Stepping into Industry 4.0-based optimization model: a hybrid of the NSGA-III and MOAOA

What is it about?

In this research, we presented a new bi-objective mathematical model based on Industry 4.0 to schedule the received customer orders, which not only minimizes the total earliness and tardiness of orders but also decreases the probability of machine failure in smart manufacturing. Then, we considered some crucial policies of Industry 4.0, such as preventive self-maintenance, self-scheduling, and real-time decision-making. Moreover, a novel approach proposes that a machine self-optimizes both production scheduling and following preventive self-maintenance policy in real-time situations. To solve the problem, a new multi-objective meta-heuristics, namely multi-objective Archimedes optimization algorithm (MOAOA) was proposed and compared with two well-known meta-heuristics, namely non-dominated sorting genetic algorithm-II (NSGA-II) and multi-objective simulated annealing (MOSA). Also, different metrics were computed to measure the performance of the algorithms. Finally, a new approach based on crowding-distance-quality was presented to find the best solution from the frontier.

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