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In a globally integrated economy, production is frequently distributed across multiple factories or production lines, a challenge that is addressed in the Distributed Flow Shop Scheduling Problem (DFSP). The DFSP involves scheduling a set of jobs across multiple distributed flow shop factories, each equipped with identical machine setups. Our study introduces two key extensions to the DFSP: (1) the option to increase machine speed at additional cost, and (2) inter-factory transportation of semi-finished products, incurring transportation costs and delays at the benefit of higher factory utilization. While most existing research focuses on makespan, we consider total tardiness to generate customer-oriented schedules. Our objective is to minimize both total tardiness and cost of transportation and speed increase. To efficiently solve this multi-objective problem, we introduce an iterated greedy algorithm that is tailored to the problem characteristics. Computational experiments demonstrate that our method outperforms conventional mathematical and constraint programming approaches. Our findings quantify the benefits of transportation and machine speed adjustments. We find that the combination of both can lead to synergistic improvements. Further, our results offer valuable insights into the trade-offs between total tardiness and costs for decision makers in distributed manufacturing.