@article{frey_kolisch_artigues_2017, title={Column Generation for Outbound Baggage Handling at Airports}, volume={51}, ISSN={["0041-1655"]}, DOI={10.1287/trsc.2017.0739}, abstractNote={ The planning of outbound baggage handling at international airports is challenging. Outgoing flights have to be assigned and scheduled to handling facilities at which the outgoing baggage is loaded into containers. To avoid disruptions of the system the objective is to minimize workload peaks over the entire system. The resource demand of the jobs that have to be scheduled depends on the arrival process of the baggage. In this paper we present a time-indexed mathematical programming formulation for planning the outbound baggage. We propose an innovative decomposition procedure in combination with a column generation scheme to solve practical problem instances. The decomposition significantly reduces the symmetry effect in the time-indexed formulation and also speeds up the computational time of the corresponding Dantzig–Wolfe formulation. To further improve our column generation algorithm we propose state-of-the-art acceleration techniques for the primal problem and the pricing problem. Computational results based on real data from a major European airport show that the proposed procedure reduces the maximal workloads by more than 60% compared to the current assignment procedure used. }, number={4}, journal={TRANSPORTATION SCIENCE}, author={Frey, Markus and Kolisch, Rainer and Artigues, Christian}, year={2017}, month={Nov}, pages={1226–1241} }
 @article{frey_kiermaier_kolisch_2017, title={Optimizing Inbound Baggage Handling at Airports}, volume={51}, ISSN={["0041-1655"]}, DOI={10.1287/trsc.2016.0702}, abstractNote={ In this paper we consider the planning and scheduling of inbound baggage that is picked up by passengers at the baggage claim hall. Although this is a standard process at airports, to our knowledge there has been no mathematical model proposed in the literature optimizing the inbound baggage handling process. As the inbound baggage handling problem turns out to be NP-hard, we propose a hybrid heuristic combining a greedy randomized adaptive search procedure with a guided fast local search and path-relinking. We demonstrate how we implemented the algorithm at Munich’s Franz Josef Strauss Airport where it is in use to operate the inbound baggage handling process. In a case study, we compare the results of the mathematical model with the solutions of the hybrid heuristic and the solutions provided by the airport. The proposed algorithm reduces baggage peaks at the baggage carousels by 38% and waiting times for passengers by 11%. All computational results are based on an extensive simulation incorporating real world data. }, number={4}, journal={TRANSPORTATION SCIENCE}, author={Frey, Markus and Kiermaier, Ferdinand and Kolisch, Rainer}, year={2017}, month={Nov}, pages={1210–1225} }
 @article{gartner_kolisch_neill_padman_2015, title={Machine Learning Approaches for Early DRG Classification and Resource Allocation}, volume={27}, ISSN={["1526-5528"]}, DOI={10.1287/ijoc.2015.0655}, abstractNote={ Recent research has highlighted the need for upstream planning in healthcare service delivery systems, patient scheduling, and resource allocation in the hospital inpatient setting. This study examines the value of upstream planning within hospital-wide resource allocation decisions based on machine learning (ML) and mixed-integer programming (MIP), focusing on prediction of diagnosis-related groups (DRGs) and the use of these predictions for allocating scarce hospital resources. DRGs are a payment scheme employed at patients’ discharge, where the DRG and length of stay determine the revenue that the hospital obtains. We show that early and accurate DRG classification using ML methods, incorporated into an MIP-based resource allocation model, can increase the hospital’s contribution margin, the number of admitted patients, and the utilization of resources such as operating rooms and beds. We test these methods on hospital data containing more than 16,000 inpatient records and demonstrate improved DRG classification accuracy as compared to the hospital’s current approach. The largest improvements were observed at and before admission, when information such as procedures and diagnoses is typically incomplete, but performance was improved even after a substantial portion of the patient’s length of stay, and under multiple scenarios making different assumptions about the available information. Using the improved DRG predictions within our resource allocation model improves contribution margin by 2.9% and the utilization of scarce resources such as operating rooms and beds from 66.3% to 67.3% and from 70.7% to 71.7%, respectively. This enables 9.0% more nonurgent elective patients to be admitted as compared to the baseline. }, number={4}, journal={INFORMS JOURNAL ON COMPUTING}, author={Gartner, Daniel and Kolisch, Rainer and Neill, Daniel B. and Padman, Rema}, year={2015}, pages={718–734} }