Fuzzy multi-objective optimisation for master planning in a ceramic supply chain

This is an Accepted Manuscript of an article published in International Journal of Production Research on 2012, available online: http://www.tandfonline.com/10.1080/00207543.2011.588267.In this paper, we consider the master planning problem for a centralised replenishment, production and distribution ceramic tile supply chain. A fuzzy multi-objective linear programming (FMOLP) approach is presented which considers the maximisation of the fuzzy gross margin, the minimisation of the fuzzy idle time and the minimisation of the fuzzy backorder quantities. By using an interactive solution methodology to convert this FMOLP model into an auxiliary crisp single-objective linear model, a preferred compromise solution is obtained. For illustration purposes, an example based on modifications of real-world industrial problems is used.This research has been carried out in the framework of a project funded by the Science and Technology Ministry of the Spanish Government, entitled 'Project of reinforcement of the competitiveness of the Spanish managerial fabric through the logistics as a strategic factor in a global environment' (Ref. PSE-370000-2008-8).Peidro Payá, D.; Mula, J.; Alemany Díaz, MDM.; Lario Esteban, FC. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. International Journal of Production Research. 50(11):3011-3020. https://doi.org/10.1080/00207543.2011.588267S301130205011Alemany, M.M.E.et al., 2010. Mathematical programming model for centralized master planning in ceramic tile supply chains.International Journal of Production Research, 48 (17), 5053–5074Beamon, B. M. (1998). Supply chain design and analysis: International Journal of Production Economics, 55(3), 281-294. doi:10.1016/s0925-5273(98)00079-6Chen, C.-L., & Lee, W.-C. (2004). Multi-objective optimization of multi-echelon supply chain networks with uncertain product demands and prices. Computers & Chemical Engineering, 28(6-7), 1131-1144. doi:10.1016/j.compchemeng.2003.09.014Chern, C.-C., & Hsieh, J.-S. (2007). A heuristic algorithm for master planning that satisfies multiple objectives. Computers & Operations Research, 34(11), 3491-3513. doi:10.1016/j.cor.2006.02.022Kreipl, S., & Pinedo, M. (2009). Planning and Scheduling in Supply Chains: An Overview of Issues in Practice. Production and Operations Management, 13(1), 77-92. doi:10.1111/j.1937-5956.2004.tb00146.xLai, Y.-J., & Hwang, C.-L. (1993). Possibilistic linear programming for managing interest rate risk. Fuzzy Sets and Systems, 54(2), 135-146. doi:10.1016/0165-0114(93)90271-iLi, X., Zhang, B., & Li, H. (2006). Computing efficient solutions to fuzzy multiple objective linear programming problems. Fuzzy Sets and Systems, 157(10), 1328-1332. doi:10.1016/j.fss.2005.12.003Mula, J., Peidro, D., Díaz-Madroñero, M., & Vicens, E. (2010). Mathematical programming models for supply chain production and transport planning. European Journal of Operational Research, 204(3), 377-390. doi:10.1016/j.ejor.2009.09.008Mula, J., Peidro, D., and Poler, R., 2010b. The effectiveness of a fuzzy mathematical programming approach for supply chain production planning with fuzzy demand.International Journal of Production Economics, In pressPark *, Y. B. (2005). An integrated approach for production and distribution planning in supply chain management. International Journal of Production Research, 43(6), 1205-1224. doi:10.1080/00207540412331327718Peidro, D., Mula, J., Poler, R., & Lario, F.-C. (2008). Quantitative models for supply chain planning under uncertainty: a review. The International Journal of Advanced Manufacturing Technology, 43(3-4), 400-420. doi:10.1007/s00170-008-1715-yPeidro, D., Mula, J., Poler, R., & Verdegay, J.-L. (2009). Fuzzy optimization for supply chain planning under supply, demand and process uncertainties. Fuzzy Sets and Systems, 160(18), 2640-2657. doi:10.1016/j.fss.2009.02.021Selim, H., Araz, C., & Ozkarahan, I. (2008). Collaborative production–distribution planning in supply chain: A fuzzy goal programming approach. Transportation Research Part E: Logistics and Transportation Review, 44(3), 396-419. doi:10.1016/j.tre.2006.11.001Selim, H., & Ozkarahan, I. (2006). A supply chain distribution network design model: An interactive fuzzy goal programming-based solution approach. The International Journal of Advanced Manufacturing Technology, 36(3-4), 401-418. doi:10.1007/s00170-006-0842-6Torabi, S. A., & Hassini, E. (2008). An interactive possibilistic programming approach for multiple objective supply chain master planning. Fuzzy Sets and Systems, 159(2), 193-214. doi:10.1016/j.fss.2007.08.010Haehling von Lanzenauer, C., & Pilz-Glombik, K. (2002). Coordinating supply chain decisions: an optimization model. OR Spectrum, 24(1), 59-78. doi:10.1007/s291-002-8200-3Zimmermann, H.-J. (1978). Fuzzy programming and linear programming with several objective functions. Fuzzy Sets and Systems, 1(1), 45-55. doi:10.1016/0165-0114(78)90031-

