The Investigation of research-teaching model for undergraduate students

Nowadays, the research-teaching models of high education are highly developed in Chinese universities. However, many common problems are presented in these teaching processes, which are mainly three types of problems as bellows: 1. teaching evaluation mechanism; 2. creative teaching training for teachers; 3. teaching management model. The reasons of these problems are analyzed in this paper. According to several research-teaching methods three types of research-teaching models are applied in the course Measurement Technology, which are the combination of theory and practice, the design of opening experiments, and undergraduate students integration into researching topics. These research-teaching models are proved practically to be effective methods for improving creative and practical ability of undergraduate students

Research on Cloud Enterprise Resource Integration and Scheduling Technology Based on Mixed Set Programming

With the development of Industry 4.0 and intelligent manufacturing, aiming at the incompatibility of heterogeneous manufacturing resource interfaces and the low efficiency of collaborative scheduling of manufacturing resources among enterprises，we proposed the resource integration and scheduling strategy among enterprises based on Mixed Set Programming [1]. By using the metadata and ontology modeling methods, we were able to realize a standardized model description of manufacturing resources. At last, an enterprise application case was discussed to verify the resources integration and scheduling strategy based on Mixed Set Programming is effective to optimize and improve the efficiency of the collaborative scheduling of resources among enterprises. The resources integration and scheduling strategy based on Mixed Set Programming could be applied to promote the optimal allocation of manufacturing resources

Computational design process modeling

In the conceptual design phase, relatively simple equations and functions (or compiled code) are used to describe the aircraft and to perform trade-off studies. The latter require an optimal execution sequence in order to reduce computational cost and design time, respectively. The focus of this paper is the dynamic derivation of the optimal computational plan for each study so that the designer could focus on designing the aircraft rather than managing the process flow. Two methodologies, the Design Structure Matrix (DSM) and the Incidence Matrix are used for the computational process modeling. The incidence matrix describes the relationship between variables and equations/models. The DSM has been used to express the dependency relationships between the models and also, after manipulation, to produce the solution process. The designer specifies the independent (known) variables first. Then the variable flow is modeled using the Incidence Matrix Method (IMM). It determines how data flows through the models, and also identifies any strongly connected components (SCCs). The second step is to rearrange all equations/models hierarchically in order to reduce the feedback loops in each of the identified SCCs. This is achieved by the application of a genetic-based algorithm. Subsequently all SCCs and noncoupled models are assembled into a macro model which forms a global DSM. The global DSM is further rearranged to obtain an upper triangular matrix which defines the final model execution sequence. A simple aircraft sizing example is presented to illustrate the proposed method and algorithm. Advantages of the method include improved efficiency and the ability to deal with both algebraic and numerical models as well as with multiple outputs per model

Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism

[EN] With the development of the market globalisation trend and increasing customer orientation, many uncertainties have entered into the manufacturing context. To create an agile response to the emergence of and change in conditions, this article presents a dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. The dynamic re-scheduling function is the result of cooperation among several autonomous bio-inspired manufacturing cells with computing power and optimisation capabilities. The dynamic re-scheduling model is designed based on hormone regulation principles to agilely respond to the frequent occurrence of unexpected disturbances at the shop floor level. The cooperation mechanisms of the dynamic re-scheduling model are described in detail, and a test bed is set up to simulate and verify the dynamic re-scheduling approach. The results verify that the proposed method is able to improve the performances and enhance the stability of a manufacturing systemThis research was sponsored by the National Natural Science Foundation of China (NSFC) under Grant No. 51175262 and No. 61105114 and the Jiangsu Province Science Foundation for Excellent Youths under Grant BK20121011. This research was also sponsored by the CASES project supported by a Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme under grant agreement No. 294931Zheng, K.; Tang, D.; Giret Boggino, AS.; Gu, W.; Wu, X. (2015). Dynamic shop floor re-scheduling approach inspired by a neuroendocrine regulation mechanism. Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture. 229(S1):121-134. https://doi.org/10.1177/0954405414558699S121134229S1Maravelias, C. T., & Sung, C. (2009). Integration of production planning and scheduling: Overview, challenges and opportunities. Computers & Chemical Engineering, 33(12), 1919-1930. doi:10.1016/j.compchemeng.2009.06.007Yandra, & Tamura, H. (2007). A new multiobjective genetic algorithm with heterogeneous population for solving flowshop scheduling problems. International Journal of Computer Integrated Manufacturing, 20(5), 465-477. doi:10.1080/09511920601160288Fattahi, P., & Fallahi, A. (2010). Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability. CIRP Journal of Manufacturing Science and Technology, 2(2), 114-123. doi:10.1016/j.cirpj.2009.10.001Renna, P. (2011). Multi-agent based scheduling in manufacturing cells in a dynamic environment. International Journal of Production Research, 49(5), 1285-1301. doi:10.1080/00207543.2010.518736Qin, L., & Kan, S. (2013). Production Dynamic Scheduling Method Based on Improved Contract Net of Multi-agent. Advances in Intelligent Systems and Computing, 929-936. doi:10.1007/978-3-642-31656-2_128Iwamura, K., Mayumi, N., Tanimizu, Y., & Sugimura, N. (2010). A Study on Real-time Scheduling for Holonic Manufacturing Systems - Application of Reinforcement Learning -. Service Robotics and Mechatronics, 201-204. doi:10.1007/978-1-84882-694-6_35Jana, T. K., Bairagi, B., Paul, S., Sarkar, B., & Saha, J. (2013). Dynamic schedule execution in an agent based holonic manufacturing system. Journal of Manufacturing Systems, 32(4), 801-816. doi:10.1016/j.jmsy.2013.07.004Dan, Z., Cai, L., & Zheng, L. (2009). Improved multi-agent system for the vehicle routing problem with time windows. Tsinghua Science and Technology, 14(3), 407-412. doi:10.1016/s1007-0214(09)70058-6Hsieh, F.-S. (2009). Developing cooperation mechanism for multi-agent systems with Petri nets. Engineering Applications of Artificial Intelligence, 22(4-5), 616-627. doi:10.1016/j.engappai.2009.02.006Tang, D., Gu, W., Wang, L., & Zheng, K. (2011). A neuroendocrine-inspired approach for adaptive manufacturing system control. International Journal of Production Research, 49(5), 1255-1268. doi:10.1080/00207543.2010.518734Keenan, D. M., Licinio, J., & Veldhuis, J. D. (2001). A feedback-controlled ensemble model of the stress-responsive hypothalamo-pituitary-adrenal axis. Proceedings of the National Academy of Sciences, 98(7), 4028-4033. doi:10.1073/pnas.051624198Farhy, L. S. (2004). Modeling of Oscillations in Endocrine Networks with Feedback. Numerical Computer Methods, Part E, 54-81. doi:10.1016/s0076-6879(04)84005-9Cavalieri, S., Macchi, M., & Valckenaers, P. (2003). Journal of Intelligent Manufacturing, 14(1), 43-58. doi:10.1023/a:1022287212706Leitão, P., & Restivo, F. (2008). A holonic approach to dynamic manufacturing scheduling. Robotics and Computer-Integrated Manufacturing, 24(5), 625-634. doi:10.1016/j.rcim.2007.09.005Bal, M., & Hashemipour, M. (2009). Virtual factory approach for implementation of holonic control in industrial applications: A case study in die-casting industry. Robotics and Computer-Integrated Manufacturing, 25(3), 570-581. doi:10.1016/j.rcim.2008.03.020Leitao P. An agile and adaptive holonic architecture for manufacturing control. PhD Thesis, University of Porto, Porto, 2004

