Fuzzy Clustering Approach for Marketing Recycled Products of Tabriz Municipality Waste Management Organization

The main concern of municipalities is the realization of sustainable revenues. Organizations affiliated with municipalities should play a role in generating revenue by defining specialized tasks while assisting municipal tasks. Tabriz Municipality Waste Management Organization seeks to achieve this by defining its strategies and goals. The organization has implemented various projects to generate revenue from recycled products. Poor planning and failure to fully outsource are among the obstacles of this organization. Therefore, marketing of recycled products is an important project. Lack of careful planning in this regard, marketing costs and weakness of private sector investment projects are the most important obstacles facing the organization. This article has determined the degree of homogeneity of waste organization projects in the marketing of recycled products with a fuzzy clustering approach and according to the opinions of experts. The results show that some of the organization's projects lack value. Instead, some projects, such as the construction of a recycling town with a variety of recycled products, renewable energy recycling, and plastic recycling with a variety of products, have similar features in the product mix marketing element, and this can reduce marketing costs and Focus on such projects

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

An Optimization Method for Coordinating Supplier Selection and Low-Carbon Design of Product Family

[EN] New stricter environmental regulations and consumer rising issues are making greenhouse gases (GHG) emission an increasing and urgent concern for manufacturing companies. Companies and researchers are seeking appropriate methods to reduce GHG emission of the manufactured products. Previous studies on low-carbon product design mainly concern on a single product. Currently, it is common to design a product family instead of a single product for increasing varieties to satisfy customers' requirements. Owing to the difference in design methods, the low-carbon design method for a single product cannot handle a product family. In addition, nowadays, the sourcing strategy is widely adopted by companies. A key problem of the procurement is supplier selection. The supplier selection affects not only profit but also GHG emission. However, it has not been simultaneously considered in low-carbon product design. In this article, an optimization model for coordinating low-carbon design of product family and supplier selection is proposed. In the model, the profit and the GHG emission of a product family are taken into consideration at the same time. Moreover, a genetic algorithm is developed to solve the established model. Finally, a case study is performed to verify the validity of the proposed approach.This work was supported by National Natural Science Foundation of China [Grant number 51575264]; the Fundamental Research Funds for the Central Universities [Grant number 56XBA17006]; and Qin Lan Project.Wang, Q.; Tang, D.; Yin, L.; Ullah, I.; Salido, MA.; Giret Boggino, AS. (2018). An Optimization Method for Coordinating Supplier Selection and Low-Carbon Design of Product Family. International Journal of Precision Engineering and Manufacturing. 19(11):1715-1726. https://doi.org/10.1007/s12541-018-0199-4S171517261911Intergovernmental Panel on Climate Change (IPCC). “IPCC Fourth Assessment Report: Climate Change 2007,” Working Group III Report “Mitigation of Climate Change”, pp. 447–496, 2007.He, B., Wang, J., and Deng, Z., “Cost-Constrained Low-Carbon Product Design,” The International Journal of Advanced Manufacturing Technology, Vol. 79, Nos. 9–12, pp. 1821–1828, 2015.Xu, Z.-Z., Wang, Y.-S., Teng, Z.-R., Zhong, C.-Q., and Teng, H.-F., “Low-Carbon Product Multi-Objective Optimization Design for Meeting Requirements of Enterprise, User and Government,” Journal of Cleaner Production, Vol. 103, pp. 747–758, 2015.Chiang, T.