Un modelo integrado para el enrutamiento de aeronaves y la programación de la tripulación: Relajación lagrangiana y algoritmo metaheurístico

[EN] Airline optimization is a significant problem in recent researches and airline industryl as it can determine the level of service, profit and competition status of the airline. Aircraft and crew are expensive resources that need efficient utilization. This paper focuses simultaneously on two major issues including aircraft maintenance routing and crew scheduling. Several key issues such as aircraft replacement, fairly night flights assignment and long-life aircrafts are considered in this model. We used the flight hours as a new framework to control aircraft maintenance. At first, an integrated mathematical model for aircraft routing and crew scheduling problems is developed with the aim of cost minimization. Then, Lagrangian relaxation and Particle Swarm Optimization algorithm (PSO) are used as the solution techniques. To evaluate the efficiency of solution approaches, model is solved with different numerical examples in small, medium and large sizes and compared with GAMS output. The results show that Lagrangian relaxation method provides better solutions comparing to PSO and also has a very small gap to optimum solution.[ES] La optimización de aerolíneas es un problema importante en investigaciones recientes e industria de aerolíneas, ya que puede determinar el nivel de servicio, el beneficio y el estado de competencia de la aerolínea. Las aeronaves y la tripulación son recursos costosos que necesitan una utilización eficiente. Este artículo se centra simultáneamente en dos cuestiones principales, incluyendo el enrutamiento de mantenimiento de aeronaves y la programación de la tripulación. En este modelo se consideran varios temas clave, como el reemplazo de aeronaves, la asignación de vuelos nocturnos y los aviones envejecidos. Usamos las horas de vuelo como un nuevo marco para controlar el mantenimiento de las aeronaves. Al principio, se desarrolla un modelo matemático integrado para el enrutamiento de aeronaves y los problemas de programación de la tripulación con el objetivo de la minimización de costos. A continuación, se utilizan como técnicas de solución la relajación lagran-giana y el algoritmo “Particle Swarm Optimization” (PSO). Para evaluar la eficiencia de los en-foques de la solución, el modelo se resuelve con diferentes ejemplos numéricos en tamaños pequeños, medianos y grandes y se compara con la salida GAMS. Los resultados muestran que el método de relajación lagrangiana proporciona mejores soluciones en comparación con PSO y también tiene una pequeña diferencia para una solución óptimaMirjafari, M.; Rashidi Komijan, A.; Shoja, A. (2020). An integrated model for aircraft routing and crew scheduling: Lagrangian Relaxation and metaheuristic algorithm. 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A robust mathematical model and heuristic algorithms for integrated aircraft routing and scheduling, with consideration of fleet assignment problem, Journal of Air Transport Management, Volume 58, Pages 21-30. https://doi.org/10.1016/j.jairtraman.2016.08.008Jiang, H., Barnhart, C. (2009) Dynamic airline scheduling, Transport. Sci, 43(3), Pages 336-354. https://doi.org/10.1287/trsc.1090.0269Kasirzadeh, A., Saddoune, M., Soumis, F. (2015). Airline crew scheduling: models, algorhitms and data sets, Euro Journal on Transportation and Logistics, 6(2), Pages 111-137. https://doi.org/10.1007/s13676-015-0080-xLacasse-Guay, E., Desaulniers, G., Soumis, F. (2010). Aircraft routing under different business processes, J. Air Transport. Manag, 16(5), Pages 258-263. https://doi.org/10.1016/j.jairtraman.2010.02.001Muter, İbrahim, Birbil, Ş. İlker, Bülbül, Kerem, Şahin, Güvenç,Yenigün, Hüsnü, Taş,Duygu andTüzün, Dilek (2013). 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Integrated Aircraft Fleeting, Routing, and Crew Pairing Models and Algorithms for the Airline Industry, Faculty of the Virginia Polytechnic Institute and State University In Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Industrial and Systems Engineering.Shao, S., Sherali, H.D., Haouari, M. (2017). A novel model and decomposition approach for the integrated airline fleet assignment, aircraft routing, crew pairing problem, Transport. Sci, 51(1), Pages 233-249. https://doi.org/10.1287/trsc.2015.0623Sherali, H.D., Bish, E.K., Zhu, X. (2006). Airline fleet assignment concepts, models and algorithms, Eur. J. Oper. Res, 172(1), Pages 1-30. https://doi.org/10.1016/j.ejor.2005.01.056Warburg, V., Hansen, T.G., Larsen, A., Norman, H., Andersson, E. (2008). Dynamic airline scheduling: an analysis of potentials of refleeting and retiming, J. Air Transport. Manag. 14(4), Pages 163-167. https://doi.org/10.1016/j.jairtraman.2008.03.004Yan, C. and Kung, J. (2018). 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Class-based storage location assignment : an overview of the literature

