Paweł Rajba

Scheduling Problem with Uncertain Parameters in Just in Time System

This paper tackles a stochastic version of single machine scheduling with random processing times and due dates. The objective function is to find a schedule of jobs which minimizes the weighted sum of earliness and tardiness penalties, taking into consideration probabilistic modelling of the tasks processing times. Since the problem is NP-hard, it is not possible to find optimal values for large scales of the problem in a reasonable run time, therefore, a tabu search meta-heuristic is provided. Computational experiments demonstrate that the proposed tabu search algorithm is strongly capable of finding near optimal stability solutions with a very low gap.

Challenges and mitigation approaches for getting secured applications in an enterprise company

For years many companies have paid attention to making sure infrastructure is protected adequately while making applications secured was underestimated. This approach is changing nowadays, but according to many security research companies (like WhiteHat or Gartner) a lot of vulnerabilities are still present in applications. Those vulnerabilities are on different levels like architecture or code and they have multiple sources like wrong requirements, processes, tools, unskilled developers or everything at the same time. In the paper we present the challenges that were discovered when we applied some mitigation approaches during the security journey in an enterprise company in the automotive industry.

Robustness of the Uncertain Single Machine Total Weighted Tardiness Problem with Elimination Criteria Applied

In the paper we investigate an uncertain single machine total weighted tardiness problem where uncertainty of times of jobs execution and jobs deadlines are modeled by random variables with a normal distribution. We check the influence of applying elimination criteria to the robustness of the determined solutions. Results are very promising and encourage further research.

Sampling Method for the Flow Shop with Uncertain Parameters

In the classic approach for optimization problems modelling well defined parameters are assumed. However, in real life problems we find ourself very often in a situation where parameters are not defined precisely. This may have many sources like inaccurate measurement, inability to establishing precise values, randomness, inconsistent information or subjectivity.

Stable Scheduling with Random Processing Times

In this work stability of solutions determined by algorithms based on tabu search method for a certain (NP-hard) one-machine arrangement problem was examined. The times of tasks performance are deterministic and they also constitute random variables of the standard or the Erlang’s schedule. The best results were obtained when as a criterion to choose an element from the neighborhood convex combinations of the first and the second moments of the random goal function were accepted. In this way determined solutions are stable, i.e. little sensitive to parameters random changes.