Job Shop Scheduling in Discrete Manufacturing Based on Improved Hybrid Lion Swarm Optimization

Abstract

Job shop scheduling problem (JSSP) is a NP-hard problem, which is widely used in manufacturing industry. In this paper, we proposed an improved hybrid Lion Swarm Optimization (IHLSO) algorithm for the JSSP based on the actual production requirements. The proposed IHLSO included two improved algorithms, which were respectively hybrid Lion Swarm Optimization (HLSO) algorithm and HLSO with Solis&Wets (SW) local search algorithm (SW-HLSO). IHLSO improved the global searching ability of basic Lion Swarm Optimization (LSO) algorithm by combing Particle Swarm Optimization (PSO) algorithm and improved the local searching ability by applying SW algorithm. Tests on the benchmark instances of the JSSP showed excellent performance of the improved LSO. In the actual production scene of machine tool manufacturing enterprises, the algorithm proposed in this paper is more suitable for the demand of factory scheduling than other methods. In addition, in order to facilitate the application of the algorithm in actual production and help the employees to arrange production more conveniently, we had designed a Graph User Interface (GUI) to realize intelligent production scheduling in digital workshop.