@inproceedings {icaps16-88,
    track    = {​​​Applications Track},
    title    = {Solving Realistic Unit Commitment Problems Using Temporal Planning: Challenges and Solutions},
    url      = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/12992},
    author   = {Chiara Piacentini and  Daniele Magazzeni and  Derek Long and  Maria Fox and  Chris Dent},
    abstract = {When facing real world planning problems, standard planners are often inadequate and enhancement of the current techniques are required. In this paper we present the challenges that we have faced in solving the Unit Commitment (UC) problem, a well-known problem in the electrical power industry for which current best methods are based on Mixed Integer Programming (MIP). Typical UC instances involve hundreds or even thousands of generating units, pushing the scalability of state of the art planners beyond their limits. Furthermore, UC is characterised by state-dependent action costs, a feature that not many domain independent planners can efficiently handle. In this paper we focus on the challenge of making domain-independent planning competitive with the MIP method on realistic-sized UC instances. We present the results of our investigation into modelling the UC problem as a temporal planning problem, and show how we scaled up from handling fewer than 10 generating units to more than 400, obtaining solutions almost as high quality as those generated by MIP. We conclude by discussing future directions for temporal planning in this domain, that lie beyond what can be modelled and solved using MIP methods.},
    keywords = {Description and modeling of novel application domains,Engineering issues in using P&S techniques}
}

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In this paper we present the challenges that we have faced in solving the Unit Commitment (UC) problem, a well-known problem in the electrical power industry for which current best methods are based on Mixed Integer Programming (MIP). Typical UC instances involve hundreds or even thousands of generating units, pushing the scalability of state of the art planners beyond their limits. Furthermore, UC is characterised by state-dependent action costs, a feature that not many domain independent planners can efficiently handle. In this paper we focus on the challenge of making domain-independent planning competitive with the MIP method on realistic-sized UC instances. We present the results of our investigation into modelling the UC problem as a temporal planning problem, and show how we scaled up from handling fewer than 10 generating units to more than 400, obtaining solutions almost as high quality as those generated by MIP. We conclude by discussing future directions for temporal planning in this domain, that lie beyond what can be modelled and solved using MIP methods.","keywords":"Description and modeling of novel application domains,Engineering issues in using P&S techniques","bibtex":"@inproceedings {icaps16-88,\r\n track = {​​​Applications Track},\r\n title = {Solving Realistic Unit Commitment Problems Using Temporal Planning: Challenges and Solutions},\r\n url = {http://www.aaai.org/ocs/index.php/ICAPS/ICAPS16/paper/view/12992},\r\n author = {Chiara Piacentini and Daniele Magazzeni and Derek Long and Maria Fox and Chris Dent},\r\n abstract = {When facing real world planning problems, standard planners are often inadequate and enhancement of the current techniques are required. In this paper we present the challenges that we have faced in solving the Unit Commitment (UC) problem, a well-known problem in the electrical power industry for which current best methods are based on Mixed Integer Programming (MIP). Typical UC instances involve hundreds or even thousands of generating units, pushing the scalability of state of the art planners beyond their limits. Furthermore, UC is characterised by state-dependent action costs, a feature that not many domain independent planners can efficiently handle. In this paper we focus on the challenge of making domain-independent planning competitive with the MIP method on realistic-sized UC instances. We present the results of our investigation into modelling the UC problem as a temporal planning problem, and show how we scaled up from handling fewer than 10 generating units to more than 400, obtaining solutions almost as high quality as those generated by MIP. 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