Advanced Tree-Seed Algorithm for Large Sized JSP Problems

Globalizing economies force manufacturing companies to develop themselves and take new measures.Correct planning of the production process is vital in order to increase the competitiveness of companies.For this reason, the Job shop Scheduling Problem (JSP) has a great role in the planning of production in companies.In JSPs, the determined jobs in the manufacturing companies must be run in the correct order on existing and suitable machines.Planning to complete the work in the company in the shortest possible time on the smachines and in the order, is a combinatorial difficult optimization problem.Both exact methods and meta-heuristic approaches are used in solving JSPs, which is an NP-hard optimization problem.Depending on the number of jobs and machines in the companies, the size of this optimization problem changes.In JSPs where the number of jobs and machines is high, precise methods may be insufficient to produce solutions in operational time.Therefore, meta-heuristic algorithms are frequently used in solving JSPs.In this work, the exploration and exploitation capabilities in the Tree Seed Algorithm (TSA) are enhanced with the swap, symmetry, and shift mutation operators.The proposed new TSA (Advanced TSA-ATSA) algorithm is compared on large-sized JSPs with meta-heuristic algorithms which are well known in the literature.The proposed ATSA has shown promising performance in experimental studies.