Aerodynamic Noise Prediction for a Rod-Airfoil Configuration using Large Eddy Simulations

Aerodynamic noise produced by aerodynamic interaction between a cylinder (rod) and an airfoil in tandem arrangement is investigated using large eddy simulations. Wake from the rod convects with the flow, impinges of the airfoil to produce unsteady force which acts as a sound source. This rod-airfoil interaction problem is a model problem for noise generation due to inflow or upstream-generated turbulence interacting with a turbomachine bladerow or a wind turbine rotor. The OpenFoam and Charles (developed by Cascade Technologies) solvers are chosen to carry out the numerical simulations. The airfoil is set at zero angle of attack for the simulations. The flow conditions are specified by the Reynolds number (based on the rod diameter), Red = 48 K, and the flow Mach number, M = 0.2. Comparisons with measured data are made for (a) mean and root-mean-squared velocity profiles in the rod and airfoil wakes, (b) velocity spectra in the near field, and (c) far-field pressure spectra and directivity. Near-field flow data (on- and off-surface) is used with the Ffowcs Williams-Hawkings (FW-H) acoustic analogy as well as Amiet’s theory to predict far-field sound

An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints

[EN] Nowadays, the manufacturing industry faces the challenge of reducing energy consumption and the associated environmental impacts. Production scheduling is an effective approach for energy-savings management. During the entire workshop production process, both the processing and transportation operations consume large amounts of energy. To reduce energy consumption, an energy-efficient job-shop scheduling problem (EJSP) with transportation constraints was proposed in this paper. First, a mixed-integer programming model was established to minimize both the comprehensive energy consumption and makespan in the EJSP. Then, an enhanced estimation of distribution algorithm (EEDA) was developed to solve the problem. In the proposed algorithm, an estimation of distribution algorithm was employed to perform the global search and an improved simulated annealing algorithm was designed to perform the local search. Finally, numerical experiments were implemented to analyze the performance of the EEDA. The results showed that the EEDA is a promising approach and that it can solve EJSP effectively and efficiently.This work was supported by the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJB460018), the Innovation Foundation for Science and Technology of Yangzhou University (No. 2016CXJ020 and No. 2017CXJ018), Science and Technology Project of Yangzhou under (No. YZ2017278), Research Topics of Teaching Reform of Yangzhou University under (No. YZUJX2018-28B), and the Spanish Government (No. TIN2016-80856-R and No. TIN2015-65515-C4-1-R).Dai, M.; Zhang, Z.; Giret Boggino, AS.; Salido, MA. (2019). An Enhanced Estimation of Distribution Algorithm for Energy-Efficient Job-Shop Scheduling Problems with Transportation Constraints. Sustainability. 11(11):1-23. https://doi.org/10.3390/su11113085S1231111Wu, X., & Sun, Y. (2018). A green scheduling algorithm for flexible job shop with energy-saving measures. Journal of Cleaner Production, 172, 3249-3264. doi:10.1016/j.jclepro.2017.10.342Wang, Q., Tang, D., Li, S., Yang, J., Salido, M., Giret, A., & Zhu, H. (2019). An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products. Sustainability, 11(2), 460. doi:10.3390/su11020460Meng, Y., Yang, Y., Chung, H., Lee, P.