With the outbreak of COVID-19, we used an annealing machine to create shifts for our researchers under constraints that took into account the newly created risks of infection.
With the unprecedented outbreak of the infectious disease, the researchers were required to work under
which did not allow for overcrowding. To ameliorate this issue, the researchers were divided into separate
allow the continuation of their work.
We decided to set restrictions on the number of people who could work a shift. For example, for a large room that houses several departments, we set a limit on the number of people who could come to work across multiple departments. We also set different conditions for personnel working in laboratories, depending on the size of each laboratory.
Even though more than 30 departments share the same building, there was no mechanism to manage work restrictions across these departments.
The work schedule for each research topic was also in flux, with readjusting shifts being required in such high frequency.
In addition, because many different teams use the laboratory, if prior arrangements are not made properly, experiments cannot be conducted even if a person comes to work if he or she has a conflict with a member of another team. These issues made the coordination process very complicated. Forcing the researcher or department head to make these sorts of adjustments distracted them from their main tasks.
We collected the following information and used an annealing machine to compute the optimal combination of researchers' work schedules that satisfied various constraints, thereby automating the creation of researchers' shifts.
We have set a limit of up to 30% of the normal capacity of each laboratory or rooms.
Fixed shift placement
Ideal shift placement
There had been no mechanism to manage attendance across departments, but it is now possible to organize
shift work for
the hundreds of people who work at the institute.
Our solution can create shifts on a weekly basis, making it possible to accommodate requests to come to work with as much consideration as possible for the researcher's research progress and family circumstances.
The "Work Shift Optimization Solutions" uses Hitachi's calculation technology to create optimal work shifts that balance the needs of management, inquiring customers, and operators of companies operating call centers.See more...
Combinatorial optimization will be explained by using two easy-to-understand examples of familiar combinatorial optimization problems. The importance of finding and using fast solution methods for combinatorial optimization processes in an IoT society will also be discussed.
This article presents a use case in which an annealing machine is used to formulate a reinsurance portfolio that leverages the vast amount of data held by an insurance company to address natural catastrophe risks.
For beginners, this article explains the procedures and key points of the process of solving optimization problems with an annealing machine. The process involves dividing the problem into four stages: "organizing problem and defining problem," "formulation," "input data preparation," and "executing CMOS annealing machine".
This article provides an easy-to-understand explanation of the mathematical assumptions you need to understand, focusing on "quadratic" and "discrete," which are some of the characteristics of the problems that annealing machines excel at solving.