Editorial Challenge: From a Quarterly to a Bimonthly Journal

Starting with issue 4 of volume 7(2012) International Journal of Computers Communications & Control (INT J COMPUT COMMUN, IJCCC) [4] is a member of, and subscribes to the principles of, the Committee on Publication Ethics (COPE) [2].Beginning with issue 1 of volume 8(2013) IJCCC will be published as a bimonthly journal (6 issues/year) [5]

Cell Formation Heuristic Procedure Considering Production Data

[EN] Manufacturing cell formation is one of foremost, and critical aspect of any manufacturing cell design problem. A large number of cell formation methods are developed and still counting. Consideration of production data in cell formation makes these methods more complex and tedious. In this paper an attempt has been made to develop a simple, easy to understand and implement cell formation procedure, having the capability to handle production data viz. operation sequence, production volume, and inter-cell movement cost simultaneously. The results obtained from proposed procedures are in tune with some highly complex methods, which validates the performance of proposed procedure. To demonstrate its ability to handle other production parameters with little modifications, a modification for consideration to part processing cost in addition to above mentioned production data is developed and explained. Towards the end the procedure to handle alternate process plans in conjugation with production data by the proposed cell formation procedure is also discussed.Kumar, S.; Sharma, RK. (2014). Cell Formation Heuristic Procedure Considering Production Data. International Journal of Production Management and Engineering. 2(2):75-84. doi:10.4995/ijpme.2014.2078SWORD758422Ahi, A., Aryanezhad, M. B., Ashtiani, B., & Makui, A. (2009). A novel approach to determine cell formation, intracellular machine layout and cell layout in the CMS problem based on TOPSIS method. Computers & Operations Research, 36(5), 1478-1496. doi:10.1016/j.cor.2008.02.012Arkat, J., Hosseinabadi Farahani, M., & Hosseini, L. (2011). Integrating cell formation with cellular layout and operations scheduling. The International Journal of Advanced Manufacturing Technology, 61(5-8), 637-647. doi:10.1007/s00170-011-3733-4Beaulieu, A., Ait-Kadi, D., & Gharbi, A. (1993). Heuristic for Flexible Machine Selection Problems. Journal of Decision Systems, 2(3-4), 241-253. doi:10.1080/12460125.1993.10511583Beaulieu, A., Gharbi, A., & Ait-Kadi. (1997). An algorithm for the cell formation and the machine selection problems in the design of a cellular manufacturing system. International Journal of Production Research, 35(7), 1857-1874. doi:10.1080/002075497194958Boutsinas, B. (2013). Machine-part cell formation using biclustering. European Journal of Operational Research, 230(3), 563-572. doi:10.1016/j.ejor.2013.05.007Chow, W. S., and Hawaleshka, O., (1992), An efficient algorithm for solving the machine chaining problem in cellular manufacturing, Computers ind. Engng., Vol. 22, No. 1, 95-100.CHU, C.