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). A decision-making methodology for low-carbon electronic product design. Decision Support Systems, 71, 1-13. doi:10.1016/j.dss.2015.01.004He, B., Tang, W., Wang, J., Huang, S., Deng, Z., & Wang, Y. (2015). Low-carbon conceptual design based on product life cycle assessment. The International Journal of Advanced Manufacturing Technology, 81(5-8), 863-874. doi:10.1007/s00170-015-7253-5(Roger) Jiao, J., Simpson, T. W., & Siddique, Z. (2007). Product family design and platform-based product development: a state-of-the-art review. Journal of Intelligent Manufacturing, 18(1), 5-29. doi:10.1007/s10845-007-0003-2Francalanza, E., Borg, J. C., & Constantinescu, C. L. (2012). A Case for Assisting ‘Product Family’ Manufacturing System Designers. Procedia CIRP, 3, 376-381. doi:10.1016/j.procir.2012.07.065Bryan, A., Wang, H., & Abell, J. (2013). Concurrent Design of Product Families and Reconfigurable Assembly Systems. Journal of Mechanical Design, 135(5). doi:10.1115/1.4023920Wang, Q., Tang, D., Yin, L., Salido, M. A., Giret, A., & Xu, Y. (2016). Bi-objective optimization for low-carbon product family design. Robotics and Computer-Integrated Manufacturing, 41, 53-65. doi:10.1016/j.rcim.2016.02.001Tang, D., Wang, Q., & Ullah, I. (2016). Optimisation of product configuration in consideration of customer satisfaction and low carbon. International Journal of Production Research, 55(12), 3349-3373. doi:10.1080/00207543.2016.1231430Kim, S., & Moon, S. K. (2017). Sustainable platform identification for product family design. Journal of Cleaner Production, 143, 567-581. doi:10.1016/j.jclepro.2016.12.073Xiao, W., Du, G., Zhang, Y., & Liu, X. (2018). Coordinated optimization of low-carbon product family and its manufacturing process design by a bilevel game-theoretic model. Journal of Cleaner Production, 184, 754-773. doi:10.1016/j.jclepro.2018.02.240Mangun, D., & Thurston, D. L. (2002). Incorporating component reuse, remanufacture, and recycle into product portfolio design. IEEE Transactions on Engineering Management, 49(4), 479-490. doi:10.1109/tem.2002.807292Debo, L. G., Toktay, L. B., & Wassenhove, L. N. V. (2009). Joint Life-Cycle Dynamics of New and Remanufactured Products. Production and Operations Management, 15(4), 498-513. doi:10.1111/j.1937-5956.2006.tb00159.xVorasayan, J., & Ryan, S. M. (2009). Optimal Price and Quantity of Refurbished Products. Production and Operations Management, 15(3), 369-383. doi:10.1111/j.1937-5956.2006.tb00251.xKwak, M., & Kim, H. M. (2011). Assessing product family design from an end-of-life perspective. Engineering Optimization, 43(3), 233-255. doi:10.1080/0305215x.2010.482990Kwak, M., & Kim, H. (2012). Market Positioning of Remanufactured Products With Optimal Planning for Part Upgrades. Journal of Mechanical Design, 135(1), 011007. doi:10.1115/1.4023000Debo, L. G., Toktay, L. B., & Van Wassenhove, L. N. (2005). Market Segmentation and Product Technology Selection for Remanufacturable Products. Management Science, 51(8), 1193-1205. doi:10.1287/mnsc.1050.0369Ferguson, M. E., & Toktay, L. B. (2009). The Effect of Competition on Recovery Strategies. Production and Operations Management, 15(3), 351-368. doi:10.1111/j.1937-5956.2006.tb00250.xJin, Y., Muriel, A., & Lu, Y. (2015). When to Offer Lower Quality or Remanufactured Versions of a Product. Decision Sciences, 47(4), 699-719. doi:10.1111/deci.12175Liu, B., Holmbom, M., Segerstedt, A., & Chen, W. (2014). Effects of carbon emission regulations on remanufacturing decisions with limited information of demand distribution. International Journal of Production Research, 53(2), 532-548. doi:10.1080/00207543.2014.957875Wang, Y., Chen, W., & Liu, B. (2017). Manufacturing/remanufacturing decisions for a capital-constrained manufacturer considering carbon emission cap and trade. Journal of Cleaner Production, 140, 1118-1128. doi:10.1016/j.jclepro.2016.10.058Yenipazarli, A. (2016). Managing new and remanufactured products to mitigate environmental damage under emissions regulation. European Journal of Operational Research, 249(1), 117-130. doi:10.1016/j.ejor.2015.08.020Perera, H. S. ., Nagarur, N., & Tabucanon, M. T. (1999). Component part standardization: A way to reduce the life-cycle costs of products. International Journal of Production Economics, 60-61, 109-116. doi:10.1016/s0925-5273(98)00179-0Jiao, J., & Zhang, Y. (2005). Product portfolio planning with customer-engineering interaction. IIE Transactions, 37(9), 801-814. doi:10.1080/07408170590917011Mukhopadhyay, S. K., & Ma, H. (2009). Joint procurement and production decisions in remanufacturing under quality and demand uncertainty. International Journal of Production Economics, 120(1), 5-17. doi:10.1016/j.ijpe.2008.07.032Wang, K., & Choi, S. H. (2014). A holonic approach to flexible flow shop scheduling under stochastic processing times. Computers & Operations Research, 43, 157-168. doi:10.1016/j.cor.2013.09.01