-H., & Shao, C. (2018). Enhancing Sustainability and Energy Efficiency in Smart Factories: A Review. Sustainability, 10(12), 4779. doi:10.3390/su10124779Gahm, C., Denz, F., Dirr, M., & Tuma, A. (2016). Energy-efficient scheduling in manufacturing companies: A review and research framework. European Journal of Operational Research, 248(3), 744-757. doi:10.1016/j.ejor.2015.07.017Giret, A., Trentesaux, D., & Prabhu, V. (2015). Sustainability in manufacturing operations scheduling: A state of the art review. Journal of Manufacturing Systems, 37, 126-140. doi:10.1016/j.jmsy.2015.08.002Akbar, M., & Irohara, T. (2018). Scheduling for sustainable manufacturing: A review. Journal of Cleaner Production, 205, 866-883. doi:10.1016/j.jclepro.2018.09.100Che, A., Wu, X., Peng, J., & Yan, P. (2017). Energy-efficient bi-objective single-machine scheduling with power-down mechanism. Computers & Operations Research, 85, 172-183. doi:10.1016/j.cor.2017.04.004Lee, S., Do Chung, B., Jeon, H. W., & Chang, J. (2017). A dynamic control approach for energy-efficient production scheduling on a single machine under time-varying electricity pricing. Journal of Cleaner Production, 165, 552-563. doi:10.1016/j.jclepro.2017.07.102Rubaiee, S., & Yildirim, M. B. (2019). An energy-aware multiobjective ant colony algorithm to minimize total completion time and energy cost on a single-machine preemptive scheduling. Computers & Industrial Engineering, 127, 240-252. doi:10.1016/j.cie.2018.12.020Zhang, M., Yan, J., Zhang, Y., & Yan, S. (2019). Optimization for energy-efficient flexible flow shop scheduling under time of use electricity tariffs. Procedia CIRP, 80, 251-256. doi:10.1016/j.procir.2019.01.062Li, J., Sang, H., Han, Y., Wang, C., & Gao, K. (2018). Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. Journal of Cleaner Production, 181, 584-598. doi:10.1016/j.jclepro.2018.02.004Lu, C., Gao, L., Li, X., Pan, Q., & Wang, Q. (2017). Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm. Journal of Cleaner Production, 144, 228-238. doi:10.1016/j.jclepro.2017.01.011Fu, Y., Tian, G., Fathollahi-Fard, A. M., Ahmadi, A., & Zhang, C. (2019). Stochastic multi-objective modelling and optimization of an energy-conscious distributed permutation flow shop scheduling problem with the total tardiness constraint. Journal of Cleaner Production, 226, 515-525. doi:10.1016/j.jclepro.2019.04.046Schulz, S., Neufeld, J. S., & Buscher, U. (2019). A multi-objective iterated local search algorithm for comprehensive energy-aware hybrid flow shop scheduling. Journal of Cleaner Production, 224, 421-434. doi:10.1016/j.jclepro.2019.03.155Liu, Y., Dong, H., Lohse, N., Petrovic, S., & Gindy, N. (2014). An investigation into minimising total energy consumption and total weighted tardiness in job shops. Journal of Cleaner Production, 65, 87-96. doi:10.1016/j.jclepro.2013.07.060Liu, Y., Dong, H., Lohse, N., & Petrovic, S. (2016). A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance. International Journal of Production Economics, 179, 259-272. doi:10.1016/j.ijpe.2016.06.019May, G., Stahl, B., Taisch, M., & Prabhu, V. (2015). Multi-objective genetic algorithm for energy-efficient job shop scheduling. International Journal of Production Research, 53(23), 7071-7089. doi:10.1080/00207543.2015.1005248Zhang, R., & Chiong, R. (2016). Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption. Journal of Cleaner Production, 112, 3361-3375. doi:10.1016/j.jclepro.2015.09.097Salido, M. A., Escamilla, J., Giret, A., & Barber, F. (2015). A genetic algorithm for energy-efficiency in job-shop scheduling. The International Journal of Advanced Manufacturing Technology, 85(5-8), 1303-1314. doi:10.1007/s00170-015-7987-0Masmoudi, O., Delorme, X., & Gianessi, P. (2019). Job-shop scheduling problem with energy consideration. International Journal of Production Economics, 216, 12-22. doi:10.1016/j.ijpe.2019.03.021Mokhtari, H., & Hasani, A. (2017). An energy-efficient multi-objective optimization for flexible job-shop scheduling problem. Computers & Chemical Engineering, 104, 339-352. doi:10.1016/j.compchemeng.2017.05.004Meng, L., Zhang, C., Shao, X., & Ren, Y. (2019). MILP models for energy-aware flexible job shop scheduling problem. Journal of Cleaner Production, 210, 710-723. doi:10.1016/j.jclepro.2018.11.021Dai, M., Tang, D., Giret, A., & Salido, M. A. (2019). Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints. Robotics and Computer-Integrated Manufacturing, 59, 143-157. doi:10.1016/j.rcim.2019.04.006Lacomme, P., Larabi, M., & Tchernev, N. (2013). Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles. International Journal of Production Economics, 143(1), 24-34. doi:10.1016/j.ijpe.2010.07.012Nageswararao, M., Narayanarao, K., & Ranagajanardhana, G. (2014). Simultaneous Scheduling of Machines and AGVs in Flexible Manufacturing System with Minimization of Tardiness Criterion. Procedia Materials Science, 5, 1492-1501. doi:10.1016/j.mspro.2014.07.336Saidi-Mehrabad, M., Dehnavi-Arani, S., Evazabadian, F., & Mahmoodian, V. (2015). An Ant Colony Algorithm (ACA) for solving the new integrated model of job shop scheduling and conflict-free routing of AGVs. Computers & Industrial Engineering, 86, 2-13. doi:10.1016/j.cie.2015.01.003Guo, Z., Zhang, D., Leung, S. Y. S., & Shi, L. (2016). A bi-level evolutionary optimization approach for integrated production and transportation scheduling. Applied Soft Computing, 42, 215-228. doi:10.1016/j.asoc.2016.01.052Karimi, S., Ardalan, Z., Naderi, B., & Mohammadi, M. (2017). Scheduling flexible job-shops with transportation times: Mathematical models and a hybrid imperialist competitive algorithm. Applied Mathematical Modelling, 41, 667-682. doi:10.1016/j.apm.2016.09.022Liu, Z., Guo, S., & Wang, L. (2019). Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption. Journal of Cleaner Production, 211, 765-786. doi:10.1016/j.jclepro.2018.11.231Tang, D., & Dai, M. (2015). Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem. Chinese Journal of Mechanical Engineering, 28(5), 1048-1055. doi:10.3901/cjme.2015.0617.082Hao, X., Lin, L., Gen, M., & Ohno, K. (2013). Effective Estimation of Distribution Algorithm for Stochastic Job Shop Scheduling Problem. Procedia Computer Science, 20, 102-107. doi:10.1016/j.procs.2013.09.246Wang, L., Wang, S., Xu, Y., Zhou, G., & Liu, M. (2012). A bi-population based estimation of distribution algorithm for the flexible job-shop scheduling problem. Computers & Industrial Engineering, 62(4), 917-926. doi:10.1016/j.cie.2011.12.014Jarboui, B., Eddaly, M., & Siarry, P. (2009). An estimation of distribution algorithm for minimizing the total flowtime in permutation flowshop scheduling problems. Computers & Operations Research, 36(9), 2638-2646. doi:10.1016/j.cor.2008.11.004Hauschild, M., & Pelikan, M. (2011). An introduction and survey of estimation of distribution algorithms. Swarm and Evolutionary Computation, 1(3), 111-128. doi:10.1016/j.swevo.2011.08.003Liu, F., Xie, J., & Liu, S. (2015). A method for predicting the energy consumption of the main driving system of a machine tool in a machining process. Journal of Cleaner Production, 105, 171-177. doi:10.1016/j.jclepro.2014.09.058Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418-429. doi:10.1016/j.rcim.2013.04.001Beasley, J. E. (1990). OR-Library: Distributing Test Problems by Electronic Mail. Journal of the Operational Research Society, 41(11), 1069-1072. doi:10.1057/jors.1990.166Zhao, F., Shao, Z., Wang, J., & Zhang, C. (2015). A hybrid differential evolution and estimation of distribution algorithm based on neighbourhood search for job shop scheduling problems. International Journal of Production Research, 54(4), 1039-1060. doi:10.1080/00207543.2015.1041575Van Laarhoven, P. J. M., Aarts, E. H. L., & Lenstra, J. K. (1992). Job Shop Scheduling by Simulated Annealing. Operations Research, 40(1), 113-125. doi:10.1287/opre.40.1.113Wang, L., & Zheng, D.-Z. (2001). An effective hybrid optimization strategy for job-shop scheduling problems. 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A hormone regulation based approach for distributed and on-line scheduling of machines and automated guided vehicles

