A note on optimization in deteriorating systems using scheduling problems with the aging effect and resource allocation models

AbstractThis paper concerns scheduling problems with the aging effect and additional resource allocation. A measurable result of the aging phenomenon is that the time required to perform a job increases whereas the additional resource allocation allows one to decrease it. As an example of a deteriorating system that can be described and optimized by the application of the models and algorithms considered, we choose the pickling process, where cleaning of metal items decreases the efficiency of the pickling (cleaning) bath (i.e., one containing an active substance), whereas heating it up can improve the efficiency. In particular, we focus on the optimization problems for such systems and model them as single-machine scheduling problems with job processing times dependent on the fatigue of a machine and on the allocation of additional resources. The objectives considered are the minimization of time criteria (the maximum completion time and the maximum lateness) under a given resource consumption as well as the minimization of the resource consumption under given time criteria. The computational complexity of the problems is determined and solution properties are proved. On the basis of these, we construct optimal polynomial time algorithms for some cases of the problems considered

Metaheuristic algorithms for scheduling on parallel machines with variable set-up times

In order to meet growing demands of the market modern manufacturing and service environments must offer an increasingly broad range of services or products as well as ensure their required amount and short lead times. It can be done by the application of universal machines or workers which are able to perform different tasks. On the other hand, human activity environments are often affected by learning. Therefore, in this paper, we analyse related problems, which can be expressed as the makespan minimization scheduling problem on identical parallel machines with variable setup times affected by learning of workers. To provide an efficient schedule, we propose metaheuristic algorithms. Their potential applicability is verified numerically

Parallel Computation Approaches to Optimize Learning Systems

This paper is devoted to the total tardiness minimization scheduling problem, where the efficiency of a processor increases due to its learning. Such problems model real-life settings that occur in the presence of a human learning (industry, manufacturing, management) and in some computer systems. However, the increasing growth of significant achievements in the field of artificial intelligence and machine learning is a premise that the human-like learning will be present in mechanized industrial processes that are controlled or performed by machines as well as in the greater number of multi-agent computer systems. Therefore, the optimization algorithms dedicated in this paper for scheduling problems with learning are not only the answer for present day scheduling problems (where human plays important role), but they are also a step forward to the improvement of self-learning and adapting systems that undeniably will occur in a new future. To solve the analysed problem, we propose parallel computation approaches that are based on NEH, tabu search and simulated annealing algorithms. The numerical analysis confirm high accuracy of these methods and show that the presented approaches significantly decrease running times of simulated annealing and tabu search and also reduce the running times of NEH