaking job-shop scheduling problems. Keywords Multi-objective optimisation, evolutionary algorithm, ensemble models, job-shop scheduling 2 Heuristic-based Scheduling Problems Methods for solving scheduling problems can be broadly classified into two categories  . The first is enumeration-based methods that utilise dynamic programming or branch-and-bound techniques to find the optimal solution. The second is heuristic-based search methods that find near optimal solutions. The enumeration-based methods eliminate candidate solutions by employing restrictive criteria. However, it is well-known that the enumeration-based methods are time-consuming, and are not efficient for large-scale problems  ,  . State space explosion as a result of increasing the number of state variables is the key limitation of dynamic programming techniques, owing to increasing number of state variables  . On the other hand, the long execution time owing to the number of variables involved in branching as well as the strategies used for bounding is the key limitation of branch-and-bound techniques  . In regards to heuristic-based search methods, the candidate solutions are produced by meta-heuristics searches, e.g. single-objective optimisation using constructive heuristics  . Another example is a hybrid genetic programming and hyper-heuristic method proposed in  . The method is used in a single-objective jobshop scheduling problem for evolving dispatch rules, whereby the results show the effectiveness of the genetic programming-based method over hybrid genetic algorithm (GA) methods.