[EN] With the continuous innovation of technology, automated guided vehicles are playing an increasingly important role on manufacturing systems. Both the scheduling of operations on machines as well as the scheduling of automated guided vehicles are essential factors contributing to the efficiency of the overall manufacturing systems. In this article, a hormone regulation¿based approach for on-line scheduling of machines and automated guided vehicles within a distributed system is proposed. In a real-time environment, the proposed approach assigns emergent tasks and generates feasible schedules implementing a task allocation approach based on hormonal regulation mechanism. This approach is tested on two scheduling problems in literatures. The results from the evaluation show that the proposed approach improves the scheduling quality compared with state-of-the-art on-line and off-line approaches.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was sponsored by the National Natural Science Foundation of China (NSFC) under grant nos 51175262 and 51575264 and the Jiangsu Province Science Foundation for Excellent Youths under grant no. BK2012032. This research was also sponsored by the CASES project which was supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under grant agreement no. 294931.Zheng, K.; Tang, D.; Giret Boggino, AS.; Salido, MA.; Sang, Z. (2016). A hormone regulation based approach for distributed and on-line scheduling of machines and automated guided vehicles. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 232(1):99-113. https://doi.org/10.1177/0954405416662078S99113232

Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems

[EN] Many real-world problems are known as planning and scheduling problems, where resources must be allocated so as to optimize overall performance objectives. The traditional scheduling models consider performance indicators such as processing time, cost, and quality as optimization objectives. However, most of them do not take into account energy consumption and robustness. We focus our attention in a job-shop scheduling problem where machines can work at different speeds. It represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The main goal of the paper is focused on the analysis of three important objectives (energy efficiency, robustness, and makespan) and the relationship among them. We present some analytical formulas to estimate the ratio/relationship between these parameters. It can be observed that there exists a clear relationship between robustness and energy efficiency and a clear trade-off between robustness/energy efficiency and makespan. It represents an advance in the state of the art of production scheduling, so obtaining energy-efficient solutions also supposes obtaining robust solutions, and vice versa.This research has been supported by the Spanish Government under research project MICINN TIN2013-46511-C2-1-P, the European CASES project (No. 294931) supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the FP7, and the European TETRACOM project (No. 609491) supported by FP7-ICT-2013-10. This research was also supported by the National Science Foundation of China (No. 51175262) and the Jiangsu Province Science Foundation for Excellent Youths under Grant BK2012032.Salido Gregorio, MA.; Escamilla Fuster, J.; Barber Sanchís, F.; Giret Boggino, AS.; Tang, D.; Dai, M. (2015). Energy efficiency, robustness, and makespan optimality in job-shop scheduling problems. AI EDAM. 30(3):300-312. https://doi.org/10.1017/S0890060415000335S300312303Billaut, J.-C., Moukrim, A., & Sanlaville, E. (Eds.). (2008). Flexibility and Robustness in Scheduling. doi:10.1002/9780470611432Nowicki, E., & Smutnicki, C. (2005). An Advanced Tabu Search Algorithm for the Job Shop Problem. Journal of Scheduling, 8(2), 145-159. doi:10.1007/s10951-005-6364-5Agnetis, A., Flamini, M., Nicosia, G., & Pacifici, A. (2010). A job-shop problem with one additional resource type. Journal of Scheduling, 14(3), 225-237. doi:10.1007/s10951-010-0162-4Mouzon, G., Yildirim, M. B., & Twomey, J. (2007). Operational methods for minimization of energy consumption of manufacturing equipment. International Journal of Production Research, 45(18-19), 4247-4271. doi:10.1080/00207540701450013Weinert, N., Chiotellis, S., & Seliger, G. (2011). Methodology for planning and operating energy-efficient production systems. CIRP Annals, 60(1), 41-44. doi:10.1016/j.cirp.2011.03.015Duflou, J. R., Sutherland, J. W., Dornfeld, D., Herrmann, C., Jeswiet, J., Kara, S., … Kellens, K. (2012). Towards energy and resource efficient manufacturing: A processes and systems approach. CIRP Annals, 61(2), 587-609. doi:10.1016/j.cirp.2012.05.002Laborie P. (2009). IBM ILOG CP Optimizer for detailed scheduling illustrated on three problems. Proc. 6th Int. Conf. Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems, CPAIOR09.Dahmus J. , & Gutowski T. (2004). An environmental analysis of machining. Proc. ASME Int. Mechanical Engineering Congr. RD&D Exposition, Anaheim, CA.Huang, K.-L., & Liao, C.-J. (2008). Ant colony optimization combined with taboo search for the job shop scheduling problem. Computers & Operations Research, 35(4), 1030-1046. doi:10.1016/j.cor.2006.07.003IBM. (2010). Modeling With IBM ILOG CP Optimizer—Practical Scheduling Examples (white paper). Armonk, NY: IBM Software Group.Kramer L. , Barbulescu L. , & Smith S. (2007). Understanding performance tradeoffs in algorithms for solving oversubscribed scheduling. Proc. 22nd Conf. Artificial Intelligence, AAAI-07, Vancouver.Seow, Y., & Rahimifard, S. (2011). A framework for modelling energy consumption within manufacturing systems. CIRP Journal of Manufacturing Science and Technology, 4(3), 258-264. doi:10.1016/j.cirpj.2011.03.007Li, W., Zein, A., Kara, S., & Herrmann, C. (2011). An Investigation into Fixed Energy Consumption of Machine Tools. Glocalized Solutions for Sustainability in Manufacturing, 268-273. doi:10.1007/978-3-642-19692-8_47Szathmáry, E. (2006). A robust approach. Nature, 439(7072), 19-20. doi:10.1038/439019aFang, K., Uhan, N., Zhao, F., & Sutherland, J. W. (2011). A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction. Journal of Manufacturing Systems, 30(4), 234-240. doi:10.1016/j.jmsy.2011.08.004Gutowski, T., Murphy, C., Allen, D., Bauer, D., Bras, B., Piwonka, T., … Wolff, E. (2005). Environmentally benign manufacturing: Observations from Japan, Europe and the United States. Journal of Cleaner Production, 13(1), 1-17. doi:10.1016/j.jclepro.2003.10.004Garrido A. , Salido M.A. , Barber F. , & López M.A. (2000). Heuristic methods for solving job-shop scheduling problems. Proc. ECAI-2000 Workshop on New Results in Planning, Scheduling and Design, Berlín.Verfaillie G. , & Schiex T. (1994). Solution reuse in dynamic constraint satisfaction problems. Proc. 12th National Conf. Artificial Intelligence, AAAI-94.Dai, M., Tang, D., Giret, A., Salido, M. A., & Li, W. D. (2013). Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm. Robotics and Computer-Integrated Manufacturing, 29(5), 418-429. doi:10.1016/j.rcim.2013.04.001Neugebauer, R., Wabner, M., Rentzsch, H., & Ihlenfeldt, S. (2011). Structure principles of energy efficient machine tools. CIRP Journal of Manufacturing Science and Technology, 4(2), 136-147. doi:10.1016/j.cirpj.2011.06.017Mouzon, G., & Yildirim, M. B. (2008). A framework to minimise total energy consumption and total tardiness on a single machine. International Journal of Sustainable Engineering, 1(2), 105-116. doi:10.1080/19397030802257236Bruzzone, A. A. G., Anghinolfi, D., Paolucci, M., & Tonelli, F. (2012). Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops. CIRP Annals, 61(1), 459-462. doi:10.1016/j.cirp.2012.03.08

