Oliver Krauss

Genetic improvement in code interpreters and compilers

Modern compilers provide code optimizations before and during run-time, thus moving required domain knowledge about the compilation process away from the developer and speeding up resulting software. These optimizations are often based on formal proof, or alternatively have recovery paths as backup. Genetic improvement (GI), a field of science utilizing genetic programming, a stochastic optimization technique, has been previously utilized to fix bugs in software and improve non-functional software requirements. This work proposes to research the applicability of GI in an offline phase directly at the interpreter or compiler level. The primary goal is to reformulate existing source code in such a way that existing optimizations can be applied in order to increase performance even further and requiring even less domain knowledge from the developer about a programming language and/or compiler. From these reformulations, patterns can be identified that allow code restructuring without the overhead GI poses.

Multidisciplinary Team Meetings - A Literature Based Process Analysis

Multidisciplinary Team Meetings (MDTM) are conducted to discuss the treatment of one or more patients. This paper discusses MDTM with a focus on tumor treatment and shows workflows in different settings, identifies organizational and technical problems in the MDTM and solutions thereof. It aims to answer the following research questions: (RQ1) What is the current state of the art in MDTM?(RQ2) How are they conducted and what is the variation in different hospital settings? (RQ3) What technical problems and possible solutions thereof exist? This is done by conducting a literature review entailing a forward search of 837 papers and a backward search. The results show that a unified workflow model for MDTM can’t be found since they are highly dependent on institutional and tumor dependent specifics. The identified problems and solutions show a lack of research towards technical solutions and process interoperability. An outlook on extending research in these areas is given.

Heuristics for job shop scheduling with volatile machine constraints

An alteration of the job shop scheduling problem, concerning advertisement scheduling on digital advertisement spaces, is presented. Dispatching Rules (DR), Iterated Local Search (ILS) and Genetic Algorithms (GA) are discussed and applied to the problem space. The results show that ILS is the best performing heuristic, and surpasses the other heuristics especially in large problem spaces (≥ 100 machines, ≥ 100 jobs). The results match previously made findings, which indicates that effects on large-scale problems should be further researched in conjunction with amalgam algorithms between DR, GA and ILS.

Elevator simulation for testing advertisement scheduling systems

An agent-based elevator simulation was implemented to test the validity of an advertisement scheduling system. The elevator simulation imitates the elevators and their advertisement system, thus testing the different schedules and sending the results back to the scheduler. To validate the results, certain data sets of the simulation application are compared with expected values. First results have shown that the created data is in a valid range around the estimations. Finally, methods to further improve the simulation are presented. It was found that agent-based simulations are a good method to test systems, which are too complex or expensive to test in the real environment.