A Collaborative Model to Improve Farmers' Skill Level by Investments in an Uncertain Context

[EN] Some small farms are forced to waste a part of their harvests for not reaching the quality standards fixed by consumers. Meanwhile, modern retailers (MR) are interested in selling more quality products to increase their profits. MR could invest in a collaboration program so the small farmers could have access to better technologies and formation to increase the proportion of quality products. Unfortunately, the demand, the quantity of harvest, the proportion of harvest being of quality, and its increase with each investment are uncertain parameters. A fuzzy model considering these uncertainties is proposed to determine the investments that MR should made to maximize the profits of the supply chain in a collaboration context. A method to transform the fuzzy model into an equivalent crisp model and an interactive resolution method are applied.The first author acknowledges the partial support of the Programme of Formation of University Professors of the Spanish Ministry of Education, Culture, and Sport (FPU15/03595). The other authors acknowledge the partial support of Project 691249, "RUCAPS: Enhancing and implementing Knowledge based ICT solutions within high Risk and Uncertain Conditions for Agriculture Production Systems", funded by the EU under its funding scheme H2020-MCSA-RISE-2015.Esteso, A.; Alemany Díaz, MDM.; Ortiz Bas, Á.; Guyon, C. (2018). A Collaborative Model to Improve Farmers' Skill Level by Investments in an Uncertain Context. IFIP Advances in Information and Communication Technology. 534:590-598. https://doi.org/10.1007/978-3-319-99127-6_51S590598534Zhao, G., Liu, S., Lopez, C.: A literature review on risk sources and resilience factors in agri-food supply chains. In: Camarinha-Matos, Luis M., Afsarmanesh, H., Fornasiero, R. (eds.) PRO-VE 2017. IFIP AICT, vol. 506, pp. 739–752. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65151-4_66Esteso, A., Alemany, M.M.E., Ortiz, A.: Improving vegetables quality in small-scale farms through stakeholders collaboration. In: 12th International Conference on Industrial Engineering and Industrial Management (in Press)Sutopo, W., Hisjam, M., Yuniaristanto: An agri-food supply chain model to empower farmers for supplying deteriorated product to modern retailer. In: Yang, G.C., Ao, S.I., Huang, X., Castillo, O. (eds.) IAENG Transactions on Engineering Technologies. LNEE, vol 186, pp. 189–202. Springer, Dordrecht (2013). https://doi.org/10.1007/978-94-007-5651-9_14Sutopo, W., Hisjam, M., Yuniaristanto, Kurniawan, B.: A goal programming approach for assessing the financial risk of corporate social responsibility programs in agri-food supply chain network. In: Proceedings of the World Congress on Engineering 2013, pp. 732–736 (2013)Sutopo, W., Hisjam, M., Yuniaristanto: An agri-food supply chain model for cultivating the capabilities of farmers accessing market using social responsibility program. Int. Sch. Sci. Res. Innov. 5(11), 1588–1592 (2011)Sutopo, W., Hisjam, M., Yuniaristanto: An agri-food supply chain model to enhance the business skills of small-scale farmers using corporate social responsibility. Makara J. Technol. 16(1), 43–50 (2012)Sutopo, W., Hisjam, M., Yuniaristanto: Developing an agri-food supply chain application for determining the priority of CSR program to empower farmers as a qualified supplier of modern retailer. In: 2013 World Congress on Engineering and Computer Science, pp. 1180–1184 (2013)Wahyudin, R.S., Hisjam, M., Yuniaristanto, Kurniawan, B.: An agri-food supply chain model for cultivating the capabilities of farmers in accessing capital using corporate social responsibility program. In: Proceedings of the International MultiConference of Engineers and Computer Scientists, pp. 877–882 (2015)Jiménez, M., Arenas, M., Bilbao, A., Rodríguez, M.V.: Linear programming with fuzzy parameters: an interactive method resolution. Eur. J. Oper. Res. 177, 1599–1609 (2007)Peidro, D., Mula, J., Jiménez, M., Botella, M.M.: A fuzzy linear programming based approach for tactical supply chain planning in an uncertainty environment. Eur. J. Oper. Res. 205, 65–80 (2010)Esteso, A., Alemany, M.M.E., Ortiz, A.: Conceptual framework for managing uncertainty in a collaborative agri-food supply chain context. In: Camarinha-Matos, Luis M., Afsarmanesh, H., Fornasiero, R. (eds.) PRO-VE 2017. IFIP AICT, vol. 506, pp. 715–724. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65151-4_6

