NEC Project:
Title Application of annealing machines to production planning optimization
Industrial Partner NEC Corporation
NEC (https://www.nec.com), founded in 1899, is now particularly addressing thedevelopment of solutions for society that will help resolve many issues the world is facing
and which will engender the creation of a brighter and more prosperous society. Throughco-creation initiatives with many different stakeholders, including customers, business
partners, private individuals, government agencies, and international institutions, we areactively devising new business models to create social value by harnessing ourextensive information and communication technology (ICT) assets.
Industrial Mentors
Ryoji Miyazaki, Ph.D. NEC Corp.
BackgroundIn recent years, the diversification of needs has changed the manufacturing scenefrom mass production to high-mix low volume production. On the other hand, there has
been a remarkable development of general-purpose processing devices that canprocess multiple products with a single machine, such as 3D printers, laser cutting
machines, computer numerical control machines, and surface mounting devices. As aresult, factories are faced with the need to efficiently produce multiple types of products
in a single day while sharing general-purpose processing devices. In order to achieve
such production, efficient plans must be made regarding which products to produce, inwhat quantities, and in what order. However, production planning in such a situation is
not easy, and that often remains dependent on certain skilled workers or inefficientmethods. If an efficient production plan can be created in a stable manner, thecompetitiveness of the factories will be directly improved.
The production planning is also known as job-shop scheduling [1], which is a kind of
combinatorial optimization problem. Combinatorial optimization problems are latent insocial issues and are classified as NP-hard in terms of computational complexity theory.

Because of their difficulty, they are often left as an unsolvable part of a social problem.
Optimization of production planning is a concrete example of such problems.
One approach to combinatorial optimization problems is simulated annealing [2].
While this generic method can be applied to many problems, it is not as efficient as thealgorithms devised for each problem. However, optimization using annealing methods
has recently been reevaluated. The quantum computer boom triggered that. Quantum
computers are different from conventional computers in that they use quantum effects toperform calculations, and are expected to have the potential for high-speed calculations[3]. Quantum annealing [4,5], which incorporates quantum effects into simulated
annealing, has been physically implemented and is provided by D-Wave Systems [6] asa quantum annealer. However, the scale of the problem this machine can handle is notlarge. The advent of the quantum annealer prompted vendors to develop pseudoannealing environments that use supercomputers or other devices to perform annealing
calculations. NEC provides Vector Annealing [7], an annealing-based algorithm
implemented on NEC’s own supercomputer, SX-Aurora TSUBASA [8]. These
environments have the advantage of being able to handle large scale problems in amanner similar to quantum annealing. Thus, we can now utilize powerful optimization
environments.
In the current situation, we can expect to solve complex production planning problems
using the latest annealing machines. However, we have little knowledge of how big aproblem can be solved and in what amount of time. In addition, there are various typesof production planning problems, and we do not know what types of problems are easy
or difficult to solve with annealing. Comparison with other methods is also an importantissue.
Project and Expectations
In this project, we will approach the optimization problem of production planning
mainly by annealing. We will study how to formulate problems and how to apply
annealing to the problems effectively. As mentioned in Background, since there arevarious types of production plans, we will attempt to apply annealing to several types ofproblems. In addition, we will not only study the existing settings of the production plans
but also create the settings themselves, to find out what settings annealing can beapplied to. We expect to clarify how large a problem can be solved in a realistic amountof time, and what kind of constraints can be handled. Performance comparison withmethods other than annealing will be expected as an advanced task.
In this project, students will be able to use the latest annealing machines to solve
real-world social problems. When using the annealing machine, coding in Python isrequired. Therefore, the students need to have Python programming skills. Knowledgeof computer-based optimization methods is also desirable, while experience using
annealing machines is not required. The environment for using the annealing machine
will be prepared by NEC. At present, the use of D-Wave and Vector Annealing isenvisioned. NEC will also provide materials for learning about production planning
optimization and annealing. Examples of production planning will also be given. However,as mentioned above, the students are encouraged to create their own problems andstudy the applicability of annealing, not limited to those specific examples.
Requirements
Python programming skills.
References[1] “The Job Shop Problem” in Google OR-Tools,
https://developers.google.com/optimization/scheduling/job_shop[2] S. Kirkpatrick, C. D. Gelatt Jr., M. P. Vecchi, “Optimization by Simulated Annealing,”
Science 220, 671 (1983); The article on simulated annealing in Wikipedia,
https://en.wikipedia.org/wiki/Simulated_annealing[3] “What is quantum computing?” in IBM’s website,
https://www.ibm.com/topics/quantum-computing[4] “What is Quantum Annealing?” in D-Wave System Documentation,
https://docs.dwavesys.com/docs/latest/c_gs_2.html[5] H. Nishimori, “Quantum Annealing,”
http://www.qa.iir.titech.ac.jp/~nishimori/QA/q-annealing_e.html[6] D-Wave Systems Inc., https://www.dwavesys.com/[7] Announcement from NEC, “NEC launches simulated annealing service utilizing vector
supercomputers,” https://www.nec.com/en/press/202109/global_20210901_01.html[8] NEC’s website on SX-Aurora TSUBASA, https://www.hpc.nec/