-H., & TSAI, M. (1990). A comparison of three array-based clustering techniques for manufacturing cell formation. International Journal of Production Research, 28(8), 1417-1433. doi:10.1080/00207549008942802Nair, G. J., & Narendran, T. T. (1998). CASE: A clustering algorithm for cell formation with sequence data. International Journal of Production Research, 36(1), 157-180. doi:10.1080/002075498193985Jie Lian, Chen Guang Liu, Wen Juan Li, Steve Evans, Yong Yin, (2013), Formation of independent manufacturing cells with the consideration of multiple identical machines, International journal of production research, Kim, C. O.,Baek, J. G., Baek, J. K., (2004), A two-phase heuristic algorithm for cell formation problems considering alternative part routes and machine sequences, International Journal of Production Research, 42:18, 3911-3927,Krushinsky, D., & Goldengorin, B. (2012). An exact model for cell formation in group technology. Computational Management Science, 9(3), 323-338. doi:10.1007/s10287-012-0146-2Kumar, L. (2008). Part-machine group formation with operation sequence, time and production volume. International Journal of Simulation Modelling, 7(4), 198-209. doi:10.2507/ijsimm07(4)4.113Lokesh, K., & Jain, P. K. (2010). Concurrently part-machine groups formation with important production data. International Journal of Simulation Modelling, 9(1), 5-16. doi:10.2507/ijsimm09(1)1.133Miltenburg, J., & Zhang, W. (1991). A comparative evaluation of nine well-known algorithms for solving the cell formation problem in group technology. Journal of Operations Management, 10(1), 44-72. doi:10.1016/0272-6963(91)90035-vMukattash, A. M., Adil, M. B., & Tahboub, K. K. (2002). Heuristic approaches for part assignment in cell formation. Computers & Industrial Engineering, 42(2-4), 329-341. doi:10.1016/s0360-8352(02)00020-7Mahesh, O., & Srinivasan, G. (2002). Incremental cell formation considering alternative machines. International Journal of Production Research, 40(14), 3291-3310. doi:10.1080/00207540210146189Sudhakara Pandian, R., & Mahapatra, S. S. (2009). Manufacturing cell formation with production data using neural networks. Computers & Industrial Engineering, 56(4), 1340-1347. doi:10.1016/j.cie.2008.08.003Papaioannou, G., & Wilson, J. M. (2010). The evolution of cell formation problem methodologies based on recent studies (1997–2008): Review and directions for future research. European Journal of Operational Research, 206(3), 509-521. doi:10.1016/j.ejor.2009.10.020Sarker, B. R., (1996), The resemblance coefficients in group technology: a survey and comparative study of relational metrics, Computers ind. Engng., Vol. 30, No. 1, 103-116.Seifoddini, H., (1998), Comparison between single linkage and average linkage clustering techniques in forming machine cells", Computers & industrial engineering 15(14), 210-216.SHAFER, S. M., & MEREDITH, J. R. (1990). A comparison of selected manufacturing cell formation techniques. International Journal of Production Research, 28(4), 661-673. doi:10.1080/00207549008942747VENUGOPAL, V., & NARENDRAN, T. T. (1994). Machine-cell formation through neural network models. International Journal of Production Research, 32(9), 2105-2116. doi:10.1080/00207549408957061Won, Y., & Lee, K. C. (2001). Group technology cell formation considering operation sequences and production volumes. International Journal of Production Research, 39(13), 2755-2768. doi:10.1080/00207540010005060Yasuda *, K., Hu, L., & Yin, Y. (2005). A grouping genetic algorithm for the multi-objective cell formation problem. International Journal of Production Research, 43(4), 829-853. doi:10.1080/00207540512331311859Yin, Y., & Yasuda, K. (2005). Similarity coefficient methods applied to the cell formation problem: a comparative investigation. Computers & Industrial Engineering, 48(3), 471-489. doi:10.1016/j.cie.2003.01.001Yin, Y., & Yasuda, K. (2006). Similarity coefficient methods applied to the cell formation problem: A taxonomy and review. International Journal of Production Economics, 101(2), 329-352. doi:10.1016/j.ijpe.2005.01.01

