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Industrial Optimization

Practical problem solving with mathematical optimization

mathematical optimization scheme

Digitalization allows companies to have access to a lot of data that is relevant for their operation. How to use it to act cleverly is often harder than collecting and managing the data. The Industrial Optimization group has strong experience in developing solutions, which assist or automate decision making in operative or strategic planning in order to find economically optimal solutions.

Our solutions are for example in use in

  • Logistics
  • Production planning
  • Energy market auctions
  • Supply chain management
  • Smart city planning


The solutions are typically mathematical models with case-specific goals and constraints. The scale of models ranges from large mixed-integer linear models to simpler heuristic black-box optimization. For solutions we use both commercial optimization tools and in-house software. In most cases our solutions are embedded in enterprise IT systems.

Besides the typically confidential customer projects we are doing research on applied optimization. This covers applications of optimization to scientific and engineering problems as well as new ways to model and solve important problems.

Some of the research topics close to our heart are

  • Fast and agile ways to develop and maintain optimization models and algorithms
  • Combining Big Data and optimization
  • The role of increasingly cheap cloud computing in optimization

We encourage you to contact us. You may, for example, be interested in discussing

  • How we can help your company to work more efficiently
  • How we can use our scientific and practical competence in joint research activities

Rummukainen, H., and Nurminen, J.K., 2019.  Practical Reinforcement Learning - Experiences in Lot Scheduling Application. 9th IFAC Conference on Manufacturing Modelling, Management and Control, Berlin, Germany, August 28–30, 2019.  IFAC PapersOnLine 52 (13), pp. 1415–1420. 

Rummukainen, H., Makkonen, M., and Uusitalo, J., 2019. Economic value of optical and X-ray CT scanning in bucking of Scots pine. To appear in: Wood Material Science & Engineering.

​Rummukainen, H., Nurminen, J.K., Syrjänen, T., and Numminen, J.-P., 2019.  Machine Learning from Prior Designs for Facility Layout Optimization.  Presented in: International Conference on Optimization and Learning 2019. pdf

Lappalainen, J., Korvola, T., Nurminen, J.K., Lepistö, V., Mäki-Jouppila, T., 2019. Cloud-based framework for simulation-based optimization of ship energy systems. In Proceedings of the 2nd International Conference on Modelling and Optimisation of Ship Energy Systems. University of Strathclyde, Glasgow, UK, May 8–10, pp. 65–71.

Korvola, T., Abdurafikov, R. and Reda, F., 2018. Control strategies for a residential property with solar building, thermal and electricity storages. In EuroSun 2018 Conference Proceedings. doi:10.18086/eurosun2018.06.01

Katasonov, A., Lastusilta, T., Korvola, T., Saari, L., Bendas, D., Camp, R., Mohammed, W. M. and Lee Nieto, A. 2017. An Approach to Production Scheduling Optimization: A Case of an Oil Lubrication and Hydraulic Systems Manufacturer. 2017 International Conference on Engineering, Technology and Innovation. pdf

Ruusu, R., Cao, S., Hasan, A., Kortelainen, J. and Karhela, T. 2016. Developing an Energy Management System for Optimizing the Interaction of a Residential Building with the Electrical and Thermal Grids. CLIMA Conference, May 2016, Aalborg University, Denmark. pdf

Hasan, A., Vesanen, T., Jung N. and Holopainen R. 2016. Automated optimum geometry generation of a building for the minimization of heating and cooling energy demands. BSO 2016, Building Simulation & Optimization 2016, 12th-14th September 2016. Newcastle, UK. pdf

Palonen, M., Hamdy, M. and Hasan, A. 2013. MOBO A New Software for Multi-Objective Building Performance Optimization. BS-13, IBPSA Building Simulation Conference, August 2013, France. pdf

Rummukainen, H., Korvola, T. and Ketonen, M. 2009. Kehittyneiden optimointimenetelmien sovellus sahan tuotannonsuunnittelussa. In: Ketonen, M. (ed.). In Automaatio XVIII Seminaari 17.-18.3.2009 Helsinki [CD-ROM]. SAS publications nr 36. Helsinki: Finnish Society of Automation. pdf

Rummukainen, H., Kinnari, T. and Laakso, M. 2009. Optimization of Wood Transportation. In Madetoja, E., Niskanen, H. and Hämäläinen, J. (ed.). PRS2009 Papermaking Research Symposium, Kuopio, Finland, 1-4 June, 2009 [CD-ROM]. Kuopio: University of Kuopio. pdf


R​esearch within the industrial optimization team is typically done within larger projects. We participate currently in the following projects:


INTENS is a VTT-coordinated Finnish research-industry collaborative consortium striving to proactively advance, promote and digitalize Finnish marine industries and beyond, specially focusing on energy efficiency improvement and emissions reduction of ship energy systems. The project aims to further deepen and uniquely integrate digital transformation into the whole chain of the marine cluster, from R&D to innovation, design, manufacturing and operation. By adopting a Digital Twin approach, it is able to holistically integrate ship energy systems at the component, system, ship and fleet levels, to achieve intelligent and optimal design and operation of ship energy systems throughout their life cycles.

