Optimization of distributed job shop scheduling problems based on artificial perception algorithm

DJSSP is an extension of the traditional Job Shop Scheduling Problem (JSSP), which is Np-Hard and is a more complex optimization problem. JSSP refers to a problem where non-identical workpieces have variable processing times on different machines and must be processed in a certain order. Its main objective is to arrange the workpieces on the basis of minimizing the total processing time using machines and time parameters. DJSSP, on the other hand, is a more complex optimization problem involving multiple facilities and the coordination of the machines in these locations with the jobs and the timetable to achieve an objective using the facility, machine, job and time parameters. JSSP and DJSSP problems are classified as NP-hard problems and cannot be solved in a reasonable time using exact methods. To solve NP-hard problems, meta-heuristics are used that provide acceptable solutions in a reasonable time. In this thesis, 10 different well-known metaheuristic algorithms (Particle Swarm Optimization-PSO, Artificial Bee Colony-ABC, Grey Wolf Optimizer – GWO, Red Fox Optimizer – RFO, Jaya Algorithm-JAYA, Artificial Algae Algorithm-AAA, Tree-Seed Algorithm-TSA, Lévy Flight Distribution-LFD, Differential Search Algorithm - DSA, Whale Optimization Algorithm- WOA) from the literature are used to solve DJSSP problems. In addition, 3 different coding schemes (Random Key Encoding Scheme-RK, Smallest Position Value Encoding Scheme-SPV, Ranked-Over Value Encoding Scheme-ROV) used for discretization are discussed in order to run metaheuristic algorithms operating in the continuous search space for the DJSSP discrete problem. Each coding scheme is applied on 48 DJSSP benchmark problems with 10 metaheuristic algorithms. According to the results obtained, the AAA algorithm obtained better quality solutions than the other metaheuristic algorithms.