February 11 – 16 , 2018, Dagstuhl Seminar 18071

Planning and Operations Research


J. Christopher Beck (University of Toronto, CA)
Daniele Magazzeni (King's College London, GB)
Gabriele Röger (Universität Basel, CH)
Willem-Jan Van Hoeve (Carnegie Mellon University – Pittsburgh, US)

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The aim of operations research is to make better decisions through the application of automated analytic methods. The aim of automated planning is to find a course of actions that transforms a given world situation into a desirable setting. Both areas have in common that they deal with complex systems where a huge space of interacting options makes it almost impossible to humans to take optimal or even good decisions./

From a historical perspective, operations research stems from the application of mathematical methods to (mostly) industrial applications while planning emerged as a subfield of artificial intelligence where the emphasis was traditionally more on symbolic and logical search techniques for the intelligent selection and sequencing of actions to achieve a set of goals. Therefore operations research often focuses on the allocation of scarce resources such as transportation capacity, machine availability, production materials, or money, while planning focuses on the right choice of actions from a large space of possibilities.

A fundamental difference in the traditional problems solved by the two areas is that in operations research the problems are, with some exceptions such as column generation, modeled with a finite (and fixed) set of variables whose values must be assigned in order to satisfy a set of constraints and optimize an objective function. In contrast, in planning, it is typically unknown how many actions are required to achieve a set of goals and so a problem is defined by a state transition system with operators that can be instantiated to create a trajectory through the state space. While this difference results in problems in different complexity classes, it is often possible to cast the same problem as operation research or planning problem. For example, logistics problems are typical applications in both fields.

However, real-world artificial intelligence planning problems often require complex temporal reasoning about the efficient use and transformation of limited resources. For example, a company such as Amazon must use and consume labor, warehouse space, vehicles, and fuel to coordinate the reception, storage, order-taking, packing, and delivery of goods. Operations research deals with the orchestration of known actions rather than deciding what actions to perform and how they need to be coordinated with respect to time and resources. The latter areas are the strength of AI planning. A particular challenge, therefore, is to solve problems that exhibit both the need to develop a plan and requirements that the plan optimizes the use of limited resources over time.

In this Dagstuhl Seminar we bring together researchers in the areas of Artificial Intelligence Planning and Operations Research, and their intersection. In the seminar we want to develop a joint understanding of the problems and solution techniques that are central to these areas and move toward an understanding of how they can be hybridized to better solve existing challenges. The aim of the seminar is to extend the reach of the technologies into currently out-of-reach problems and applications.

  Creative Commons BY 3.0 DE
  J. Christopher Beck, Daniele Magazzeni, Gabriele Röger, and Willem-Jan Van Hoeve


  • Artificial Intelligence / Robotics
  • Modelling / Simulation
  • Optimization / Scheduling


  • Artificial Intelligence
  • Operations Research
  • Automated Planning and Scheduling
  • Real-World Applications

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