Enabling Multi-Robot Cooperative Additive Manufacturing: Centralized vs. Decentralized Approaches

This paper presents a decentralized approach based on a simple set of rules to carry out multi-robot cooperative 3D printing. Cooperative 3D printing is a novel approach to 3D printing that uses multiple mobile 3D printing robots to print a large part by dividing and assigning the part to multiple robots in parallel using the concept of chunk-based printing. The results obtained using the decentralized approach are then compared with those obtained from the centralized approach. Two case studies were performed to evaluate the performance of both approaches using makespan as the evaluation criterion. The first case is a small-scale problem with four printing robots and 20 chunks, whereas the second case study is a large-scale problem with ten printing robots and 200 chunks. The result shows that the centralized approach provides a better solution compared to the decentralized approach in both cases in terms of makespan. However, the gap between the solutions seems to shrink with the scale of the problem. While further study is required to verify this conclusion, the decrease in this gap indicates that the decentralized approach might compare favorably over the centralized approach for a large-scale problem in manufacturing using multiple mobile 3D printing robots. Additionally, the runtime for the large-scale problem (Case II) increases by 27-fold compared to the small-scale problem (Case I) for the centralized approach, whereas it only increased by less than 2-fold for the decentralized approach.

Improved Crop Productivity Through Optimized Planting Schedules

Problem definition: We study the optimal seeding policy under rainfall uncertainty in rain-fed agriculture and explore its advantage over commonly used heuristics in practice. Academic/practical relevance: Our work is in the area of agriculture operations, and we focus on the improvement of farmer’s expected total profit by optimizing planting schedules. Methodology: We model a farmer’s planting problem under limited planting capacity in a finite-horizon stochastic dynamic program. Results: We show that the optimal planting policy is a time-dependent, threshold-type policy, and the optimal threshold is dependent on the soil water content and planting capacity. In our computational study, we use the well-known Decision Support System for Agrotechnology Transfer simulator used in agriculture to obtain the expected yield when planting in any given period. Utilizing field weather data from Southern Africa, in a real-size, large-scale problem, we demonstrate a significant relative profit advantage of the optimal planting schedule over commonly used heuristics in practice. We show that the relative advantage of the optimal policy increases as the climate condition becomes more severe for planting. We also develop a heuristic based on the secretary problem and demonstrate the increased efficacy of the secretary heuristic. Managerial implications: We show that the farmer should plant down to the optimal threshold of seed amount. However, in practice, farmers start to plant each year after observing enough cumulative rainfall. Utilizing field weather data, in a real-size, large-scale problem, we show significant improvement of the expected total profit if the farmer could adopt the optimal policy.