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. WPOM-Working Papers on Operations Management. 11(1):25-38. https://doi.org/10.4995/wpom.v11i1.12891OJS2538111Al-Thani, Nayla Ahmad, Ben Ahmed, Mohamed and Haouari, Mohamed (2016). A model and optimization-based heuristic for the operational aircraft maintenance routing problem, Transportation Research Part C: Emerging Technologies, Volume 72, Pages 29-44. https://doi.org/10.1016/j.trc.2016.09.004Azadeh, A., HosseinabadiFarahani, M., Eivazy, H., Nazari-Shirkouhi, S., &Asadipour, G. (2013). A hybrid meta-heuristic algorithm for optimization of crew scheduling, Applied Soft Computing, Volume 13, Pages 158-164. https://doi.org/10.1016/j.asoc.2012.08.012Barnhart C. and Cohn, A. (2004). Airline schedule planning: Accomplishments and opportunities, Manufacturing & Service Operations Management, 6(1):3-22, 47, 69, 141, 144. https://doi.org/10.1287/msom.1030.0018Basdere, Mehmet and Bilge, Umit (2014). Operational aircraft maintenance routing problem with remaining time consideration, European Journal of Operational Research, Volume 235, Pages 315-328. https://doi.org/10.1016/j.ejor.2013.10.066Bazargan, Massoud (2010). Airline Operations and scheduling second edition, Embry-Riddle Aeronautical University, USA, Ashgate publishing limite.Belien, Jeroen, Demeulemeester, Eric and Brecht (2010). Integrated staffing and scheduling for an aircraft line maintenance problem, HUB RESEARCH PAPER Economics & Management.Ben Ahmed, M., Zeghal Mansour, Farah and Haouari, Mohamed (2018). Robust integrated maintenance aircraft routing and crew pairing, Journal of Air Transport Management, Volume 73, Pages 15-31. https://doi.org/10.1016/j.jairtraman.2018.07.007Ben Ahmed, M., Zeghal Mansour, F., Haouari, M. (2017). A two-level optimization approach for robust aircraft routing and retiming, Computers and Industrial Engineering, Volume 112, Pages 586-594. https://doi.org/10.1016/j.cie.2016.09.021Borndorfer, R., Schelten, U., Schlechte, T., Weider, S. (2006). A column generation approach to airline crew scheduling, Springer Berlin Heidelberg, Pages 343-348. https://doi.org/10.1007/3-540-32539-5_54Clarke, L., E. Johnson, G. Nemhauser, Z. Zhu. (1997). The Aircraft Rotation Problem. Annals of Operations Research, 69, Pages 33-46. https://doi.org/10.1023/A:1018945415148Deveci, Muhammet and ÇetinDemirel, Nihan (2018). Evolutionary algorithms for solving the airline crew pairing problem, Computers & Industrial Engineering, Volume 115, Pages 389-406. https://doi.org/10.1016/j.cie.2017.11.022Dozic, S., Kalic, M. (2015). Three-stage airline fleet planning model, J. Air Transport. Manag, 43, Pages 30-39. https://doi.org/10.1016/j.jairtraman.2015.03.011Eltoukhy, A.E., Chan, F.T., Chung, S. (2017). Airline schedule planning: a review and future directions, Ind. Manag. Data Syst, 117(6), Pages 1201-1243. https://doi.org/10.1108/IMDS-09-2016-0358Feo, T. A., J. F. Bard. (1989). Flight Scheduling and Maintenance Base Planning. Management Science, 35(12), Pages 1415-1432. https://doi.org/10.1287/mnsc.35.12.1415Goffin, J. L. (1977). On the convergence rates of subgradient optimization methods. Math. Programming, 13, Pages 329-347. https://doi.org/10.1007/BF01584346Gopalakrishnan, B., Johnson, E. L (2005). Airline crew scheduling, State-of-the-art. Ann. Oper. Res, 140(1), Pages 305-337. https://doi.org/10.1007/s10479-005-3975-3Held, M. and Karp, R.M. (1970). The Traveling-Salesman Problem and Minimum SpanningTrees. Operations Research, 18, 1138-1162. https://doi.org/10.1287/opre.18.6.1138Held, M. Wolfe, P., Crowder, H. D. (1974). Validation of subgradient optimization, Math. Programming, 6, 62-88. https://doi.org/10.1007/BF01580223Jamili, Amin (2017). 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). Solving a robust airline crew pairing problem with column generation, Computers & Operations Research, Volume 40, Issue 3, Pages 815-830. https://doi.org/10.1016/j.cor.2010.11.005Saddoune, Mohammed, Desaulniers, Guy, Elhallaoui, Issmail and François Soumis (2011). Integrated airline crew scheduling: A bi-dynamic constraint aggregation method using neighborhoods, European Journal of Operational Research, Volume 212, Pages 445-454. https://doi.org/10.1016/j.ejor.2011.02.009Safaei, Nima and K.S.Jardine, Andrew (2018). Aircraft routing with generalized maintenance constraints, Omega, Volume 80, Pages 111-122. https://doi.org/10.1016/j.omega.2017.08.013Shao Shengzhi (2012). 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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Applications using estimates of forest parameters derived from satellite and forest inventory data