More information: Not yet available

Engineering Rulez

The project will improve the plant engineering process efficiency and quality and thus ensure Finnish competitiveness in global process industry investment projects. On the other hand we will create a new engineering marketplace for platform economy. Improvement of efficiency and quality is done through: 1. Speeding up the engineering process with more automated model generation and model transformations and by enabling automatic path for design information and simulation models to the systems used in plant operation; 2. Increasing the quality of engineering with model-based verification, validation and testing; 3. Establishing interoperability through key-technology standards; 4. Validating the results using industrial applications.

More information:

Capacity with a pOsitive enviRonmEntal and societAL footprInt: portS in the future era (COREALIS)

COREALIS proposes a strategic, innovative framework, supported by disruptive technologies, including Internet of Things (IoT), data analytics, next generation traffic management and emerging 5G networks, for cargo ports to handle upcoming and future capacity, traffic, efficiency and environmental challenges. The proposed beyond state of the art innovations, target to increase efficiency and optimize land use, while being financially viable, respecting circular economy principles and being of service to the urban environment.

More information: 

SPINE - open Source modelling Platform for INtegrated Energy systems (SPINE)

The main objective of the Spine project is to develop and validate an end-to-end energy modelling toolbox that will enable open, practical, flexible and realistic planning of future European energy grids. The Spine Toolbox will have partially automated data retrieval and validation of both input and output data, enabling users to focus on core modelling tasks. The Spine Model’s flexibility in terms of temporal, geographical, technological and sectoral dimensions will allow integrated analyses in several levels of the energy grids. These features will make Spine Toolbox state-of-the-art in energy system modelling, and allow grid operators, energy producers and researchers to carry out analyses that are not possible with current modelling systems.

More information:

Coordinating Optimization of COmplex Industrial Processes (COCOP)

Final aim is to define, design and implement a concept that merges existing industrial control systems with new efficient data management and optimisation methods, and provides means to monitor and control large industrial production processes. The COCOP concept can be applied to any large industrial production site, but the project will demonstrate the concept on two pilot cases (copper and steel manufacturing process) and analyse the transferability to other two sectors: the chemical and water treatment processing. The use of the solution of the project will allow plant operators to approach optimal production and result in reduced operation costs, reduced energy and resource consumption, and decreased on-site material handling time and greenhouse gas emissions.

Project website:

Past Projects

Holistic simulation and optimization of energy systems in Smart Cities (CITYOPT)

The CITYOPT project will create a set of applications and related guidelines that support planning, detailed design and operation of energy systems in urban districts. The project will address energy system optimization in different life cycle phases considering specific optimization potentials and user & stakeholder involvement characteristics. The building of CITYOPT applications will rely on many re-usable component models that are available from existing simulation software libraries. 

Project website:

Smart Power-to-Gas-to (P2G2)

Production of industrial gases such as  hydrogen, oxygen or nitrogen requires high  amounts of energy while it is still operated  with old processes. Smart Power-to-Gas-to (P2G2) provides industrial gas  operators with a cloud-based gas  management system leveraging modern IoT  and optimization technologies. Stakeholders  in logistics, sales, operations or finance  are thus empowered to glean new insights  and drive faster decision making about  when to run production or how to organize  distribution. Each of them will be able to access dedicated  information and tools (including planning,  schedule, routing) to improve productivity,  increase uptime, speed and yield, see Smart P2G2 provides industrial  gas operators with a cloud- based platform to optimize their  production and distribution.  It delivers new insights to make  faster decisions, seize growth  opportunities and save costs.

Project website:

Cloud Collaborative Manufacturing Networks (C2NET)

The goal of C2NET Project is the creation of cloud-enabled tools for supporting the supply network optimization of manufacturing and logistic assets based on collaborative demand, production and delivery plans. C2NET Project will provide a scalable real-time architecture, platform and software to allow the supply network partners: to master complexity and data security of the supply network, to store product, process and logistic data, to optimize the manufacturing assets by the collaborative computation of production plans, to optimize the logistics assets through efficient delivery plans and to render the complete set of supply chain management information on the any digital mobile device (PC, tablets, smartphones, …) of decision makers enabling them to monitor, visualize, control, share and collaborate.

Project website:

Value added by optimal wood raw material allocation & processing (VARMA)

The project goal is to develop customer driven value chains and wood raw material allocation system by implementing smart bucking center for stems.  The fundamental idea of the proposed Wood allocation centre  concept (WAC) is to deliver the right product to the right customer in a timely manner with the highest possible value added.  The focus of the Finnish subproject is on how the combination of new measurement technologies, optimisation/allocation system and the ability to communicate (i.e. information system) within the network of wood value chains can open whole new opportunities in terms of value creation in production as well as emergence of novel services.

Project website: Not available.