14,982 research outputs found

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

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    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 PayĂĄ, D.; Mula, J.; Alemany DĂ­az, 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., DĂ­az-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-

    Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions

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    "This is an Accepted Manuscript of an article published in International Journal of Production Research on December 2014, available online: http://www.tandfonline.com/10.1080/00207543.2014.920115."In this paper, we formulate the material requirements planning) problem of a first-tier supplier in an automobile supply chain through a fuzzy multi-objective decision model, which considers three conflictive objectives to optimise: minimisation of normal, overtime and subcontracted production costs of finished goods plus the inventory costs of finished goods, raw materials and components; minimisation of idle time; minimisation of backorder quantities. Lack of knowledge or epistemic uncertainty is considered in the demand, available and required capacity data. Integrity conditions for the main decision variables of the problem are also considered. For the solution methodology, we use a fuzzy goal programming approach where the importance of the relations among the goals is considered fuzzy instead of using a crisp definition of goal weights. For illustration purposes, an example based on modifications of real-world industrial problems is used.This work has been funded by the Universitat Politecnica de Valencia Project: 'Material Requirements Planning Fourth Generation (MRPIV)' (Ref. PAID-05-12).DĂ­az-Madroñero Boluda, FM.; Mula, J.; JimĂ©nez, M. (2014). Fuzzy goal programming for material requirements planning under uncertainty and integrity conditions. International Journal of Production Research. 52(23):6971-6988. doi:10.1080/00207543.2014.920115S697169885223Aköz, O., & Petrovic, D. (2007). A fuzzy goal programming method with imprecise goal hierarchy. European Journal of Operational Research, 181(3), 1427-1433. doi:10.1016/j.ejor.2005.11.049Alfieri, A., & Matta, A. (2010). Mathematical programming representation of pull controlled single-product serial manufacturing systems. Journal of Intelligent Manufacturing, 23(1), 23-35. doi:10.1007/s10845-009-0371-xAloulou, M. A., Dolgui, A., & Kovalyov, M. Y. (2013). A bibliography of non-deterministic lot-sizing models. International Journal of Production Research, 52(8), 2293-2310. doi:10.1080/00207543.2013.855336Barba-GutiĂ©rrez, Y., & Adenso-DĂ­az, B. (2009). Reverse MRP under uncertain and imprecise demand. The International Journal of Advanced Manufacturing Technology, 40(3-4), 413-424. doi:10.1007/s00170-007-1351-yBookbinder, J. H., McAuley, P. T., & Schulte, J. (1989). Inventory and Transportation Planning in the Distribution of Fine Papers. Journal of the Operational Research Society, 40(2), 155-166. doi:10.1057/jors.1989.20Chiang, W. K., & Feng, Y. (2007). The value of information sharing in the presence of supply uncertainty and demand volatility. International Journal of Production Research, 45(6), 1429-1447. doi:10.1080/00207540600634949DĂ­az-Madroñero, M., Mula, J., & JimĂ©nez, M. (2013). A Modified Approach Based on Ranking Fuzzy Numbers for Fuzzy Integer Programming with Equality Constraints. Annals of Industrial Engineering 2012, 225-233. doi:10.1007/978-1-4471-5349-8_27DOLGUI, A., BEN AMMAR, O., HNAIEN, F., & LOULY, M. A. O. (2013). A State of the Art on Supply Planning and Inventory Control under Lead Time Uncertainty. Studies in Informatics and Control, 22(3). doi:10.24846/v22i3y201302Dubois, D. (2011). The role of fuzzy sets in decision sciences: Old techniques and new directions. Fuzzy Sets and Systems, 184(1), 3-28. doi:10.1016/j.fss.2011.06.003Grabot, B., Geneste, L., Reynoso-Castillo, G., & Vïżœrot, S. (2005). Integration of uncertain and imprecise orders in the MRP method. Journal of Intelligent Manufacturing, 16(2), 215-234. doi:10.1007/s10845-004-5890-xGuillaume, R., Thierry, C., & Grabot, B. (2010). Modelling of ill-known requirements and integration in production planning. Production Planning & Control, 22(4), 336-352. doi:10.1080/09537281003800900Heilpern, S. (1992). The expected value of a fuzzy number. Fuzzy Sets and Systems, 47(1), 81-86. doi:10.1016/0165-0114(92)90062-9Hnaien, F., Dolgui, A., & Ould Louly, M.-A. (2008). Planned lead time optimization in material requirement planning environment for multilevel production systems. Journal of Systems Science and Systems Engineering, 17(2), 132-155. doi:10.1007/s11518-008-5072-zHung, Y.-F., & Chang, C.