Bi-objective optimization for low-carbon product family design

[EN] Consumers, industry, and government entities are becoming increasingly concerned about the issue of global warming. With this in mind, manufacturers have begun to develop products with consideration of low-carbon. In recent years, many companies are utilizing product families to satisfy various customer needs with lower costs. However, little research has been conducted on the development of a product family that considers environmental factors. In this paper, a low-carbon product family design that integrates environmental concerns is proposed. To this end, a new method of platform planning is investigated with considerations of cost and greenhouse gas (GHG) emission of a product family simultaneously. In this research, a lowcarbon product family design problem is described at first, and then a GHG emission model of product family is established. Furthermore, to support lowcarbon product family design, an optimization method is applied to make a significant trade-off between cost and GHG emission to implement a feasible platform planning. Finally, the effectiveness of the proposed method is illustrated through a case study. (C) 2016 Elsevier Ltd. All rights reserved.This research was carried out as a part of the CASES project which is supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under the Grant agreement no. 294931. This research was also supported by National Natural Science Foundation of China (Nos. 51175262, 51575264); and Jiangsu Province Science Foundation for Excellent Youths under Grant BK2012032.Wang, Q.; Dunbing, T.; Yin, L.; Salido, MA.; Giret Boggino, AS.; Xu, Y. (2016). Bi-objective optimization for low-carbon product family design. Robotics and Computer-Integrated Manufacturing. 41:53-65. https://doi.org/10.1016/j.rcim.2016.02.001S53654