Storage, per se, is not only an important process in a warehouse, also it has the greatest influence on the most expensive one, i.e., order picking. This study aims to give a literature overview on class-based storage location assignment (CBSLAP). In this paper, we discuss storage policies and present a classification of storage location assignment problem. Next, different configuration of classes are presented. We identify the research gaps in the literature and conclude with promising future research directions

An overview of recent research results and future research avenues using simulation studies in project management

This paper gives an overview of three simulation studies in dynamic project scheduling integrating baseline scheduling with risk analysis and project control. This integration is known in the literature as dynamic scheduling. An integrated project control method is presented using a project control simulation approach that combines the three topics into a single decision support system. The method makes use of Monte Carlo simulations and connects schedule risk analysis (SRA) with earned value management (EVM). A corrective action mechanism is added to the simulation model to measure the efficiency of two alternative project control methods. At the end of the paper, a summary of recent and state-of-the-art results is given, and directions for future research based on a new research study are presented

Managing the bullwhip effect in multi-echelon supply chains

This editorial article presents the bullwhip effect which is one of the major problems faced by supply chain management. The bullwhip effect represents the demand variability amplification as demand information travels upstream in the supply chain. The bullwhip effect research has been attempting to prove its existence, identify its causes, quantify its magnitude and propose mitigation and avoidance solutions. Previous research has relied on different modeling approaches to quantify the bullwhip effect and to investigate the proposed mitigation/avoidance solutions. Extensive research has shown that smoothing replenishment rules and collaboration in supply chain are the most powerful approaches to counteract the bullwhip effect. The objective of this article is to highlight the bullwhip effect avoidance approaches with providing some interesting directions for future research

Assessing the Impact of Road Traffic Externalities on Residential Price Values: a Case Study in Madrid, Spain