Application of stochastic programming to reduce uncertainties in quality-based supply planning of slaughterhouses

To match products of different quality with end market preferences under supply uncertainty, it is crucial to integrate product quality information in logistics decision making. We present a case of this integration in a meat processing company that faces uncertainty in delivered livestock quality. We develop a stochastic programming model that exploits historical product quality delivery data to produce slaughterhouse allocation plans with reduced levels of uncertainty in received livestock quality. The allocation plans generated by this model fulfil demand for multiple quality features at separate slaughterhouses under prescribed service levels while minimizing transportation costs. We test the model on real world problem instances generated from a data set provided by an industrial partner. Results show that historical farmer delivery data can be used to reduce uncertainty in quality of animals to be delivered to slaughterhouses

Supply Chain Intelligence

This chapter provides on overall picture of business intelligence (BI) and supply chain analytics (SCA) as a means to support supply chain management (SCM) and decision-making. Based on the literature review, we clarify the needs of BI and performance measurement in the SCM sphere, and discuss its potential to enhance decision-making in strategic, tactical and operational levels. We also make a closer look in to SCA in different areas and functions of SCM. Our findings indicate that the main challenge for harnessing the full potential of SCA is the lack of holistic and integrated BI approaches that originates from the fact that each functional area is using its own IT applications without necessary integration in to the company’s overall BI system. Following this examination, we construct a holistic framework that illustrates how an integrated, managerially planned BI system can be developed. Finally, we discuss the main competency requirements, as well as the challenges still prohibiting the great majority of firms from building smart and comprehensive BI systems for SCM.fi=vertaisarvioitu|en=peerReviewed

Bladder cancer index: cross-cultural adaptation into Spanish and psychometric evaluation

BACKGROUND: The Bladder Cancer Index (BCI) is so far the only instrument applicable across all bladder cancer patients, independent of tumor infiltration or treatment applied. We developed a Spanish version of the BCI, and assessed its acceptability and metric properties. METHODS: For the adaptation into Spanish we used the forward and back-translation method, expert panels, and cognitive debriefing patient interviews. For the assessment of metric properties we used data from 197 bladder cancer patients from a multi-center prospective study. The Spanish BCI and the SF-36 Health Survey were self-administered before and 12 months after treatment. Reliability was estimated by Cronbach's alpha. Construct validity was assessed through the multi-trait multi-method matrix. The magnitude of change was quantified by effect sizes to assess responsiveness. RESULTS: Reliability coefficients ranged 0.75-0.97. The validity analysis confirmed moderate associations between the BCI function and bother subscales for urinary (r = 0.61) and bowel (r = 0.53) domains; conceptual independence among all BCI domains (r ≤ 0.3); and low correlation coefficients with the SF-36 scores, ranging 0.14-0.48. Among patients reporting global improvement at follow-up, pre-post treatment changes were statistically significant for the urinary domain and urinary bother subscale, with effect sizes of 0.38 and 0.53. CONCLUSIONS: The Spanish BCI is well accepted, reliable, valid, responsive, and similar in performance compared to the original instrument. These findings support its use, both in Spanish and international studies, as a valuable and comprehensive tool for assessing quality of life across a wide range of bladder cancer patients

Rjdbc: A Simple Database Replication Engine

Providing fault tolerant services is a key question among many services manufacturers. Thus, enterprises usually acquire complex and expensive replication engines. This paper offers an interesting choice to organizations which can not afford such costs. RJDBC stands for a simple, easy to install middleware, placed between the application and the database management system, intercepting all database operations and forwarding them among all the replicas of the system. However, from the point of view of the application, the database management system is accessed directly, so that RJDBC is able to supply replication capabilities in a transparent way. Such solution provides acceptable results in clustered configurations. This paper describes the architecture of the solution and some significant results