Solving Job Shop Scheduling Problem Using Cellular Learning Automata

Abstract

Cellular Learning Automata (CLA) is one of the newest optimization methods for solving NP-hard problems. The Job Shop Scheduling Problem (JSSP) is one of these problems. This paper, proposes a new approach for solving the JSSP using CLA with two kinds of actions' set. By generating actions based on received responses from the problem environment, appropriate position for operations of jobs is chosen in execution sequence. The goal in the problem is to minimize maximum completion time of jobs, known as makespan. We present our approach in an algorithmic form after problem definition and a brief description of cellular learning automata. The algorithm is tested on several instances of verity of benchmarks and the experimental results show that it generates nearly optimal solutions, compared with other approaches.