-B. (1999). Determining safety stocks for production planning in uncertain manufacturing. International Journal of Production Economics, 58(2), 199-208. doi:10.1016/s0925-5273(98)00124-8Inderfurth, K. (2009). How to protect against demand and yield risks in MRP systems. International Journal of Production Economics, 121(2), 474-481. doi:10.1016/j.ijpe.2007.02.005JIMÉNEZ, M. (1996). RANKING FUZZY NUMBERS THROUGH THE COMPARISON OF ITS EXPECTED INTERVALS. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 04(04), 379-388. doi:10.1142/s0218488596000226JimĂ©nez, M., Arenas, M., Bilbao, A., & RodrıŽguez, M. V. (2007). Linear programming with fuzzy parameters: An interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609. doi:10.1016/j.ejor.2005.10.002Jones, D. (2011). A practical weight sensitivity algorithm for goal and multiple objective programming. European Journal of Operational Research, 213(1), 238-245. doi:10.1016/j.ejor.2011.03.012Lage Junior, M., & Godinho Filho, M. (2010). Variations of the kanban system: Literature review and classification. International Journal of Production Economics, 125(1), 13-21. doi:10.1016/j.ijpe.2010.01.009Jung, J. Y., Blau, G., Pekny, J. F., Reklaitis, G. V., & Eversdyk, D. (2004). A simulation based optimization approach to supply chain management under demand uncertainty. Computers & Chemical Engineering, 28(10), 2087-2106. doi:10.1016/j.compchemeng.2004.06.006Koh, S. C. L. (2004). MRP-controlled batch-manufacturing environment under uncertainty. Journal of the Operational Research Society, 55(3), 219-232. doi:10.1057/palgrave.jors.2601710Lai, 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-iLee, H. L., & Billington, C. (1993). Material Management in Decentralized Supply Chains. Operations Research, 41(5), 835-847. doi:10.1287/opre.41.5.835Lee, Y. H., Kim, S. H., & Moon, C. (2002). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control, 13(1), 35-46. doi:10.1080/09537280110061566Li, 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.2005.12.003Louly, M.-A., & Dolgui, A. (2011). Optimal time phasing and periodicity for MRP with POQ policy. International Journal of Production Economics, 131(1), 76-86. doi:10.1016/j.ijpe.2010.04.042Louly, M. A., Dolgui, A., & Hnaien, F. (2008). Optimal supply planning in MRP environments for assembly systems with random component procurement times. International Journal of Production Research, 46(19), 5441-5467. doi:10.1080/00207540802273827Mohapatra, P., Benyoucef, L., & Tiwari, M. K. (2013). Integration of process planning and scheduling through adaptive setup planning: a multi-objective approach. International Journal of Production Research, 51(23-24), 7190-7208. doi:10.1080/00207543.2013.853890Mula, J., & DĂ­az-Madroñero, M. (2012). Solution Approaches for Material Requirement Planning* with Fuzzy Costs. Industrial Engineering: Innovative Networks, 349-357. doi:10.1007/978-1-4471-2321-7_39Mula, J., Poler, R., & GarcĂ­a, J. P. (2006). EvaluaciĂłn de Sistemas para la PlanificaciĂłn y Control de la ProducciĂłn/[title] [title language=en]Evaluation of Production Planning and Control Systems. InformaciĂłn tecnolĂłgica, 17(1). doi:10.4067/s0718-07642006000100004Mula, J., Poler, R., & Garcia, J. P. (2006). MRP with flexible constraints: A fuzzy mathematical programming approach. Fuzzy Sets and Systems, 157(1), 74-97. doi:10.1016/j.fss.2005.05.045Mula, J., Poler, R., & Garcia-Sabater, J. P. (2008). Capacity and material requirement planning modelling by comparing deterministic and fuzzy models. International Journal of Production Research, 46(20), 5589-5606. doi:10.1080/00207540701413912Mula, J., Poler, R., & Garcia-Sabater, J. P. (2007). Material Requirement Planning with fuzzy constraints and fuzzy coefficients. Fuzzy Sets and Systems, 158(7), 783-793. doi:10.1016/j.fss.2006.11.003Mula, J., Poler, R., GarcĂ­a-Sabater, J. P., & Lario, F. C. (2006). Models for production planning under uncertainty: A review. International Journal of Production Economics, 103(1), 271-285. doi:10.1016/j.ijpe.2005.09.001Noori, S., Feylizadeh, M. R., Bagherpour, M., Zorriassatine, F., & Parkin, R. M. (2008). Optimization of material requirement planning by fuzzy multi-objective linear programming. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 222(7), 887-900. doi:10.1243/09544054jem1014Olhager, J. (2013). Evolution of operations planning and control: from production to supply chains. International Journal of Production Research, 51(23-24), 6836-6843. doi:10.1080/00207543.2012.761363Peidro, D., Mula, J., Alemany, M. M. E., & Lario, F.-C. (2012). Fuzzy multi-objective optimisation for master planning in a ceramic supply chain. 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    Review of mathematical models for production planning under uncertainty due to lack of homogeneity: proposal of a conceptual model

