Management Science Laboratory addresses a variety of topics associated with operations management, ranging from mathematical programming to numerical analysis. "We go whereever there is a mathematical problem." Our challenge starts when someone feels a conventional operation would not be efficient and might be improved. We do our best to assess, solve, and find a better solution.
Common topics in management science is diverse. Scheduling; is the lead time to manufacture a product too long? Or is tardiness frequent? Yes, we may provide a more efficient resource assignment or timetable. Location planning; can we reduce the number of facilities to provide service to a region? Yes, we could find a better deployment of facilities. Transportation planning; which scenario is the best to abate traffic congestion in a network? Yes, we can efficiently simulate traffic flows for a large-scale network. Decision making; how can we mitigate discrepant evaluations regarding multiple objects? Yes, we may provide a pairwise comparison table that would convince stakeholders.
Heretofore, we have been particularly interested in the abovementioned topics in operations management. The approaches we use are again diverse; discrete algebra, control theory, graph theory, combinatorial optimization, high-performance computing, and more. In scheduling issues, we might adopt control theory-related approaches, while occasionally use discrete algebra. In location planning contexts, we deal with a sparse graph, in which a mathematical programming or algorithmic approach is frequently effective. Because of the diversity of our targets, even-keeled vision is the most important for us.
If you are already interested in a specific topic in management science, then you are already at the right place! You can roll your research soon. If otherwise, even if you are wishy-washy, don’t worry. As long as you are not awkward to math nor programming, you may come across a new target in our laboratory.
The chief research staff (Hiroyuki Goto), collaborative researchers, and some of graduate students have been into the following themes:
1. Scheduling methods under limited resources
Keywords: max-plus algebra, optimal control, resource constraint, critical chain project management
2. Effective use of geographical information systems (GIS) data
Keywords: digital elevation, terrain analysis, data coding & encoding, parallel processing
3. Natural language processing (NIP)
Keywords: morphological analysis, indexing, classification & clustering, multivariate analysis