An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products

[EN] With increasingly stringent environmental regulations on emission standards, enterprises and investigators are looking for effective ways to decrease GHG emission from products. As an important method for reducing GHG emission of products, low-carbon product family design has attracted more and more attention. Existing research, related to low-carbon product family design, did not take into account remanufactured products. Nowadays, it is popular to launch remanufactured products for environmental benefit and meeting customer needs. On the one hand, the design of remanufactured products is influenced by product family design. On the other hand, the launch of remanufactured products may cannibalize the sale of new products. Thus, the design of remanufactured products should be considered together with the product family design for obtaining the maximum profit and reducing the GHG emission as soon as possible. The purpose of this paper is to present an optimization model to concurrently determine product family design, remanufactured products planning and remanufacturing parameters selection with consideration of the customer preference, the total profit of a company and the total GHG emission from production. A genetic algorithm is applied to solve the optimization problem. The proposed method can help decision-makers to simultaneously determine the design of a product family and remanufactured products with a better trade-off between profit and environmental impact. Finally, a case study is performed to demonstrate the effectiveness of the presented approach.This research was funded by National Natural Science Foundation of China (grant number 51575264 and 51805253); the Fundamental Research Funds for the Central Universities (grant number NP2017105); Jiangsu Planned Projects for Postdoctoral Research Funds (grant number 2018K017C); and the Qin Lan Project.Wang, Q.; Tang, D.; Li, S.; Yang, J.; Salido, MA.; Giret Boggino, AS.; Zhu, H. (2019). An Optimization Approach for the Coordinated Low-Carbon Design of Product Family and Remanufactured Products. Sustainability. 11(2):1-22. https://doi.org/10.3390/su11020460S122112Mascle, C., & Zhao, H. P. (2008). Integrating environmental consciousness in product/process development based on life-cycle thinking. International Journal of Production Economics, 112(1), 5-17. doi:10.1016/j.ijpe.2006.08.016Kengpol, A., & Boonkanit, P. (2011). The decision support framework for developing Ecodesign at conceptual phase based upon ISO/TR 14062. International Journal of Production Economics, 131(1), 4-14. doi:10.1016/j.ijpe.2010.10.006Ferrer, G., & Swaminathan, J. M. (2010). Managing new and differentiated remanufactured products. European Journal of Operational Research, 203(2), 370-379. doi:10.1016/j.ejor.2009.08.007Sutherland, J. W., Adler, D. P., Haapala, K. R., & Kumar, V. (2008). A comparison of manufacturing and remanufacturing energy intensities with application to diesel engine production. CIRP Annals, 57(1), 5-8. doi:10.1016/j.cirp.2008.03.004Song, J.-S., & Lee, K.-M. (2010). Development of a low-carbon product design system based on embedded GHG emissions. Resources, Conservation and Recycling, 54(9), 547-556. doi:10.1016/j.resconrec.2009.10.012Qi, Y., & Wu, X. (2011). Low-carbon Technologies Integrated Innovation Strategy Based on Modular Design. Energy Procedia, 5, 2509-2515. doi:10.1016/j.egypro.2011.03.431Su, J. C. P., Chu, C.-H., & Wang, Y.-T. (2012). A decision support system to estimate the carbon emission and cost of product designs. International Journal of Precision Engineering and Manufacturing, 13(7), 1037-1045. doi:10.1007/s12541-012-0135-yKuo, T. C., Chen, H. M., Liu, C. Y., Tu, J.-C., & Yeh, T.-C. (2014). Applying multi-objective planning in low-carbon product design. International Journal of Precision Engineering and Manufacturing, 15(2), 241-249. doi:10.1007/s12541-014-0331-zXu, Z.-Z., Wang, Y.-S., Teng, Z.-R., Zhong, C.-Q., & Teng, H.-F. (2015). Low-carbon product multi-objective optimization design for meeting requirements of enterprise, user and government. Journal of Cleaner Production, 103, 747-758. doi:10.1016/j.jclepro.2014.07.067He, B., Wang, J., Huang, S., & Wang, Y. (2015). Low-carbon product design for product life cycle. Journal of Engineering Design, 26(10-12), 321-339. doi:10.1080/09544828.2015.1053437Chiang, T.-A., & Che, Z. H. (2015). 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Ultrafast electronic and lattice dynamics in laser-excited crystalline bismuth

