Assigning Suppliers to Meet a Deadline

Most real-world project have a deadline and consist of completing tasks. In our setting, each task needs to be executed by a single supplier, chosen from a subset of suppliers that have the required proficiency to handle that task. The suppliers differ in their execution times, which are stochastically taken from known distributions. The Supplier Assignment for Meeting a Deadline (SAMD) problem is the problem of assigning a supplier to each task in a manner that maximizes the chance to meet the overall project deadline. We propose an A*-based approach, along with an efficient admissible heuristic function, that guarantees an optimal solution for this problem. Experimentally, we compare our A*-based approach to an exhaustive brute-force approach and several heuristic methods. The results show that our A*-based approach compares favorably with the heuristic methods, and is orders of magnitude faster than the exhaustive alternative

Optimal Approximation of Random Variables for Estimating the Probability of Meeting a Plan Deadline

In planning algorithms and in other domains, there is often a need to run long computations that involve summations, maximizations and other operations on random variables, and to store intermediate results. In this paper, as a main motivating example, we elaborate on the case of estimating probabilities of meeting deadlines in hierarchical plans. A source of computational complexity, often neglected in the analysis of such algorithms, is that the support of the variables needed as intermediate results may grow exponentially along the computation. Therefore, to avoid exponential memory and time complexities, we need to trim these variables. This is similar, in a sense, to rounding intermediate results in numerical computations. Of course, to maintain the quality of algorithms, the trimming procedure should be efficient and it must maintain accuracy as much as possible. In this paper, we propose an optimal trimming algorithm with polynomial time and memory complexities for the purpose of estimating probabilities of deadlines in plans. More specifically, we show that our algorithm, given the needed size of the representation of the variable, provides the best possible approximation, where approximation accuracy is considered with a measure that fits the goal of estimating deadline meeting probabilities

Pico-Sat to Ground Control: Optimizing Download Link via Laser Communication

Consider a constellation of over a hundred low Earth orbit satellites that aim to capture every point on Earth at least once a day. Clearly, there is a need to download from each satellite a large set of high-quality images on a daily basis. In this paper, we present a laser communication (lasercom) framework that stands as an alternative solution to existing radio-frequency means of satellite communication. By using lasercom, the suggested solution requires no frequency licensing and therefore allows such satellites to communicate with any optical ground station on Earth. Naturally, in order to allow laser communication from a low Earth orbit satellite to a ground station, accurate aiming and tracking are required. This paper presents a free-space optical communication system designed for a set of ground stations and nano-satellites. A related scheduling model is presented, for optimizing the communication between a ground station and a set of lasercom satellites. Finally, we report on SATLLA-2B, the first 300 g pico-satellite with basic free-space optics capabilities, that was launched on January 2022. We conjecture that the true potential of the presented network can be obtained by using a swarm of few hundreds of such lasercom pico-satellites, which can serve as a global communication infrastructure using existing telescope-based observatories as ground stations