Matches in SemOpenAlex for { <https://semopenalex.org/work/W4290802606> ?p ?o ?g. }
Showing items 1 to 65 of
65
with 100 items per page.
- W4290802606 abstract "Booking control problems are sequential decision-making problems that occur in the domain of revenue management. More precisely, freight booking control focuses on the problem of deciding to accept or reject bookings: given a limited capacity, accept a booking request or reject it to reserve capacity for future bookings with potentially higher revenue. This problem can be formulated as a finite-horizon stochastic dynamic program, where accepting a set of requests results in a profit at the end of the booking period that depends on the cost of fulfilling the accepted bookings. For many freight applications, the cost of fulfilling requests is obtained by solving an operational decision-making problem, which often requires the solutions to mixed-integer linear programs. Routinely solving such operational problems when deploying reinforcement learning algorithms may be too time consuming. The majority of booking control policies are obtained by solving problem-specific mathematical programming relaxations that are often non-trivial to generalize to new problems and, in some cases, provide quite crude approximations. In this work, we propose a two-phase approach: we first train a supervised learning model to predict the objective of the operational problem, and then we deploy the model within reinforcement learning algorithms to compute control policies. This approach is general: it can be used every time the objective function of the end-of-horizon operational problem can be predicted, and it is particularly suitable to those cases where such problems are computationally hard. Furthermore, it allows one to leverage the recent advances in reinforcement learning as routinely solving the operational problem is replaced with a single prediction. Our methodology is evaluated on two booking control problems in the literature, namely, distributional logistics and airline cargo management." @default.
- W4290802606 created "2022-08-12" @default.
- W4290802606 creator A5046133578 @default.
- W4290802606 creator A5053576084 @default.
- W4290802606 creator A5083656325 @default.
- W4290802606 date "2021-01-29" @default.
- W4290802606 modified "2023-09-26" @default.
- W4290802606 title "Reinforcement Learning for Freight Booking Control Problems" @default.
- W4290802606 doi "https://doi.org/10.48550/arxiv.2102.00092" @default.
- W4290802606 hasPublicationYear "2021" @default.
- W4290802606 type Work @default.
- W4290802606 citedByCount "0" @default.
- W4290802606 crossrefType "posted-content" @default.
- W4290802606 hasAuthorship W4290802606A5046133578 @default.
- W4290802606 hasAuthorship W4290802606A5053576084 @default.
- W4290802606 hasAuthorship W4290802606A5083656325 @default.
- W4290802606 hasBestOaLocation W42908026061 @default.
- W4290802606 hasConcept C121955636 @default.
- W4290802606 hasConcept C126255220 @default.
- W4290802606 hasConcept C127413603 @default.
- W4290802606 hasConcept C153083717 @default.
- W4290802606 hasConcept C154945302 @default.
- W4290802606 hasConcept C162324750 @default.
- W4290802606 hasConcept C175444787 @default.
- W4290802606 hasConcept C181622380 @default.
- W4290802606 hasConcept C195487862 @default.
- W4290802606 hasConcept C2775924081 @default.
- W4290802606 hasConcept C2781386248 @default.
- W4290802606 hasConcept C28761237 @default.
- W4290802606 hasConcept C33923547 @default.
- W4290802606 hasConcept C41008148 @default.
- W4290802606 hasConcept C42475967 @default.
- W4290802606 hasConcept C97541855 @default.
- W4290802606 hasConceptScore W4290802606C121955636 @default.
- W4290802606 hasConceptScore W4290802606C126255220 @default.
- W4290802606 hasConceptScore W4290802606C127413603 @default.
- W4290802606 hasConceptScore W4290802606C153083717 @default.
- W4290802606 hasConceptScore W4290802606C154945302 @default.
- W4290802606 hasConceptScore W4290802606C162324750 @default.
- W4290802606 hasConceptScore W4290802606C175444787 @default.
- W4290802606 hasConceptScore W4290802606C181622380 @default.
- W4290802606 hasConceptScore W4290802606C195487862 @default.
- W4290802606 hasConceptScore W4290802606C2775924081 @default.
- W4290802606 hasConceptScore W4290802606C2781386248 @default.
- W4290802606 hasConceptScore W4290802606C28761237 @default.
- W4290802606 hasConceptScore W4290802606C33923547 @default.
- W4290802606 hasConceptScore W4290802606C41008148 @default.
- W4290802606 hasConceptScore W4290802606C42475967 @default.
- W4290802606 hasConceptScore W4290802606C97541855 @default.
- W4290802606 hasLocation W42908026061 @default.
- W4290802606 hasOpenAccess W4290802606 @default.
- W4290802606 hasPrimaryLocation W42908026061 @default.
- W4290802606 hasRelatedWork W2074579507 @default.
- W4290802606 hasRelatedWork W2124495604 @default.
- W4290802606 hasRelatedWork W2230376257 @default.
- W4290802606 hasRelatedWork W2359869946 @default.
- W4290802606 hasRelatedWork W4200057647 @default.
- W4290802606 hasRelatedWork W4282964981 @default.
- W4290802606 hasRelatedWork W4285420526 @default.
- W4290802606 hasRelatedWork W4287864741 @default.
- W4290802606 hasRelatedWork W49953841 @default.
- W4290802606 hasRelatedWork W2185563150 @default.
- W4290802606 isParatext "false" @default.
- W4290802606 isRetracted "false" @default.
- W4290802606 workType "article" @default.