Victoria University

Many-Objective Genetic Programming for Job-Shop Scheduling

ResearchArchive/Manakin Repository

Show simple item record

dc.contributor.advisor Chen, Aaron
dc.contributor.advisor Mei, Yi
dc.contributor.advisor Al-Sahaf, Harith
dc.contributor.advisor Zhang, Mengjie Masood, Atiya 2020-12-13T20:55:33Z 2020-12-13T20:55:33Z 2020 2020
dc.description.abstract The Job Shop Scheduling (JSS) problem is considered to be a challenging one due to practical requirements such as multiple objectives and the complexity of production flows. JSS has received great attention because of its broad applicability in real-world situations. One of the prominent solutions approaches to handling JSS problems is to design effective dispatching rules. Dispatching rules are investigated broadly in both academic and industrial environments because they are easy to implement (by computers and shop floor operators) with a low computational cost. However, the manual development of dispatching rules is time-consuming and requires expert knowledge of the scheduling environment. The hyper-heuristic approach that uses genetic programming (GP) to solve JSS problems is known as GP-based hyper-heuristic (GP-HH). GP-HH is a very useful approach for discovering dispatching rules automatically. Although it is technically simple to consider only a single objective optimization for JSS, it is now widely evidenced in the literature that JSS by nature presents several potentially conflicting objectives, including the maximal flowtime, mean flowtime, and mean tardiness. A few studies in the literature attempt to solve many-objective JSS with more than three objectives, but existing studies have some major limitations. First, many-objective JSS problems have been solved by multi-objective evolutionary algorithms (MOEAs). However, recent studies have suggested that the performance of conventional MOEAs is prone to the scalability challenge and degrades dramatically with many-objective optimization problems (MaOPs). Many-objective JSS using MOEAs inherit the same challenge as MaOPs. Thus, using MOEAs for many-objective JSS problems often fails to select quality dispatching rules. Second, although the reference points method is one of the most prominent and efficient methods for diversity maintenance in many-objective problems, it uses a uniform distribution of reference points which is only appropriate for a regular Pareto-front. However, JSS problems often have irregular Pareto-front and uniformly distributed reference points do not match well with the irregular Pareto-front. It results in many useless points during evolution. These useless points can significantly affect the performance of the reference points-based algorithms. They cannot help to enhance the solution diversity of evolved Pareto-front in many-objective JSS problems. Third, Pareto Local Search (PLS) is a prominent and effective local search method for handling multi-objective JSS optimization problems but the literature does not discover any existing studies which use PLS in GP-HH. To address these limitations, this thesis's overall goal is to develop GP-HH approaches to evolving effective rules to handle many conflicting objectives simultaneously in JSS problems. To achieve the first goal, this thesis proposes the first many-objective GP-HH method for JSS problems to find the Pareto-fronts of nondominated dispatching rules. Decision-makers can utilize this GP-HH method for selecting appropriate rules based on their preference over multiple conflicting objectives. This study combines GP with the fitness evaluation scheme of a many-objective reference points-based approach. The experimental results show that the proposed algorithm significantly outperforms MOEAs such as NSGA-II and SPEA2. To achieve the second goal, this thesis proposes two adaptive reference point approaches (model-free and model-driven). In both approaches, the reference points are generated according to the distribution of the evolved dispatching rules. The model-free reference point adaptation approach is inspired by Particle Swarm Optimization (PSO). The model-driven approach constructs the density model and estimates the density of solutions from each defined sub-location in a whole objective space. Furthermore, the model-driven approach provides smoothness to the model by applying a Gaussian Process model and calculating the area under the mean function. The mean function area helps to find the required number of the reference points in each mean function. The experimental results demonstrate that both adaptive approaches are significantly better than several state-of-the-art MOEAs. To achieve the third goal, the thesis proposes the first algorithm that combines GP as a global search with PLS as a local search in many-objective JSS. The proposed algorithm introduces an effective fitness-based selection strategy for selecting initial individuals for neighborhood exploration. It defines the GP's proper neighborhood structure and a new selection mechanism for selecting the effective dispatching rules during the local search. The experimental results on the JSS benchmark problem show that the newly proposed algorithm can significantly outperform its baseline algorithm (GP-NSGA-III). en_NZ
dc.language.iso en_NZ
dc.language.iso en
dc.publisher Victoria University of Wellington en_NZ
dc.subject Many-objective optimization en_NZ
dc.subject Genetic programming en_NZ
dc.subject Scheduling en_NZ
dc.subject Heuristic en_NZ
dc.title Many-Objective Genetic Programming for Job-Shop Scheduling en_NZ
dc.type text en_NZ
vuwschema.contributor.unit School of Engineering and Computer Science en_NZ
vuwschema.type.vuw Awarded Doctoral Thesis en_NZ Computer Science en_NZ Victoria University of Wellington en_NZ Doctoral en_NZ Doctor of Philosophy en_NZ
dc.rights.license Author Retains Copyright en_NZ 2020-12-11T02:24:07Z
vuwschema.subject.anzsrcfor 080108 Neural, Evolutionary and Fuzzy Computation en_NZ
vuwschema.subject.anzsrcfor 080199 Artificial Intelligence and Image Processing not elsewhere classified en_NZ
vuwschema.subject.anzsrctoa 3 APPLIED RESEARCH en_NZ

Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ResearchArchive

Advanced Search


My Account