A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

• Published in: Sensors (Basel, Switzerland) Vol. 20; no. 18; p. 5440
• Main Authors: Viana, Monique Simplicio, Morandin Junior, Orides, Contreras, Rodrigo Colnago
• Format: Journal Article
• Language: English
• Published: MDPI 22.09.2020; MDPI AG
• Subjects: combinatorial optimization; genetic algorithm; job shop scheduling problem; local search; multi-crossover
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s20185440

It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.