Operations Management and Finance

Production Economics research focuses on various allocation problems, for example on the optimal use of productive resources within manufacturing and service industries or optimal financial decisions.

It encompasses developments in theory and application, wherever engineering meets the managerial and economic environment in which industry operates. Its research has been strongly supported and encouraged by Swedish industries and others. The research methodologies include mathematical modelling, optimisation, decision analysis, simulation, empirical studies and conceptual modelling. The research is centered around two broad avenues, Operations Management and Finance.

Operations Management 

Operation Management is one of focus areas at the Division of Production Economics. We use optimisation and queueing theory to develop models in classic fields such as inventory systems. In addition we also study the operations issues such as production and inventory control, planning and scheduling in newly emerged production systems which facing challenges of global competition, new operations technology, sustainable operations, and new business models. Statistics tools are also used for investigating the development trends in various industries, in order to highlight the critical problems in production operations.

In terms of Operations Management, the following research areas are in general treated by the researchers at the division Production Economics:

  • Manufacturing Planning and Control
  • Supply Chain and Operations Management
  • Sustainable Operations Management
  • Modelling production/inventory systems
  • Manufacturing Strategy


Our research in finance is focused on application of optimisation in finance, with research topics ranging from making optimal investments to estimation of realistic financial models.

In general, we determine optimal investment decisions with stochastic programming, which allows a flexible formulation of the problem that can include transaction costs, taxes and market crashes. By utilizing the inherent structure in stochastic programming problems, we have designed efficient primal-dual interior point solvers which can efficiently solve these problems on parallel machines. Examples of applications include optimal investments in options, hedging of derivative portfolios, optimal management of exchange rate risk and optimal investments in interest rate portfolios.

When an optimal decision is determined it is imperative to have a realistic model of the assets, and much of the focus in recent research is on realistic financial models. The main focus is a general framework for estimating curves and surfaces that are consistent with market prices, and designing efficient optimisation algorithms for determining the most realistic solution. For the interest rate market yield curves are estimated which can deal with different tenors and credit risk, and for the derivative markets local volatility surfaces are determined from market prices. These can give a good description of the risk factors in the financial markets, which with suitably estimated stochastic processes can be used in the stochastic programming models to determine optimal investment decisions.

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