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    [EN] Lack of homogeneity in the product (LHP) appears in some production processes that confer heterogeneity in the characteristics of the products obtained. Supply chains with this issue have to classify the product in different homogeneous subsets, whose quantity is uncertain during the production planning process. This paper proposes a generic framework for reviewing in a unified way the literature about production planning models dealing with LHP uncertainty. This analysis allows the identification of similarities among sectors to transfer solutions between them and gaps existing in the literature for further research. The results of the review show: (1) sectors affected by LHP inherent uncertainty, (2) the inherent LHP uncertainty types modelled, and (3) the approaches for modelling LHP uncertainty most widely employed. Finally, we suggest a conceptual model reflecting the aspects to be considered when modelling the production planning in sectors with LHP in an uncertain environment.This research was initiated within the framework of the project funded by the Ministerio de EconomĂ­a y Competitividad [Ref. DPI2011-23597] entitled ‘Methods and models for operations planning and order management in supply chains characterised by uncertainty in production due to the lack of product uniformity’ (PLANGES-FHP) already finished. After, the project leading to this application has received funding from the European Union’s research and innovation programme under the H2020 Marie SkƂodowska-Curie Actions with the grant agreement No 691249, Project entitled ’Enhancing and implementing Knowledge based ICT solutions within high Riskand Uncertain Conditions for Agriculture Production Systems’ (RUC-APS).Mundi, I.; Alemany DĂ­az, MDM.; Poler, R.; Fuertes-Miquel, VS. (2019). 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    Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

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    In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified Δ-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method

    Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

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    In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

    Master production schedule using robust optimization approaches in an automobile second-tier supplier

