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 <datafield tag="088" ind1="" ind2="">
  <subfield code="a">2024HaeringBarcelona</subfield> 
  </datafield>
<datafield tag="909" ind1="C" ind2="0">
<subfield code="p">TRANSP-OR</subfield>
</datafield>
<datafield tag="980" ind1="" ind2="">
<subfield code="a">TALK</subfield>
</datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Haering, Tom</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Torres, Fabian Alejandro</subfield> 
  </datafield>
 <datafield tag="700" ind1="" ind2="">
  <subfield code="a">Bierlaire, Michel</subfield> 
  </datafield>
<datafield tag="245" ind1="" ind2="">
<subfield code="a">
Fast Algorithms for Capacitated Continuous Pricing with Discrete Choice
Demand Models</subfield>
</datafield>
<datafield tag="260" ind1="" ind2="">
<subfield code="c">2024</subfield>
</datafield>
<datafield tag="711" ind1="2" ind2="">
<subfield code="a">
2nd EPFL Symposium on Transportation Research</subfield>
<subfield code="c">
EPFL, Barcelona, Spain</subfield>
<subfield code="d">February 05, 2024</subfield>
</datafield>
<datafield tag="520" ind1="" ind2="">
<subfield code="a">
This research introduces the Breakpoint Exact Algorithm with Capacities (BEAC) and the Breakpoint Heuristic Algorithm (BHA), both of which offer substantial advancements in solving the choice-based pricing problem (CPP) with and without capacity constraints. The BEAC, enhancing the Breakpoint Exact Algorithm (BEA) with a capacity management strategy, outperforms the state-of-the-art mixed-integer linear programming (MILP) approach by 20 times in computational speed. The BHA, employing a coordinate descent method, excels in high-dimensional scenarios, showing remarkable efficiency in both capacitated and uncapacitated cases. Notably, it outpaces the MILP by a factor of 100 to 5000 for the capacitated case, and the state-of-the-art Branch and Benders Decomposition approach by several orders of magnitude for the uncapacitated case, while maintaining an average optimality gap of less than 0.2\%. The dynamic line search extension of the BHA succeeds in identifying the global optimum in all tested instances, albeit with a significant speed reduction. For future research, other extensions of the BHA to escape local optima should be considered.</subfield>
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