Yesterday, we published a new course titled "Metaheuristic Optimization". On the course website, you can find all slides and example algorithm implementations and you can download the complete course material as a single tar.xz archive (can be opened under Windows and Linux) from here. As the third completely published course of our institute, after the courses on "Object-Oriented Programming with Java" and "Distributed Computing", this marks another milestone in our strive for teaching excellence.

The new course tries to give a complete and yet in-depth overview on the topic of optimization from the perspective of metaheuristics, i.e., approximate algorithms that can find good solutions for computational hard problems within a short time. The course is designed to require little background knowledge. Many of the presented algorithms can directly be implemented during the course in Java by the instructor within a few minutes. This shows that the algorithms we discuss are not scary and can be mastered even with basic programming knowledge. It also closes the gap between research and practice – after all, we are the Institute of Applied Optimization.

In the course, we discuss a broad spectrum of different optimization methods, ranging from local search algorithms such as hill climbers, Simulated Annealing, and Tabu Search to global search methods such as Evolutionary Algorithms, Evolution Strategies, Genetic Programming, Differential Evolution, Particle Swarm Optimization, Ant Colony Optimization, and Estimation of Distribution Algorithms. We also discuss several phenomena that make problems difficult for these algorithms as well as general concepts such as multi-objective and constraint optimization as well as several example applications. All in all, this course aims to give the student the knowledge to recognize an optimization problem when she sees it, the ability to choose the right algorithm for the right problem, together with the practical experience to implement and apply said algorithm in a short time.

Benchmarking, the empirical algorithm performance comparison, is usually the only feasible way to find which algorithm is good for a given problem. Benchmarking consists of two steps: First, the algorithms are applied to the benchmarking problems and data is collected. Second, the collected data is evaluated. There is little guidance for the first and a lack of tool support for the second step. Researchers investigating new problems need to implement both data collection and evaluation. In our new paper "From Standardized Data Formats to Standardized Tools for Optimization Algorithm Benchmarking," we want to make the case for defining standard directory structures and file formats for the performance data and metadata of experiments with optimization algorithms. Such formats must be easy to read, write, and to incorporate into existing setups. If there are commonly accepted formats and researchers would actually use them, then this would allow more general tools to emerge. There would be real incentive for everyone who makes an evaluation tool to use the common format right away and then, maybe, publish their tool for others to use. Then, researchers then would no longer need to implement their own evaluation programs. We try to derive suitable formats by analyzing what existing tools do and what information they need. We then present a general tool, our framework, including an open source library for reading and writing data in our format. Since our framework obtains its data from a general file format, it can assess the performance of arbitrary algorithms implemented in arbitrary programming languages on arbitrary single-objective optimization problems.

Yesterday I had the honor to attend the Chinese-German Future Forum on the Collaboration in Vocational Education [Deutsch-Chinesisches Zukunftsforum zur Zusammenarbeit in der Berufsbildung, 中德职业教育合作研讨会寄语中德职教未来发展] which was one of the parallel sessions of the [Ersten Sitzung des Hochrangigen Deutsch-Chinesischen Dialogs für den gesellschaftlich-kulturellen Austausch, 中德高级别人文交流对话机制首次会议] held at the Diaoyutai State Guesthouse [钓鱼台国宾馆] in Beijing as part of the delegation of our Hefei University [合肥学院]. The meeting was co-organized by the Central Institute for Vocational and Technical Education (CIVTE) of the Ministry of Education of China [中国教育部职业技术教育中心研究所], the China Education Association For International Exchange [中国教育国际交流协会], and the Hans-Seidel Foundation [德国汉斯·赛德尔基金会]. Hefei University can look back on a history of more than thirty years of highly successful collaboration with German institutes on vocational education, so this meeting was an important venue for us. Here you can find the conference programme in Chinese and German language.

If you do not live in an English-speaking country, your language will make use of all sorts of odd characters, such as "ä", "ö", "ü", and "ß" in German or "你" and "我" in Chinese. Of course, you intend that such characters, if used in your web page, blog posts, or text files, come out correctly on the screen of your readers. In my course on Distributed Computing, there actually is a lesson on text encoding, but I can only briefly touch the topic there. Here I just want to summarize it a bit more comprehensively.

Today, I attended the fresh logistics Asia 2017 exhibition in Shanghai, a trade show for all elements involved in the logistics of fresh food delivery. Fresh food logistics are basically logistics with added requirements regarding temperature, delay, and safe handling of the transported items. The exhibits ranged from production machinery, delivery cars, fork lifts, and big and small containers, and storage systems over appliances like freezers, fridges, cooling systems and doors, registers to food and beverage products and supply offers. Like in case of the Intertraffic China 2017 exhibition I visited about ten days ago, the focus was more on hardware, with very cool exhibits and demonstrations.

For me as a researcher in optimization and mainly interested in the planning and the operations research making logistics and supply chains efficient, it was particularly interesting to see that even at such a rather hardware- and product-oriented fair, several exhibitors focusing on digitization, data management, and optimization of the logistics aspects were present, such as 晶链通. Yet, this subject was less prominent compared to the Intermodal Asia 2017 exhibition, maybe because large-volume container transport on a global scale over a heterogeneous network of carriers makes central data keeping an absolute requirement. Optimization and planning is enabled by the availability of data, it cannot work easily with Excel sheets. This is why I think that a technological evolution will probably start with better ERP, CRM, and warehouse management software, maybe as SaaS systems, for logistics and supply-chain management. Such systems have an immediate and obvious advantage for their users, as they enforce a clear work structure and allow for central statistical analysis. Based on the data collected, it becomes possible to move towards automated planning and optimization instead of starting with the latter.

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