A Cellular Learning Automata (CLA) Approach to Job Shop Scheduling Problem

Abstract

Job shop scheduling problem (JSSP), as one of the NP-Hard combinatorial optimization problems, has attracted the attention of many researchers during the last four decades. The overall purpose regarding this problem is to minimize maximum completion time of jobs, known as makespan. This paper addresses an approach to evolving Cellular Learning Automata (CLA) in order to enable it to solve the JSSP by minimizing the makespan. This approach is applied to several instances of a variety of benchmarks and the experimental results show that it produces nearly optimal solutions, compared with other approaches.