This paper addresses the parallel flowshop scheduling problem with stochastic processing times, where a product composed of several components has to be finished at a particular moment. These components are processed in independent parallel factories, and each factory can be modeled as a permutation flowshop. The processing time of each operation at each factory is a random variable following a given probability distribution. The aim is to find the robust starting time of the operations at each factory in a way that all the components of the product are completed on a given deadline with a user-defined probability. A simheuristic algorithm is proposed in order to minimize each of the following key performance indicators: (i) the makespan in the deterministic version; and (ii) the expected makespan or a makespan percentile in the stochastic version. A set of computational experiments are carried out to illustrate the performance of the proposed methodology by comparing the outputs under different levels of stochasticity.

Flowshop production is adopted as the major type of production of reinforced precast concrete components and it has higher requirements on shop floor schedules than other types, especially that from rescheduling. However, up to now, very few approach for the optimization of the shop floor rescheduling has been proposed in spite of its vital importance. This research proposes an approach for optimizing shop floor rescheduling of multiple production lines for flowshop production of reinforced precast concrete components. The approach comprehensively utilizes the over-assigned time, which is the difference value between the assigned production time and the estimated one of a production step for a precast component to deal with production emergencies. Meanwhile, it keeps the adjustment of schedules at minimum to avoid massive material re-dispatch. First of all, the optimization objectives and constraints of optimized shop floor rescheduling of multiple production lines for flowshop precast production are analyzed and a mathematic model is thus formulated. Then, the solver of the model is established by using genetic algorithm. Finally, the approach is validated by case studies. It is concluded that the approach contributes to the effective and efficient optimized rescheduling of multiple production lines for flowshop precast production.

We study scheduling on multiple two-stage flowshops in which each job has to pass through an R-operation and a T-operation. Motivated by the current research in data centers, we consider two restricted versions of the problem in which the jobs are inclined: one restricts that for each job, the R-operation consumes no less time than the T-operation, while the other assumes that the T-operation consumes no less time than the R-operation for each job. For the first case, we present an online 2-competitive algorithm and an offline 11/6-approximation algorithm. For the second case, we give an online 5/2-competitive algorithm, and prove, for the offline setting, that the problem can be reduced to the problem in the first case.

We consider crane scheduling at rail transshipment yards, in which gantry cranes move containers between trains, trucks and a storage area. The single-crane scheduling problem arises at crane transshipment terminals and as a subproblem of the multiple-crane scheduling problem. We consider a makespan objective function, which is equivalent to minimizing the train dwell time in the yard, and introduce time windows for container moves, for example, as a customer service promise. Our proposed decomposition algorithm with integrated dynamic branch-and-cut or dynamic programming solves practically relevant instances within short time limits.

The number of tasks that a satellite may execute in orbit is strongly related to the amount of energy its Electrical Power System (EPS) is able to harvest and to store. The manner the stored energy is distributed within the satellite has also a great impact on the CubeSat’s overall efficiency. Most CubeSat’s EPS do not prioritize energy constraints in their formulation. Unlike that, this work proposes an innovative energy-driven scheduling algorithm based on energy harvesting maximization policy. The energy harvesting circuit is mathematically modeled and the solar panel I-V curves are presented for different temperature and irradiance levels. Considering the models and simulations, the scheduling algorithm is designed to keep solar panels working close to their maximum power point by triggering tasks in the appropriate form. Tasks execution affects battery voltage, which is coupled to the solar panels through a protection circuit. A software based Perturb and Observe strategy allows defining the tasks to be triggered. The scheduling algorithm is tested in FloripaSat, which is an 1U CubeSat. A test apparatus is proposed to emulate solar irradiance variation, considering the satellite movement around the Earth. Tests have been conducted to show that the scheduling algorithm improves the CubeSat energy harvesting capability by 4.48% in a three orbit experiment and up to 8.46% in a single orbit cycle in comparison with the CubeSat operating without the scheduling algorithm.

We consider the classical scheduling problem on parallel identical machines to minimize the makespan. There is a long history of study on this problem, focusing on exact and approximation algorithms. It is thus natural to consider whether these algorithms can be further improved in terms of running times. We provide strong lower bounds on the running times of exact and approximation schemes for the classical scheduling problem based on Exponential Time Hypothesis, showing that the best known approximation and exact algorithms are almost the best possible in terms of the running time.