In this paper, a novel mathematical model is developed for the distributed multi-agent network scheduling problem in a dynamic job shop environment with availability constraints and new job arrivals. In a dynamic collaborative-competitive environment, a number of factories with independent ownership are merged to form a multi-agent production network in which each production agent, despite participation, seeks to optimize its own objective function as a primary priority. To minimize the makespan and the total energy consumption, the ε-constraint approach is used. Since the problem is NP-hard, a memetic algorithm based on a non-dominated sorting genetic algorithm-II and local search are proposed. Finally, the proposed algorithm is compared with a hybrid Pareto-based tabu search algorithm (HPTSA). The obtained results show that in large-size instances, our proposed algorithm outperforms the HPTSA.
