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.