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    [EN] This paper considers a real-world automobile second-tier supplier that manufactures decorative surface finishings of injected parts provided by several suppliers, and which devises its master production schedule by a manual spreadsheet-based procedure. The imprecise production time in this manufacturer's production process is incorporated into a deterministic mathematical programming model to address this problem by two robust optimization approaches. The proposed model and the corresponding robust solution methodology improve production plans by optimizing the production, inventory and backlogging costs, and demonstrate the their feasibility for a realistic master production schedule problem that outperforms the heuristic decision-making procedure currently being applied in the firm under study.Funding was provided by Horizon 2020 Framework Programme (Grant Agreement No. 636909) in the frame of the "Cloud Collaborative Manufacturing Networks" (C2NET) project.MartĂ­n, AG.; DĂ­az-Madroñero Boluda, FM.; Mula, J. (2020). Master production schedule using robust optimization approaches in an automobile second-tier supplier. Central European Journal of Operations Research. 28(1):143-166. https://doi.org/10.1007/s10100-019-00607-2S143166281Alem DJ, Morabito R (2012) Production planning in furniture settings via robust optimization. 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    The relevance of outsourcing and leagile strategies in performance optimization of an integrated process planning and scheduling

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    Over the past few years growing global competition has forced the manufacturing industries to upgrade their old production strategies with the modern day approaches. As a result, recent interest has been developed towards finding an appropriate policy that could enable them to compete with others, and facilitate them to emerge as a market winner. Keeping in mind the abovementioned facts, in this paper the authors have proposed an integrated process planning and scheduling model inheriting the salient features of outsourcing, and leagile principles to compete in the existing market scenario. The paper also proposes a model based on leagile principles, where the integrated planning management has been practiced. In the present work a scheduling problem has been considered and overall minimization of makespan has been aimed. The paper shows the relevance of both the strategies in performance enhancement of the industries, in terms of their reduced makespan. The authors have also proposed a new hybrid Enhanced Swift Converging Simulated Annealing (ESCSA) algorithm, to solve the complex real-time scheduling problems. The proposed algorithm inherits the prominent features of the Genetic Algorithm (GA), Simulated Annealing (SA), and the Fuzzy Logic Controller (FLC). The ESCSA algorithm reduces the makespan significantly in less computational time and number of iterations. The efficacy of the proposed algorithm has been shown by comparing the results with GA, SA, Tabu, and hybrid Tabu-SA optimization methods

    Optimal management of bio-based energy supply chains under parametric uncertainty through a data-driven decision-support framework

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    This paper addresses the optimal management of a multi-objective bio-based energy supply chain network subjected to multiple sources of uncertainty. The complexity to obtain an optimal solution using traditional uncertainty management methods dramatically increases with the number of uncertain factors considered. Such a complexity produces that, if tractable, the problem is solved after a large computational effort. Therefore, in this work a data-driven decision-making framework is proposed to address this issue. Such a framework exploits machine learning techniques to efficiently approximate the optimal management decisions considering a set of uncertain parameters that continuously influence the process behavior as an input. A design of computer experiments technique is used in order to combine these parameters and produce a matrix of representative information. These data are used to optimize the deterministic multi-objective bio-based energy network problem through conventional optimization methods, leading to a detailed (but elementary) map of the optimal management decisions based on the uncertain parameters. Afterwards, the detailed data-driven relations are described/identified using an Ordinary Kriging meta-model. The result exhibits a very high accuracy of the parametric meta-models for predicting the optimal decision variables in comparison with the traditional stochastic approach. Besides, and more importantly, a dramatic reduction of the computational effort required to obtain these optimal values in response to the change of the uncertain parameters is achieved. Thus the use of the proposed data-driven decision tool promotes a time-effective optimal decision making, which represents a step forward to use data-driven strategy in large-scale/complex industrial problems.Peer ReviewedPostprint (published version

    Multi crteria decision making and its applications : a literature review

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    This paper presents current techniques used in Multi Criteria Decision Making (MCDM) and their applications. Two basic approaches for MCDM, namely Artificial Intelligence MCDM (AIMCDM) and Classical MCDM (CMCDM) are discussed and investigated. Recent articles from international journals related to MCDM are collected and analyzed to find which approach is more common than the other in MCDM. Also, which area these techniques are applied to. Those articles are appearing in journals for the year 2008 only. This paper provides evidence that currently, both AIMCDM and CMCDM are equally common in MCDM
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