Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling Yahia Zakaria, Ahmed BahaaElDin and Mayada Hadhoud Computer Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt Keywords: Feature Selection, Flexible Job Shop Scheduling, Dynamic Scheduling, Genetic Programming. Abstract: Dynamic flexible job shop scheduling is an optimization problem concerned with job assignment in dynamic production environments where future job arrivals are unknown. Job scheduling systems employ a pair of rules: a routing rule which assigns a machine to process an operation and a sequencing rule which determines the order of operation processing. Since hand-crafted rules can be time and effort-consuming, many papers employ genetic programming to generate optimum rule trees from a set of terminals and operators. Since the terminal set can be large, the search space can be huge and inefficient to explore. Feature selection techniques can reduce the terminal set size without discarding important information and they have shown to be effective for improving rule generation for dynamic job shop scheduling. In this paper, we extend a niching-based feature selection technique to fit the requirements of dynamic flexible job shop scheduling. The results show that our method can generate rules that achieves significantly better performance compared to ones generated from the full feature set. 1 INTRODUCTION Job Shop Scheduling (JSS) (Brucker and Schlie, 1990) is a popular optimization problem with many practical applications in multiple fields such as cloud computing and manufacturing. JSS aims to assign job operations to machines where each job contains a se- quence of operations that can only be processed in a certain order and each operation can only be pro- cessed on a certain machine. Since static job shop as- sumes that all jobs are known before the scheduling starts, it is inapplicable to many realistic use-cases where job arrival times are unknown. Dynamic Job Shop Scheduling (DJSS) is an extension of JSS where jobs can arrive at any point in time and the sched- uler has no information about future jobs. JSS also assumes that each operation is processable on only one machine. This assumption is not always true es- pecially in large production environments. Flexible Job Shop Scheduling (FJSS) relaxes the aforemen- tioned assumption by allowing each operation to be processed on any member from a subset of the ma- chines. In this paper, we are only concerned with Dy- namic Flexible Job Shop Scheduling (DFJSS). Due to the scalability and speed requirements in practical DJSS applications, Dispatching Rules (DR) are popular due to their simplicity and scalability (Blackstone et al., 1982). Dispatching rules are heuristics that compute a priority for each operation in the machine queue. In DFJSS, Scheduling is operated by a pair of rules (Dauzere-Peres and Paulli, 1997): A Routing Rule (RR) that routes each operation to a ma- chine queue and A Sequencing Rule (SR) that assigns a priority to each operation in the queue. Scheduling rules can be handcrafted by experts but different en- vironments require customized rules so manual rule design tend to be time and effort-consuming. Genetic Programming (GP) (Koza, 1992) has been adopted by many recent works for automated rule generation. GP encodes the rules as trees where leaves denote features holding information about the decision situation, and inner nodes denote operators connected via edges to its operands. To search for op- timum rules, GP starts from a random tree population and applies a sequence of selection, crossover and mutation operators to generate offspring. However, the probability of finding a good rule degrades with the expansion of the search space. Since adding more features will exponentially expand the search space, irrelevant features should be discarded. In DJSS, fea- ture selection (Mei et al., 2017) was applied to opt out features deemed irrelevant to the rule fitness. To our knowledge, the feature selection method proposed by (Mei et al., 2017) is yet to be applied for DFJSS. Zakaria, Y., BahaaElDin, A. and Hadhoud, M. Applying Feature Selection to Rule Evolution for Dynamic Flexible Job Shop Scheduling. DOI: 10.5220/0007957801390146 In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019), pages 139-146 ISBN: 978-989-758-384-1 Copyright c  2019 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved 139