-A. and Che, Z., “A Decision-Making Methodology for Low-Carbon Electronic Product Design,” Decision Support Systems, Vol. 71, pp. 1–13, 2015.He, B., Deng, Z., Huang, S., and Wang, J., “Application of Unascertained Number for the Integration of Carbon Footprint in Conceptual Design,” Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, Vol. 229, No. 11, pp. 2088–2092, 2015.Kuo, T. C., “The Construction of a Collaborative Framework in Support of Low Carbon Product Design,” Robotics and Computer-Integrated Manufacturing, Vol. 29, No. 4, pp. 174–183, 2013.Su, J. C., Chu, C.-H., and Wang, Y.-T., “A Decision Support System to Estimate the Carbon Emission and Cost of Product Designs,” International Journal of Precision Engineering and Manufacturing, Vol. 13, No. 7, pp. 1037–1045, 2012.Song, J.-S. and Lee, K.-M., “Development of a Low-Carbon Product Design System Based on Embedded GHG Emissions,” Resources, Conservation and Recycling, Vol. 54, No. 9, pp. 547–556, 2010.Fujita, K., Sakaguchi, H., and Akagi, S., “Product Variety Deployment and Its Optimization under Modular Architecture and Module Commonalization,” Proc. of the 1999 ASME Design Engineering Technical Conferences, pp. 12–15, 1999.Wang, W., Qin, X., Yan, X., Tong, S., and Sha, Q., “Developing a Systematic Method for Constructing the Function Platform of Product Family,” Proc. of IEEE International Conference on Industrial Engineering and Engineering Management, pp. 60–64, 2007.Kumar, D., Chen, W., and Simpson, T. W., “A Market-Driven Approach to Product Family Design,” International Journal of Production Research, Vol. 47, No. 1, pp. 71–104, 2009.Kwong, C. K., Luo, X., and Tang, J., “A Multiobjective Optimization Approach for Product Line Design,” IEEE Transactions on Engineering Management, Vol. 58, No. 1, pp. 97–108, 2011.Gupta, S. and Krishnan, V., “Integrated Component and Supplier Selection for a Product Family,” Production and Operations Management, Vol. 8, No. 2, pp. 163–182, 1999.Lamothe, J., Hadj-Hamou, K., and Aldanondo, M., “An Optimization Model for Selecting a Product Family and Designing Its Supply Chain,” European Journal of Operational Research, Vol. 169, No. 3, pp. 1030–1047, 2006.Balakrishnan, N. R. and Chakravarty, A. K., “Product Design with Multiple Suppliers for Component Variants,” International Journal of Production Economics, Vol. 112, No. 2, pp. 723–741, 2008.Luo, X., Kwong, C., Tang, J., Deng, S., and Gong, J., “Integrating Supplier Selection in Optimal Product Family Design,” International Journal of Production Research, Vol. 49, No. 14, pp. 4195–4222, 2011.Deng, S., Aydin, R., Kwong, C., and Huang, Y., “Integrated Product Line Design and Supplier Selection: A Multi-Objective Optimization Paradigm,” Computers & Industrial Engineering, Vol. 70, pp. 150–158, 2014.Hsu, T.-H., Chu, K.-M., and Chan, H.-C., “The Fuzzy Clustering on Market Segment,” Proc. of Ninth IEEE International Conference on Fuzzy Systems, pp. 621–626, 2000.Jiao, J. and Zhang, Y., “Product Portfolio Planning with Customer-Engineering Interaction,” IIE Transactions, Vol. 37, No. 9, pp. 801–814, 2005.Leach, M. P. and Liu, A. H., “Applying Conjoint Analysis to International Markets: A Cross-Cultural Comparison of Model Fit,” Proc. of the 1997 Academy of Marketing Science (AMS) Annual Conference, pp. 69–73, 2015.Ben-Akiva, M. E., Lerman, S. R., and Lerman, S. R., “Discrete Choice Analysis: Theory and Application to Travel Demand,” MIT Press, 1985.Mukhopadhyay, S. K. and Ma, H., “Joint Procurement and Production Decisions in Remanufacturing under Quality and Demand Uncertainty,” International Journal of Production Economics, Vol. 120, No. 1, pp. 5–17, 2009.Wang, K. and Choi, S., “A Holonic Approach to Flexible Flow Shop Scheduling under Stochastic Processing Times,” Computers & Operations Research, Vol. 43, pp. 157–168, 2014.Zhang, A. and Huang, G. Q., “Impacts of Business Environment Changes on Global Manufacturing Outsourcing in China,” Supply Chain Management: An International Journal, Vol. 17, No. 2, pp. 138–151, 2012.Ting, S.-C. and Cho, D. I., “An Integrated Approach for Supplier Selection and Purchasing Decisions,” Supply Chain Management: An International Journal, Vol. 13, No. 2, pp. 116–127, 2008.Syswerda, G., “Uniform Crossover in Genetic Algorithms,” Proc. of the Third International Conference on Genetic Algorithms, pp. 2–9, 1989