Zoekresultaten

An optimization-simulation closed-loop feedback framework for modeling the airport capacity management problem under uncertainty

This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.

Enhancing a simulation-based Allocator Software with machine learning and genetic algorithms for improving the gate assignment problem

Assigning gates to flights considering physical, operational, and temporal constraints is known as the Gate Assignment Problem. This article proposes the novelty of coupling a commercial stand and gate allocation software with an off-the-grid optimization algorithm. The software provides the assignment costs, verifies constraints and restrictions of an airport, and provides an initial allocation solution. The gate assignment problem was solved using a genetic algorithm. To improve the robustness of the allocation results, delays and early arrivals are predicted using a random forest regressor, a machine learning technique and in turn they are considered by the optimization algorithm. Weather data and schedules were obtained from Zurich International Airport. Results showed that the combination of the techniques result in more efficient and robust solutions with higher degree of applicability than the one possible with the sole use of them independently.

Opt-Sim approach for the gate allocation problem in covid-19 times

This study tackles the gate allocation problem (GAP) at the airport terminal, considering the current covid-19 pandemic restrictions. The GAP has been extensively studied by the research community in the last decades, as it represents a critical factor that determines an airport's capacity. Currently, the airport passenger terminal operations have been redesigned to be aligned and respect the covid-19 regulation worldwide. This provides operators with new challenges on how to handle the passengers inside the terminal. The purpose of this study is to come up with an efficient gate allocator that considers potential issues derived by the current pandemic, i.e., avoid overcrowded areas. A sim-opt approach has been developed where an evolutionary algorithm (EA) is used in combination with a dynamic passenger flow simulation model to find a feasible solution. The EA aims to find a (sub)optimal solution for the GAP, while the simulation model evaluates its efficiency and feasibility in a real-life scenario. To evaluate the potential of the Opt-Sim approach, it has been applied to a real airport case study.

Diversity scale

Evolutionary analysis of the carnitine- and choline acyltransferases suggests distinct evolution of CPT2 versus CPT1 and related variants

Carnitine/choline acyltransferases play diverse roles in energy metabolism and neuronal signalling. Our knowledge of their evolutionary relationships, important for functional understanding, is incomplete. Therefore, we aimed to determine the evolutionary relationships of these eukaryotic transferases. We performed extensivephylogenetic and intron position analyses. We found that mammalian intramitochondrial CPT2 is most closely related to cytosolic yeast carnitine transferases (Sc-YAT1 and 2), whereas the other members of the family are related to intraorganellar yeast Sc-CAT2. Therefore, the cytosolically active CPT1 more closely resembles intramitochondrial ancestors than CPT2. The choline acetyltransferase is closely related to carnitine acetyltransferase and shows lower evolutionary rates than long chain acyltransferases. In the CPT1 family several duplications occurred during animal radiation, leading to the isoforms CPT1A, CPT1B and CPT1C. In addition, we found five CPT1-like genes in Caenorhabditis elegans that strongly group to the CPT1 family. The long branch leading to mammalian brain isoform CPT1C suggests that either strong positive or relaxed evolution has taken place on this node. The presented evolutionary delineation of carnitine/choline acyltransferases adds to current knowledge on their functions and provides tangible leads for further experimental research.

Balancing continuity of care and home care schedule costs using blueprint routes

In a home care setting, high-quality care is typically associated with continuity of care. In addition, the increasing pressure due to labor shortages calls for cost-efficient operations. This paper focuses on obtaining cost-efficient daily schedules over a longer time horizon, with balanced shift lengths, while ensuring continuity of care (using the continuity of care index). To address this challenge, we propose a novel method based on blueprint routes. This method generates daily schedules by constructing optimized shifts and routes with regard to travel time, (time window) waiting time, and shift costs based on hourly wages. To ensure continuity of care, the daily scheduling decisions are strategically guided using the concept named blueprint routes. The blueprint routes are pre-optimized (partial) routes that help to align the daily schedules to achieve continuity of care in the subsequent nurse-to-shift assignment. Model-based evolutionary algorithms are employed to overcome the NP-hardness of the routing problem and nurse-to-shift assignment. Real-life-based numerical experiments demonstrate that continuity of care does not have to compromise home care schedule costs significantly.