Heterogeneous Networked Cooperative Scheduling With Anarchic Particle Swarm Optimization

This paper proposes a mathematical model and new solving algorithm for scheduling of a distributed production network with heterogeneous parallel factories distributed in the different geographical places. In this problem, two subproblems must be solved, i.e., 1) assigning jobs to appropriate factory and 2) scheduling jobs on parallel machines in each factory. We also assume that after initial assignment, for better balancing in machines' loading in the different factories, each job can be shifted among factories. After modeling the problem as mixed integer linear programming, with proposing a new method for solution representation, we propose a novel solving algorithm namely anarchic particle swarm optimization to minimize makespan of jobs. This algorithm is inspired by an anarchic society whose members behave anarchically to improve their situations. By such anarchic particles, the algorithm can prevent falling in local optimum traps. The obtained results of mixed integer linear programming solved by CPLEX are compared with the proposed algorithm, a genetic algorithm and a noncooperative local scheduling for small-sized instances. At the end, the effectiveness of anarchic particle swarm optimization, standard particle swarm optimization, and genetic algorithm are examined on the test problems which contained up to 500 jobs.