Femtosecond spectroscopy is applied to study transient electronic and lattice processes in bismuth. Components with relaxation times of 1 ps, 7 ps and ~ 1 ns are detected in the photoinduced reflectivity response of the crystal. To facilitate the assignment of the observed relaxation to the decay of particular excited electronic states we use pump pulses with central wavelengths ranging from 400 nm to 2.3 mum. Additionally, we examine the variation of parameters of coherent A1g phonons upon the change of excitation and probing conditions. Data analysis reveals a significant wavevector dependence of electron-hole and electron- phonon coupling strength along \Gamma--T direction of the Brillouin zone.Comment: 19 pages, 9 figure

Entropy Driven Atomic Motion in Laser-Excited Bismuth

We introduce a thermodynamical model based on the two-temperature approach in order to fully understand the dynamics of the coherent A(1g) phonon in laser-excited bismuth. Using this model, we simulate the time evolution of (111) Bragg peak intensities measured by Fritz et al. [Science 315, 633 (2007)] in femtosecond x-ray diffraction experiments performed on a bismuth film for different laser fluences. The agreement between theoretical and experimental results is striking not only because we use fluences very close to the experimental ones but also because most of the model parameters are obtained from ab initio calculations performed for different electron temperatures

Smart and sustainable urban logistic applications aided by intelligent techniques

[EN] CO2-free urban logistics is one of the 10 objectives to reach by 2030 as part of transport policy. What technologies can help to accomplish it? In this paper, we discuss the very complex situation that today¿s big and modern cities are facing with a tremendous environment of many urban logistics companies running in the same city. In the majority of cases, there is less or none coordination among them worsening traffic congestions. We believe that intelligent techniques are one of the key approaches that can aid to support smart and sustainable urban logistic applications. There are large open problems in the field of cooperative urban logistics that can greatly improve with the help of artificial intelligence. Some solutions are cited in this paper, but the overall conclusion is that there is still much work to be done.Giret Boggino, AS. (2019). Smart and sustainable urban logistic applications aided by intelligent techniques. Service Oriented Computing and Applications (Online). 13(3):185-186. https://doi.org/10.1007/s11761-019-00271-zS185186133Market reports (2019) Global last mile delivery market size, status and forecast 2019–2025. The Market reports. Report code : 1362721, pp 1–114Xiao Z, Wang JJ, Lenzer J, Sun Y (2017) Understanding the diversity of final delivery solutions for online retailing: a case of Shenzhen, China. In: World conference on transport research—WCTR 2016 Shanghai. Transportation Research Procedia, vol 25, pp 985–998, 2017. 10–15 July 2016Gonzalez-Feliu J, Semet F, Routhier JL (2014) Sustainable urban logistics: concepts, methods and information systems. Springer, BerlinMacharis C, Melo S (2011) City distribution and urban freight transport: multiple perspectives. Edward Elgar Publishing, CheltenhamPagell M, Wu Z (2009) Building a more complete theory of sustainable supply chain management using case studies of 10 exemplars. J Supply Chain Manag 45:37–56Morana J, Gonzalez-Feliu J (2015) A sustainable urban logistics dashboard from the perspective of a group of operational managers. Manag Res Rev 38(10):1068–1085Gunasekaran A, Kobu B (2007) Performance measures and metrics in logistics and supply chain management: a review of recent literature (1995–2004) for research and applications. Int J Prod Res 45:2819–2840Griffis SE, Goldsby TJ, Cooper M, Closs DJ (2007) Aligning logistics performance measures to the information needs of the firm. J Bus Logist 48:35–56Alonso-Mora J, Samaranayake S, Wallar A, Frazzoli E, Rus D (2017) On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment. Proc Natl Acad Sci 114(3):462–467Gentile G, Noekel K (2016) Modeling public transport passenger flows in the era of intelligent transport systems. Springer, BerlinNeirotti P, De Marco A, Cagliano AC, Mangano G, Scorrano F (2014) Current trends in smart city initiatives: some stylised facts. Cities 38:25–36Chatterjee R (2016) Optimizing last mile delivery using public transport with multiagent based control. Master thesis, pp 1–59Skiver RL, Godfrey M (2017) Crowdserving: a last mile delivery method for brickand—mortar retailers. Glob J Bus Res 11(2):67–77Brüning M, Schönewolf W (2011) Freight transport system for urban shipment and delivery. In: IEEE forum on integrated and sustainable transportation systems, Vienna, pp 136–14