From the combination of optical satellite data, digital map data, and forest inventory plot data, continuous estimates have been made for several forest parameters (wood volume, age and biomass). Five different project areas within Sweden are presented which have utilized these estimates for a range of applications. The method for estimating the forest parameters was a ”k-Nearest Neighbor” algorithm, which used a weighted mean value of k spectrally similar reference plots. Reference data were obtained from the Swedish National Forest Inventory. The output was continuous estimates at the pixel level for each of the variables estimated. Validation results show that accuracy of the estimates for all parameters was low at the pixel level (e.g., for total wood volume RMSE ranged from 58-80%), with a tendency toward the mean, and an underestimation of higher values while overestimating lower values. However, when the accuracy of the estimates is assessed over larger areas, the errors are lower, with best results being 10% RMSE over a 100 ha aggregation, and 17% RMSE over a 19 ha aggregation. Applications presented in this paper include moose and bird habitat studies, county level planning activities, use as input information to prognostic programs, and computation of statistics on timber volume within drainage basins and smaller land holdings. This paper provides a background on the kNN method and gives examples of how end users are currently applying satellite-produced estimation data such as these

Factors Affecting Teacher Readiness for Online Learning (TROL) in Early Childhood Education: TISE and TPACK

This study aims to find empirical information about the effect of Technological Pedagogical Content Knowledge (TPACK), and Technology Integration Self Efficacy (TISE) on Teacher Readiness for Online Learning (TROL). This study uses a quantitative survey method with path analysis techniques. This study measures the readiness of kindergarten teachers in distance learning in Tanah Datar Regency, West Sumatra Province, Indonesia with a sampling technique using simple random sampling involving 105 teachers. Empirical findings reveal that; 1) there is a direct positive effect of Technology Integration Self Efficacy on Teacher Readiness for Online Learning; 2) there is a direct positive effect of PACK on Teacher Readiness for Online Learning; 3) there is a direct positive effect of Technology Integration Self Efficacy on TPACK. If want to improve teacher readiness for online learning, Technological Pedagogical Content Knowledge (TPACK) must be improved by paying attention to Technology Integration Self Efficacy (TISE). Keywords: TROL, TPACK, TISE, Early Childhood Education References: Abbitt, J. T. (2011). An Investigation of the Relationship between Self-Efficacy Beliefs about Technology Integration and Technological Pedagogical Content Knowledge (TPACK) among Preservice Teachers. Journal of Digital Learning in Teacher Education, 27(4), 134–143. Adedoyin, O. B., & Soykan, E. (2020). Covid-19 pandemic and online learning: The challenges and opportunities. Interactive Learning Environments, 1–13. https://doi.org/10.1080/10494820.2020.1813180 Adnan, M. (2020). Online learning amid the COVID-19 pandemic: Students perspectives. Journal of Pedagogical Sociology and Psychology, 1(2), 45–51. https://doi.org/10.33902/JPSP.2020261309 Alqurashi, E. (2016). 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Technological Pedagogical Content Knowledge (TPACK) in Action. Journal of Research on Technology in Education, 43(3), 211–229. https://doi.org/10.1080/15391523.2011.10782570 Hatlevik, I. K. R., & Hatlevik, O. E. (2018). Examining the relationship between teachers’ ICT self-efficacy for educational purposes, collegial collaboration, lack of facilitation and the use of ICT in teaching practice. Frontiers in Psychology, 9(JUN), 1–8. https://doi.org/10.3389/fpsyg.2018.00935 Hung, M. L. (2016). Teacher readiness for online learning: Scale development and teacher perceptions. Computers and Education, 94, 120–133. https://doi.org/10.1016/j.compedu.2015.11.012 Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers and Education, 55(3), 1080–1090. https://doi.org/10.1016/j.compedu.2010.05.004 Juanda, A., Shidiq, A. S., & Nasrudin, D. (2021). 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Arbitrary-Lagrangian-Eulerian discontinuous Galerkin schemes with a posteriori subcell finite volume limiting on moving unstructured meshes

We present a new family of high order accurate fully discrete one-step Discontinuous Galerkin (DG) finite element schemes on moving unstructured meshes for the solution of nonlinear hyperbolic PDE in multiple space dimensions, which may also include parabolic terms in order to model dissipative transport processes. High order piecewise polynomials are adopted to represent the discrete solution at each time level and within each spatial control volume of the computational grid, while high order of accuracy in time is achieved by the ADER approach. In our algorithm the spatial mesh configuration can be defined in two different ways: either by an isoparametric approach that generates curved control volumes, or by a piecewise linear decomposition of each spatial control volume into simplex sub-elements. Our numerical method belongs to the category of direct Arbitrary-Lagrangian-Eulerian (ALE) schemes, where a space-time conservation formulation of the governing PDE system is considered and which already takes into account the new grid geometry directly during the computation of the numerical fluxes. Our new Lagrangian-type DG scheme adopts the novel a posteriori sub-cell finite volume limiter method, in which the validity of the candidate solution produced in each cell by an unlimited ADER-DG scheme is verified against a set of physical and numerical detection criteria. Those cells which do not satisfy all of the above criteria are flagged as troubled cells and are recomputed with a second order TVD finite volume scheme. The numerical convergence rates of the new ALE ADER-DG schemes are studied up to fourth order in space and time and several test problems are simulated. Finally, an application inspired by Inertial Confinement Fusion (ICF) type flows is considered by solving the Euler equations and the PDE of viscous and resistive magnetohydrodynamics (VRMHD).Comment: 39 pages, 21 figure