[EN] This paper describes a study of the relationship between undesired road traffic externalities and residential price values in the Spanish city of Madrid. A large database was gathered, including the price and characteristics of 21,634 flats and road traffic intensity at 3904 different points across the city. The results obtained by a hedonic model suggest that both distance from the traffic measurement point and average daily traffic are significantly related to the price of residential properties, even after controlling for structural and neighbourhood variables. Distance to traffic areas has a positive impact on dwelling prices, whilst these are negatively related to traffic intensity.Guijarro, F. (2019). Assessing the Impact of Road Traffic Externalities on Residential Price Values: a Case Study in Madrid, Spain. International Journal of Environmental research and Public Health. 16(24):1-13. https://doi.org/10.3390/ijerph16245149S1131624Kim, M., Chang, S. I., Seong, J. C., Holt, J. B., Park, T. H., Ko, J. H., & Croft, J. B. (2012). Road Traffic Noise. American Journal of Preventive Medicine, 43(4), 353-360. doi:10.1016/j.amepre.2012.06.014Sorensen, M., Hvidberg, M., Andersen, Z. J., Nordsborg, R. B., Lillelund, K. G., Jakobsen, J., … Raaschou-Nielsen, O. (2011). Road traffic noise and stroke: a prospective cohort study. European Heart Journal, 32(6), 737-744. doi:10.1093/eurheartj/ehq466Munzel, T., Gori, T., Babisch, W., & Basner, M. (2014). Cardiovascular effects of environmental noise exposure. European Heart Journal, 35(13), 829-836. doi:10.1093/eurheartj/ehu030Bodin, T., Albin, M., Ardö, J., Stroh, E., Östergren, P.-O., & Björk, J. (2009). Road traffic noise and hypertension: results from a cross-sectional public health survey in southern Sweden. Environmental Health, 8(1). doi:10.1186/1476-069x-8-38Lercher, P., Widmann, U., & Thudium, J. (2014). Hypotension and Environmental Noise: A Replication Study. International Journal of Environmental Research and Public Health, 11(9), 8661-8688. doi:10.3390/ijerph110908661Dzhambov, A. M., & Lercher, P. (2019). Road Traffic Noise Exposure and Depression/Anxiety: An Updated Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health, 16(21), 4134. doi:10.3390/ijerph16214134De Kluizenaar, Y., Janssen, S., Vos, H., Salomons, E., Zhou, H., & van den Berg, F. (2013). Road Traffic Noise and Annoyance: A Quantification of the Effect of Quiet Side Exposure at Dwellings. International Journal of Environmental Research and Public Health, 10(6), 2258-2270. doi:10.3390/ijerph10062258Urban, J., & Máca, V. (2013). Linking Traffic Noise, Noise Annoyance and Life Satisfaction: A Case Study. International Journal of Environmental Research and Public Health, 10(5), 1895-1915. doi:10.3390/ijerph10051895Shepherd, D., Welch, D., Dirks, K., & McBride, D. (2013). Do Quiet Areas Afford Greater Health-Related Quality of Life than Noisy Areas? International Journal of Environmental Research and Public Health, 10(4), 1284-1303. doi:10.3390/ijerph10041284Del Giudice, V., De Paola, P., Manganelli, B., & Forte, F. (2017). The Monetary Valuation of Environmental Externalities through the Analysis of Real Estate Prices. Sustainability, 9(2), 229. doi:10.3390/su9020229Wilhelmsson, M. (2000). The Impact of Traffic Noise on the Values of Single-family Houses. Journal of Environmental Planning and Management, 43(6), 799-815. doi:10.1080/09640560020001692Baranzini, A., & Ramirez, J. V. (2005). Paying for Quietness: The Impact of Noise on Geneva Rents. Urban Studies, 42(4), 633-646. doi:10.1080/00420980500060186Kim, K. S., Park, S. J., & Kweon, Y.-J. (2007). Highway traffic noise effects on land price in an urban area. Transportation Research Part D: Transport and Environment, 12(4), 275-280. doi:10.1016/j.trd.2007.03.002Blanco, J. C., & Flindell, I. (2011). Property prices in urban areas affected by road traffic noise. Applied Acoustics, 72(4), 133-141. doi:10.1016/j.apacoust.2010.11.004Łowicki, D., & Piotrowska, S. (2015). Monetary valuation of road noise. Residential property prices as an indicator of the acoustic climate quality. Ecological Indicators, 52, 472-479. doi:10.1016/j.ecolind.2015.01.002Szczepańska, A., Senetra, A., & Wasilewicz-Pszczółkowska, M. (2015). The effect of road traffic noise on the prices of residential property – A case study of the polish city of Olsztyn. Transportation Research Part D: Transport and Environment, 36, 167-177. doi:10.1016/j.trd.2015.02.011Levkovich, O., Rouwendal, J., & van Marwijk, R. (2015). The effects of highway development on housing prices. Transportation, 43(2), 379-405. doi:10.1007/s11116-015-9580-7Li, W., & Saphores, J.-D. (2012). Assessing Impacts of Freeway Truck Traffic on Residential Property Values. 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Goal programming problems with interval coefficients and target intervals. European Journal of Operational Research, 52(3), 345-360. doi:10.1016/0377-2217(91)90169-

Taxonomic classification of planning decisions in health care: a review of the state of the art in OR/MS

We provide a structured overview of the typical decisions to be made in resource capacity planning and control in health care, and a review of relevant OR/MS articles for each planning decision. The contribution of this paper is twofold. First, to position the planning decisions, a taxonomy is presented. This taxonomy provides health care managers and OR/MS researchers with a method to identify, break down and classify planning and control decisions. Second, following the taxonomy, for six health care services, we provide an exhaustive specification of planning and control decisions in resource capacity planning and control. For each planning and control decision, we structurally review the key OR/MS articles and the OR/MS methods and techniques that are applied in the literature to support decision making

Marine baseline and monitoring strategies for Carbon Dioxide Capture and Storage (CCS)

The QICS controlled release experiment demonstrates that leaks of carbon dioxide (CO2) gas can be detected by monitoring acoustic, geochemical and biological parameters within a given marine system. However the natural complexity and variability of marine system responses to (artificial) leakage strongly suggests that there are no absolute indicators of leakage or impact that can unequivocally and universally be used for all potential future storage sites. We suggest a multivariate, hierarchical approach to monitoring, escalating from anomaly detection to attribution, quantification and then impact assessment, as required. Given the spatial heterogeneity of many marine ecosystems it is essential that environmental monitoring programmes are supported by a temporally (tidal, seasonal and annual) and spatially resolved baseline of data from which changes can be accurately identified. In this paper we outline and discuss the options for monitoring methodologies and identify the components of an appropriate baseline survey